From 5bfcef8f6afde079de835c15f32319c238eca853 Mon Sep 17 00:00:00 2001
From: sankalpsaoji123 <53940684+sankalpsaoji123@users.noreply.github.com>
Date: Fri, 9 Aug 2019 14:52:08 +0100
Subject: [PATCH 1/4] Add files via upload

---
 Titanic_report.pdf  |   Bin 0 -> 616492 bytes
 titanic - SVM.ipynb |  7964 +++++++++++++++++++++++++++++++++
 titanic-NN.ipynb    | 10169 ++++++++++++++++++++++++++++++++++++++++++
 3 files changed, 18133 insertions(+)
 create mode 100644 Titanic_report.pdf
 create mode 100644 titanic - SVM.ipynb
 create mode 100644 titanic-NN.ipynb

diff --git a/Titanic_report.pdf b/Titanic_report.pdf
new file mode 100644
index 0000000000000000000000000000000000000000..671a2089408e0aba173ad67202a87d468594cb5c
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diff --git a/titanic - SVM.ipynb b/titanic - SVM.ipynb
new file mode 100644
index 000000000..efc68101b
--- /dev/null
+++ b/titanic - SVM.ipynb	
@@ -0,0 +1,7964 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_cell_guid": "9e319230-1cc2-4b19-ad66-06a07e07fc60",
+    "_execution_state": "idle",
+    "_uuid": "03de849fb872e11b3db2a2c99563783ce6ba784f"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(891, 12) (418, 11) ['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
+      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP-PC\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  sort=sort)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# ignore Deprecation Warning\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
+    "\n",
+    "# Random Forest Regressor\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# Neural Network\n",
+    "import keras \n",
+    "from keras.models import Sequential \n",
+    "from keras.layers import Dense\n",
+    "\n",
+    "# load the data\n",
+    "df_train = pd.read_csv('data/train.csv')\n",
+    "df_test = pd.read_csv('data/test.csv')\n",
+    "df = df_train.append(df_test , ignore_index = True)\n",
+    "\n",
+    "# some quick inspections\n",
+    "print(df_train.shape, df_test.shape, df_train.columns.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>442</th>\n",
+       "      <td>443</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Petterson, Mr. Johan Emil</td>\n",
+       "      <td>male</td>\n",
+       "      <td>25.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>347076</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>543</th>\n",
+       "      <td>544</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Beane, Mr. Edward</td>\n",
+       "      <td>male</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2908</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>547</th>\n",
+       "      <td>548</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Padro y Manent, Mr. Julian</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>SC/PARIS 2146</td>\n",
+       "      <td>13.8625</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>792</th>\n",
+       "      <td>793</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Sage, Miss. Stella Anna</td>\n",
+       "      <td>female</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2</td>\n",
+       "      <td>CA. 2343</td>\n",
+       "      <td>69.5500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>128</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Madsen, Mr. Fridtjof Arne</td>\n",
+       "      <td>male</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>C 17369</td>\n",
+       "      <td>7.1417</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived  Pclass                        Name     Sex   Age  \\\n",
+       "442          443         0       3   Petterson, Mr. Johan Emil    male  25.0   \n",
+       "543          544         1       2           Beane, Mr. Edward    male  32.0   \n",
+       "547          548         1       2  Padro y Manent, Mr. Julian    male   NaN   \n",
+       "792          793         0       3     Sage, Miss. Stella Anna  female   NaN   \n",
+       "127          128         1       3   Madsen, Mr. Fridtjof Arne    male  24.0   \n",
+       "\n",
+       "     SibSp  Parch         Ticket     Fare Cabin Embarked  \n",
+       "442      1      0         347076   7.7750   NaN        S  \n",
+       "543      1      0           2908  26.0000   NaN        S  \n",
+       "547      0      0  SC/PARIS 2146  13.8625   NaN        C  \n",
+       "792      8      2       CA. 2343  69.5500   NaN        S  \n",
+       "127      0      0        C 17369   7.1417   NaN        S  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.sample(5)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pclass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "_cell_guid": "8ac53d3d-bf63-4b0a-b4dd-cf69ebb1b189",
+    "_execution_state": "idle",
+    "_uuid": "7a92a95fd0642b65500aa0fe2d5453f8f9cff24c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df['Pclass'].isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "_cell_guid": "366c466d-d5f1-41f2-9568-3abc426bfc75",
+    "_execution_state": "idle",
+    "_uuid": "d380019bd6179b6ddcde472ce10771788ae204f4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.629630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.472826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.242363</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pclass  Survived\n",
+       "0       1  0.629630\n",
+       "1       2  0.472826\n",
+       "2       3  0.242363"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Pclass and Survived\n",
+    "df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "cf76f8ac-cc51-4ffb-8066-b605c5aa971c",
+    "_execution_state": "idle",
+    "_uuid": "617016b19a60d3ae9b2b1dbfefb73f4fb56e13a9"
+   },
+   "source": [
+    "### Name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "_cell_guid": "fa11d6a2-ad3d-443b-8579-de525fdd20c1",
+    "_execution_state": "idle",
+    "_uuid": "a40e18c8e9fe2692dec94722cc6ab316d71b5003"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                              Braund, Mr. Owen Harris\n",
+       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
+       "2                               Heikkinen, Miss. Laina\n",
+       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
+       "4                             Allen, Mr. William Henry\n",
+       "5                                     Moran, Mr. James\n",
+       "6                              McCarthy, Mr. Timothy J\n",
+       "7                       Palsson, Master. Gosta Leonard\n",
+       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n",
+       "9                  Nasser, Mrs. Nicholas (Adele Achem)\n",
+       "Name: Name, dtype: object"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Name.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "_cell_guid": "bb1fc2fe-1947-4f89-99fa-d621711615fc",
+    "_execution_state": "idle",
+    "_uuid": "93e6890cecf2d3ae919ec30b24588d780092720d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Mr              757\n",
+       "Miss            260\n",
+       "Mrs             197\n",
+       "Master           61\n",
+       "Rev               8\n",
+       "Dr                8\n",
+       "Col               4\n",
+       "Mlle              2\n",
+       "Ms                2\n",
+       "Major             2\n",
+       "Jonkheer          1\n",
+       "Lady              1\n",
+       "Capt              1\n",
+       "Dona              1\n",
+       "Sir               1\n",
+       "Don               1\n",
+       "the Countess      1\n",
+       "Mme               1\n",
+       "Name: Title, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Title'] = df.Name.map( lambda x: x.split(',')[1].split( '.' )[0].strip())\n",
+    "\n",
+    "# inspect the amount of people for each title\n",
+    "df['Title'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "_cell_guid": "67b67929-d7c3-4d52-9cde-eb37b4ef256d",
+    "_execution_state": "idle",
+    "_uuid": "a7840964bab69268bcc0e2516770f7faf00aad1d"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP-PC\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  self._setitem_with_indexer(indexer, value)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>0.575000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0.701087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0.156673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0.796875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Others</td>\n",
+       "      <td>0.318182</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Title  Survived\n",
+       "0  Master  0.575000\n",
+       "1    Miss  0.701087\n",
+       "2      Mr  0.156673\n",
+       "3     Mrs  0.796875\n",
+       "4  Others  0.318182"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Title'] = df['Title'].replace('Mlle', 'Miss')\n",
+    "df['Title'] = df['Title'].replace(['Mme','Lady','Ms'], 'Mrs')\n",
+    "df.Title.loc[ (df.Title !=  'Master') & (df.Title !=  'Mr') & (df.Title !=  'Miss') \n",
+    "             & (df.Title !=  'Mrs')] = 'Others'\n",
+    "\n",
+    "# inspect the correlation between Title and Survived\n",
+    "df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "_cell_guid": "18a55b3d-677f-4611-a41e-be3f9ff49c67",
+    "_execution_state": "idle",
+    "_uuid": "77a517250b3a9b32da2436b8522e864e4e3e7016"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Mr        757\n",
+       "Miss      262\n",
+       "Mrs       201\n",
+       "Master     61\n",
+       "Others     28\n",
+       "Name: Title, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the amount of people for each title\n",
+    "df['Title'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "_cell_guid": "516ceeba-2dab-4fff-8f5d-4fe94f4eddc8",
+    "_execution_state": "idle",
+    "_uuid": "3f031015c4e518d5b0b1874bac37a0d899eb2a60"
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.concat([df, pd.get_dummies(df['Title'])], axis=1).drop(labels=['Name'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Master</th>\n",
+       "      <th>Miss</th>\n",
+       "      <th>Mr</th>\n",
+       "      <th>Mrs</th>\n",
+       "      <th>Others</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>330877</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>54.0</td>\n",
+       "      <td>E46</td>\n",
+       "      <td>S</td>\n",
+       "      <td>51.8625</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17463</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11.1333</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>347742</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>30.0708</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>237736</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>G6</td>\n",
+       "      <td>S</td>\n",
+       "      <td>16.7000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>PP 9549</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>58.0</td>\n",
+       "      <td>C103</td>\n",
+       "      <td>S</td>\n",
+       "      <td>26.5500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>113783</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>A/5. 2151</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>31.2750</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>347082</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.8542</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>350406</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>55.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>16.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>248706</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>29.1250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>382652</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>244373</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>18.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>345763</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2649</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>239865</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>D56</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>248698</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>8.0292</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>330923</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>A6</td>\n",
+       "      <td>S</td>\n",
+       "      <td>35.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>113788</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>31.3875</td>\n",
+       "      <td>5</td>\n",
+       "      <td>26</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>347077</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2631</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>C23 C25 C27</td>\n",
+       "      <td>S</td>\n",
+       "      <td>263.0000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19950</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.8792</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>330959</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.8958</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349216</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1280</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>364858</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1280</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1281</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1281</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>B24</td>\n",
+       "      <td>S</td>\n",
+       "      <td>93.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1282</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12749</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1282</th>\n",
+       "      <td>51.0</td>\n",
+       "      <td>D28</td>\n",
+       "      <td>S</td>\n",
+       "      <td>39.4000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1283</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PC 17592</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>20.2500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1284</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C.A. 2673</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1284</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1285</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C.A. 30769</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1285</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>22.0250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1286</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>315153</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1286</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>C31</td>\n",
+       "      <td>S</td>\n",
+       "      <td>60.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13695</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1287</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1288</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>371109</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1288</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>B41</td>\n",
+       "      <td>C</td>\n",
+       "      <td>79.2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1289</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13567</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1289</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1290</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>347065</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1290</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.7333</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1291</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21332</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1291</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>C7</td>\n",
+       "      <td>S</td>\n",
+       "      <td>164.8667</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1292</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>36928</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1292</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>21.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1293</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28664</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1293</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>59.4000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1294</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112378</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1294</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>47.1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1295</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>113059</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1295</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>D40</td>\n",
+       "      <td>C</td>\n",
+       "      <td>27.7208</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1296</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17765</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1296</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>D38</td>\n",
+       "      <td>C</td>\n",
+       "      <td>13.8625</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1297</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SC/PARIS 2166</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1298</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28666</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1298</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>C80</td>\n",
+       "      <td>C</td>\n",
+       "      <td>211.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1299</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>113503</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.7208</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>334915</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1300</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13.7750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1301</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SOTON/O.Q. 3101315</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1302</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>365237</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>C78</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>90.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1303</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19928</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1303</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1304</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>347086</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1305</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A.5. 3236</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>C105</td>\n",
+       "      <td>C</td>\n",
+       "      <td>108.9000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1306</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PC 17758</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>38.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1307</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SOTON/O.Q. 3101262</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1308</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>359309</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>22.3583</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1309</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2668</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Age        Cabin Embarked      Fare  Parch  PassengerId  Pclass  \\\n",
+       "0     22.0          NaN        S    7.2500      0            1       3   \n",
+       "1     38.0          C85        C   71.2833      0            2       1   \n",
+       "2     26.0          NaN        S    7.9250      0            3       3   \n",
+       "3     35.0         C123        S   53.1000      0            4       1   \n",
+       "4     35.0          NaN        S    8.0500      0            5       3   \n",
+       "5      NaN          NaN        Q    8.4583      0            6       3   \n",
+       "6     54.0          E46        S   51.8625      0            7       1   \n",
+       "7      2.0          NaN        S   21.0750      1            8       3   \n",
+       "8     27.0          NaN        S   11.1333      2            9       3   \n",
+       "9     14.0          NaN        C   30.0708      0           10       2   \n",
+       "10     4.0           G6        S   16.7000      1           11       3   \n",
+       "11    58.0         C103        S   26.5500      0           12       1   \n",
+       "12    20.0          NaN        S    8.0500      0           13       3   \n",
+       "13    39.0          NaN        S   31.2750      5           14       3   \n",
+       "14    14.0          NaN        S    7.8542      0           15       3   \n",
+       "15    55.0          NaN        S   16.0000      0           16       2   \n",
+       "16     2.0          NaN        Q   29.1250      1           17       3   \n",
+       "17     NaN          NaN        S   13.0000      0           18       2   \n",
+       "18    31.0          NaN        S   18.0000      0           19       3   \n",
+       "19     NaN          NaN        C    7.2250      0           20       3   \n",
+       "20    35.0          NaN        S   26.0000      0           21       2   \n",
+       "21    34.0          D56        S   13.0000      0           22       2   \n",
+       "22    15.0          NaN        Q    8.0292      0           23       3   \n",
+       "23    28.0           A6        S   35.5000      0           24       1   \n",
+       "24     8.0          NaN        S   21.0750      1           25       3   \n",
+       "25    38.0          NaN        S   31.3875      5           26       3   \n",
+       "26     NaN          NaN        C    7.2250      0           27       3   \n",
+       "27    19.0  C23 C25 C27        S  263.0000      2           28       1   \n",
+       "28     NaN          NaN        Q    7.8792      0           29       3   \n",
+       "29     NaN          NaN        S    7.8958      0           30       3   \n",
+       "...    ...          ...      ...       ...    ...          ...     ...   \n",
+       "1279  21.0          NaN        Q    7.7500      0         1280       3   \n",
+       "1280   6.0          NaN        S   21.0750      1         1281       3   \n",
+       "1281  23.0          B24        S   93.5000      0         1282       1   \n",
+       "1282  51.0          D28        S   39.4000      1         1283       1   \n",
+       "1283  13.0          NaN        S   20.2500      2         1284       3   \n",
+       "1284  47.0          NaN        S   10.5000      0         1285       2   \n",
+       "1285  29.0          NaN        S   22.0250      1         1286       3   \n",
+       "1286  18.0          C31        S   60.0000      0         1287       1   \n",
+       "1287  24.0          NaN        Q    7.2500      0         1288       3   \n",
+       "1288  48.0          B41        C   79.2000      1         1289       1   \n",
+       "1289  22.0          NaN        S    7.7750      0         1290       3   \n",
+       "1290  31.0          NaN        Q    7.7333      0         1291       3   \n",
+       "1291  30.0           C7        S  164.8667      0         1292       1   \n",
+       "1292  38.0          NaN        S   21.0000      0         1293       2   \n",
+       "1293  22.0          NaN        C   59.4000      1         1294       1   \n",
+       "1294  17.0          NaN        S   47.1000      0         1295       1   \n",
+       "1295  43.0          D40        C   27.7208      0         1296       1   \n",
+       "1296  20.0          D38        C   13.8625      0         1297       2   \n",
+       "1297  23.0          NaN        S   10.5000      0         1298       2   \n",
+       "1298  50.0          C80        C  211.5000      1         1299       1   \n",
+       "1299   NaN          NaN        Q    7.7208      0         1300       3   \n",
+       "1300   3.0          NaN        S   13.7750      1         1301       3   \n",
+       "1301   NaN          NaN        Q    7.7500      0         1302       3   \n",
+       "1302  37.0          C78        Q   90.0000      0         1303       1   \n",
+       "1303  28.0          NaN        S    7.7750      0         1304       3   \n",
+       "1304   NaN          NaN        S    8.0500      0         1305       3   \n",
+       "1305  39.0         C105        C  108.9000      0         1306       1   \n",
+       "1306  38.5          NaN        S    7.2500      0         1307       3   \n",
+       "1307   NaN          NaN        S    8.0500      0         1308       3   \n",
+       "1308   NaN          NaN        C   22.3583      1         1309       3   \n",
+       "\n",
+       "         Sex  SibSp  Survived              Ticket   Title  Master  Miss  Mr  \\\n",
+       "0       male      1       0.0           A/5 21171      Mr       0     0   1   \n",
+       "1     female      1       1.0            PC 17599     Mrs       0     0   0   \n",
+       "2     female      0       1.0    STON/O2. 3101282    Miss       0     1   0   \n",
+       "3     female      1       1.0              113803     Mrs       0     0   0   \n",
+       "4       male      0       0.0              373450      Mr       0     0   1   \n",
+       "5       male      0       0.0              330877      Mr       0     0   1   \n",
+       "6       male      0       0.0               17463      Mr       0     0   1   \n",
+       "7       male      3       0.0              349909  Master       1     0   0   \n",
+       "8     female      0       1.0              347742     Mrs       0     0   0   \n",
+       "9     female      1       1.0              237736     Mrs       0     0   0   \n",
+       "10    female      1       1.0             PP 9549    Miss       0     1   0   \n",
+       "11    female      0       1.0              113783    Miss       0     1   0   \n",
+       "12      male      0       0.0           A/5. 2151      Mr       0     0   1   \n",
+       "13      male      1       0.0              347082      Mr       0     0   1   \n",
+       "14    female      0       0.0              350406    Miss       0     1   0   \n",
+       "15    female      0       1.0              248706     Mrs       0     0   0   \n",
+       "16      male      4       0.0              382652  Master       1     0   0   \n",
+       "17      male      0       1.0              244373      Mr       0     0   1   \n",
+       "18    female      1       0.0              345763     Mrs       0     0   0   \n",
+       "19    female      0       1.0                2649     Mrs       0     0   0   \n",
+       "20      male      0       0.0              239865      Mr       0     0   1   \n",
+       "21      male      0       1.0              248698      Mr       0     0   1   \n",
+       "22    female      0       1.0              330923    Miss       0     1   0   \n",
+       "23      male      0       1.0              113788      Mr       0     0   1   \n",
+       "24    female      3       0.0              349909    Miss       0     1   0   \n",
+       "25    female      1       1.0              347077     Mrs       0     0   0   \n",
+       "26      male      0       0.0                2631      Mr       0     0   1   \n",
+       "27      male      3       0.0               19950      Mr       0     0   1   \n",
+       "28    female      0       1.0              330959    Miss       0     1   0   \n",
+       "29      male      0       0.0              349216      Mr       0     0   1   \n",
+       "...      ...    ...       ...                 ...     ...     ...   ...  ..   \n",
+       "1279    male      0       NaN              364858      Mr       0     0   1   \n",
+       "1280    male      3       NaN              349909  Master       1     0   0   \n",
+       "1281    male      0       NaN               12749      Mr       0     0   1   \n",
+       "1282  female      0       NaN            PC 17592     Mrs       0     0   0   \n",
+       "1283    male      0       NaN           C.A. 2673  Master       1     0   0   \n",
+       "1284    male      0       NaN          C.A. 30769      Mr       0     0   1   \n",
+       "1285    male      3       NaN              315153      Mr       0     0   1   \n",
+       "1286  female      1       NaN               13695     Mrs       0     0   0   \n",
+       "1287    male      0       NaN              371109      Mr       0     0   1   \n",
+       "1288  female      1       NaN               13567     Mrs       0     0   0   \n",
+       "1289    male      0       NaN              347065      Mr       0     0   1   \n",
+       "1290    male      0       NaN               21332      Mr       0     0   1   \n",
+       "1291  female      0       NaN               36928    Miss       0     1   0   \n",
+       "1292    male      1       NaN               28664      Mr       0     0   1   \n",
+       "1293  female      0       NaN              112378    Miss       0     1   0   \n",
+       "1294    male      0       NaN              113059      Mr       0     0   1   \n",
+       "1295    male      1       NaN               17765      Mr       0     0   1   \n",
+       "1296    male      0       NaN       SC/PARIS 2166      Mr       0     0   1   \n",
+       "1297    male      1       NaN               28666      Mr       0     0   1   \n",
+       "1298    male      1       NaN              113503      Mr       0     0   1   \n",
+       "1299  female      0       NaN              334915    Miss       0     1   0   \n",
+       "1300  female      1       NaN  SOTON/O.Q. 3101315    Miss       0     1   0   \n",
+       "1301  female      0       NaN              365237    Miss       0     1   0   \n",
+       "1302  female      1       NaN               19928     Mrs       0     0   0   \n",
+       "1303  female      0       NaN              347086    Miss       0     1   0   \n",
+       "1304    male      0       NaN           A.5. 3236      Mr       0     0   1   \n",
+       "1305  female      0       NaN            PC 17758  Others       0     0   0   \n",
+       "1306    male      0       NaN  SOTON/O.Q. 3101262      Mr       0     0   1   \n",
+       "1307    male      0       NaN              359309      Mr       0     0   1   \n",
+       "1308    male      1       NaN                2668  Master       1     0   0   \n",
+       "\n",
+       "      Mrs  Others  \n",
+       "0       0       0  \n",
+       "1       1       0  \n",
+       "2       0       0  \n",
+       "3       1       0  \n",
+       "4       0       0  \n",
+       "5       0       0  \n",
+       "6       0       0  \n",
+       "7       0       0  \n",
+       "8       1       0  \n",
+       "9       1       0  \n",
+       "10      0       0  \n",
+       "11      0       0  \n",
+       "12      0       0  \n",
+       "13      0       0  \n",
+       "14      0       0  \n",
+       "15      1       0  \n",
+       "16      0       0  \n",
+       "17      0       0  \n",
+       "18      1       0  \n",
+       "19      1       0  \n",
+       "20      0       0  \n",
+       "21      0       0  \n",
+       "22      0       0  \n",
+       "23      0       0  \n",
+       "24      0       0  \n",
+       "25      1       0  \n",
+       "26      0       0  \n",
+       "27      0       0  \n",
+       "28      0       0  \n",
+       "29      0       0  \n",
+       "...   ...     ...  \n",
+       "1279    0       0  \n",
+       "1280    0       0  \n",
+       "1281    0       0  \n",
+       "1282    1       0  \n",
+       "1283    0       0  \n",
+       "1284    0       0  \n",
+       "1285    0       0  \n",
+       "1286    1       0  \n",
+       "1287    0       0  \n",
+       "1288    1       0  \n",
+       "1289    0       0  \n",
+       "1290    0       0  \n",
+       "1291    0       0  \n",
+       "1292    0       0  \n",
+       "1293    0       0  \n",
+       "1294    0       0  \n",
+       "1295    0       0  \n",
+       "1296    0       0  \n",
+       "1297    0       0  \n",
+       "1298    0       0  \n",
+       "1299    0       0  \n",
+       "1300    0       0  \n",
+       "1301    0       0  \n",
+       "1302    1       0  \n",
+       "1303    0       0  \n",
+       "1304    0       0  \n",
+       "1305    0       1  \n",
+       "1306    0       0  \n",
+       "1307    0       0  \n",
+       "1308    0       0  \n",
+       "\n",
+       "[1309 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "58e43a50-20cc-40d4-bae8-ab455cd52887",
+    "_execution_state": "idle",
+    "_uuid": "60486db5b7e05da01f617a8555289670e1ab9c3a"
+   },
+   "source": [
+    "### Sex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "_cell_guid": "79870c7d-e04f-4269-bf4d-ff314bfc1e96",
+    "_execution_state": "idle",
+    "_uuid": "c8262045e243302a4d4e84426c4b8c2932a0702b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Sex.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "_cell_guid": "fbf9e34d-01cd-4004-a509-cbc94b66edbc",
+    "_execution_state": "idle",
+    "_uuid": "ff1e3b6673013d9ab3d16f113c3bc57520572d4a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>0.742038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>male</td>\n",
+       "      <td>0.188908</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Sex  Survived\n",
+       "0  female  0.742038\n",
+       "1    male  0.188908"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Sex and Survived\n",
+    "df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "_cell_guid": "b083a394-d2d4-4449-b745-77854545c825",
+    "_execution_state": "idle",
+    "_uuid": "4d5e24fded5c0296f278a2b8100c896c88050635"
+   },
+   "outputs": [],
+   "source": [
+    "# map the two genders to 0 and 1\n",
+    "df.Sex = df.Sex.map({'male':0, 'female':1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "a2e1256c-73d3-4f73-b51c-eb7aaca1790a",
+    "_execution_state": "idle",
+    "_uuid": "025f3222286d5591c0600ef07ed6ed0c8f1a3309"
+   },
+   "source": [
+    "### Age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "_cell_guid": "8ba1f221-c912-4eac-a944-6b0afa88a382",
+    "_execution_state": "idle",
+    "_uuid": "30bc1b17267fb3eb668f4d383b649ecca05051a3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "263"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Age.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "441e1d2e-1b66-4482-ba21-987ac1d96b34",
+    "_execution_state": "idle",
+    "_uuid": "b812f4eb80b8b2d4116dafbc70991f6c9a6d8c1e"
+   },
+   "source": [
+    "### SibSp and Parch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "_cell_guid": "a4e895a6-47cf-4641-8228-14ea595a9ea2",
+    "_execution_state": "idle",
+    "_uuid": "a6cc2c00d763ac3f55f349f21d5679dfd9b5359b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 0)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.SibSp.isnull().sum(axis=0), df.Parch.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "_cell_guid": "cca00b7a-da36-4903-a025-ef4c50dab61e",
+    "_execution_state": "idle",
+    "_uuid": "03437dedc00ad11570635e56ae79faee105bd808"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Family</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.303538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.552795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.578431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.724138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.136364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Family  Survived\n",
+       "0       1  0.303538\n",
+       "1       2  0.552795\n",
+       "2       3  0.578431\n",
+       "3       4  0.724138\n",
+       "4       5  0.200000\n",
+       "5       6  0.136364\n",
+       "6       7  0.333333\n",
+       "7       8  0.000000\n",
+       "8      11  0.000000"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a new feature \"Family\"\n",
+    "df['Family'] = df['SibSp'] + df['Parch'] + 1\n",
+    "\n",
+    "# inspect the correlation between Family and Survived\n",
+    "df[['Family', 'Survived']].groupby(['Family'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "_cell_guid": "5217e017-3029-4fbe-afc6-3315d5937431",
+    "_execution_state": "idle",
+    "_uuid": "5099b2a2783647bce53f71acb07af846641b3c87"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1     790\n",
+       "2     235\n",
+       "3     159\n",
+       "4      43\n",
+       "6      25\n",
+       "5      22\n",
+       "7      16\n",
+       "11     11\n",
+       "8       8\n",
+       "Name: Family, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the amount of people for each Family size\n",
+    "df['Family'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "_cell_guid": "88730abd-facd-4842-ba22-6fab5e2ec332",
+    "_execution_state": "idle",
+    "_uuid": "2df0bad861e11098213fcbb90a7787504c793a9b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Family</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.161290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.303538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.552795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.578431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.724138</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Family  Survived\n",
+       "0       0  0.161290\n",
+       "1       1  0.303538\n",
+       "2       2  0.552795\n",
+       "3       3  0.578431\n",
+       "4       4  0.724138"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Family = df.Family.map(lambda x: 0 if x > 4 else x)\n",
+    "df[['Family', 'Survived']].groupby(['Family'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "_cell_guid": "d26de4ea-d9c9-460d-8a2f-f3e388de9b83",
+    "_execution_state": "idle",
+    "_uuid": "b4055838fa086398aa33e6804cfc2ec871d72fc3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    790\n",
+       "2    235\n",
+       "3    159\n",
+       "0     82\n",
+       "4     43\n",
+       "Name: Family, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Family'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "380e18ed-ce81-4a4c-ac47-bc8ac4db8122",
+    "_execution_state": "idle",
+    "_uuid": "9b02b9181f1224830ad4a93cb0477aa278ec4d38"
+   },
+   "source": [
+    "### Ticket"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "_cell_guid": "3e355a5b-65dc-4072-9cf8-2aa9e2c5c311",
+    "_execution_state": "idle",
+    "_uuid": "09ad80a90051b181a3ffd1dd7deb7671dbce32b8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Ticket.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "_cell_guid": "dce618df-3f32-45a2-814b-ef392c2248b3",
+    "_execution_state": "idle",
+    "_uuid": "c8f76441795cf10e50a38c538da303a488741d08"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0            A/5 21171\n",
+       "1             PC 17599\n",
+       "2     STON/O2. 3101282\n",
+       "3               113803\n",
+       "4               373450\n",
+       "5               330877\n",
+       "6                17463\n",
+       "7               349909\n",
+       "8               347742\n",
+       "9               237736\n",
+       "10             PP 9549\n",
+       "11              113783\n",
+       "12           A/5. 2151\n",
+       "13              347082\n",
+       "14              350406\n",
+       "15              248706\n",
+       "16              382652\n",
+       "17              244373\n",
+       "18              345763\n",
+       "19                2649\n",
+       "Name: Ticket, dtype: object"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Ticket.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "_cell_guid": "78913d9b-373b-41ad-bb86-2c4e3b18e183",
+    "_execution_state": "idle",
+    "_uuid": "4200860d04ec1319a5fa456a11ffe1f7138b7eb8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.630137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.464481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.239203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0.068966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.340426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>F</td>\n",
+       "      <td>0.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>L</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>P</td>\n",
+       "      <td>0.646154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>S</td>\n",
+       "      <td>0.323077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>W</td>\n",
+       "      <td>0.153846</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Ticket  Survived\n",
+       "0       1  0.630137\n",
+       "1       2  0.464481\n",
+       "2       3  0.239203\n",
+       "3       4  0.200000\n",
+       "4       5  0.000000\n",
+       "5       6  0.166667\n",
+       "6       7  0.111111\n",
+       "7       8  0.000000\n",
+       "8       9  1.000000\n",
+       "9       A  0.068966\n",
+       "10      C  0.340426\n",
+       "11      F  0.571429\n",
+       "12      L  0.250000\n",
+       "13      P  0.646154\n",
+       "14      S  0.323077\n",
+       "15      W  0.153846"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Ticket = df.Ticket.map(lambda x: x[0])\n",
+    "\n",
+    "# inspect the correlation between Ticket and Survived\n",
+    "df[['Ticket', 'Survived']].groupby(['Ticket'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "_cell_guid": "efd9bf4b-03bb-4f87-b709-f9662a1b2e51",
+    "_execution_state": "idle",
+    "_uuid": "e0a4426fc762e668c82b46fe9bd41fdb92786fa4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    429\n",
+       "2    278\n",
+       "1    210\n",
+       "P     98\n",
+       "S     98\n",
+       "C     77\n",
+       "A     42\n",
+       "W     19\n",
+       "7     13\n",
+       "F     13\n",
+       "4     11\n",
+       "6      9\n",
+       "L      5\n",
+       "5      3\n",
+       "9      2\n",
+       "8      2\n",
+       "Name: Ticket, dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the amount of people for each type of tickets\n",
+    "df['Ticket'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "_cell_guid": "7d2ecc98-5a2a-4066-87ca-1fe5ca6594d2",
+    "_execution_state": "idle",
+    "_uuid": "7c911250fe2a3ab1ed23f97acce1cdb855cbc181"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>65.771211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>20.235194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>15.380682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.764036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>19.262500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>19.153711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>9.197438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>8.397900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>7.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A</td>\n",
+       "      <td>10.189681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>C</td>\n",
+       "      <td>28.152273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>F</td>\n",
+       "      <td>24.677246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>L</td>\n",
+       "      <td>1.515000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>P</td>\n",
+       "      <td>119.698253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>S</td>\n",
+       "      <td>17.542900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>W</td>\n",
+       "      <td>31.056579</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Ticket        Fare\n",
+       "0       1   65.771211\n",
+       "1       2   20.235194\n",
+       "2       3   15.380682\n",
+       "3       4   16.764036\n",
+       "4       5   19.262500\n",
+       "5       6   19.153711\n",
+       "6       7    9.197438\n",
+       "7       8    8.397900\n",
+       "8       9    7.750000\n",
+       "9       A   10.189681\n",
+       "10      C   28.152273\n",
+       "11      F   24.677246\n",
+       "12      L    1.515000\n",
+       "13      P  119.698253\n",
+       "14      S   17.542900\n",
+       "15      W   31.056579"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['Ticket', 'Fare']].groupby(['Ticket'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "_cell_guid": "21e8a215-01a0-45d0-bc41-20f4a25aa509",
+    "_execution_state": "idle",
+    "_uuid": "d809da11e565b2524707b4086121c45f880a5554"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Pclass</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.147619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.309353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2.904429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>C</td>\n",
+       "      <td>2.558442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>F</td>\n",
+       "      <td>1.846154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>L</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>P</td>\n",
+       "      <td>1.102041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>S</td>\n",
+       "      <td>2.561224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>W</td>\n",
+       "      <td>2.315789</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Ticket    Pclass\n",
+       "0       1  1.147619\n",
+       "1       2  2.309353\n",
+       "2       3  2.904429\n",
+       "3       4  3.000000\n",
+       "4       5  2.333333\n",
+       "5       6  2.111111\n",
+       "6       7  3.000000\n",
+       "7       8  3.000000\n",
+       "8       9  3.000000\n",
+       "9       A  3.000000\n",
+       "10      C  2.558442\n",
+       "11      F  1.846154\n",
+       "12      L  3.000000\n",
+       "13      P  1.102041\n",
+       "14      S  2.561224\n",
+       "15      W  2.315789"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['Ticket', 'Pclass']].groupby(['Ticket'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "4eab8333-25fd-46d1-a307-ac60f4c3f277",
+    "_execution_state": "idle",
+    "_uuid": "08aba02f0f1b6560033ddfcde7796ea2bdb5da7b"
+   },
+   "source": [
+    "### Fare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "_cell_guid": "c245d17f-4245-4afa-a779-9256ce72057b",
+    "_execution_state": "idle",
+    "_uuid": "2f3a3c7dd4140e492f7b56ea21a25b0e54b3af8c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Fare.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "_cell_guid": "fcedc1d2-1fc9-4d15-bda6-4e0c7a8c1b79",
+    "_execution_state": "idle",
+    "_uuid": "279a4049e39c1350bbfb47815f4dc8bd64d84d76"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1043    3\n",
+       "Name: Ticket, dtype: object"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Ticket[df.Fare.isnull()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "_cell_guid": "4809801d-6cae-47d3-bd02-9a9c615f99c6",
+    "_execution_state": "idle",
+    "_uuid": "056b56f133204ca80786d24925efbc93c3b7f399"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1043    3\n",
+       "Name: Pclass, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Pclass[df.Fare.isnull()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "_cell_guid": "9a90fe36-97a8-4378-8a1b-a11324d429e5",
+    "_execution_state": "idle",
+    "_uuid": "7ca9047deed61252837c0c85afa81ffcac8766c1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1043    NaN\n",
+       "Name: Cabin, dtype: object"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Cabin[df.Fare.isnull()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "_cell_guid": "2d51f02f-bcb3-424d-a18b-a251e63aacc9",
+    "_execution_state": "idle",
+    "_uuid": "481f6e7941608bff1133d8adf1354229b1a115fc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1043    S\n",
+       "Name: Embarked, dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Embarked[df.Fare.isnull()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "_cell_guid": "6f9d5841-adf8-4c9c-9c59-39715827a0ad",
+    "_execution_state": "idle",
+    "_uuid": "0835a6f504f77c8d42245b5a9346e169bdc302a3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# use boxplot to visualize the distribution of Fare for each Pclass\n",
+    "sns.boxplot('Pclass','Fare',data=df)\n",
+    "plt.ylim(0, 300) # ignore one data point with Fare > 500\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "_cell_guid": "137bcdac-da05-49e6-8aed-6b4061f40c16",
+    "_execution_state": "idle",
+    "_uuid": "13c768161562d3c2c77a3f0ec8647e9dbf3eb146"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>87.508992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.179196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13.302889</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Fare\n",
+       "Pclass           \n",
+       "1       87.508992\n",
+       "2       21.179196\n",
+       "3       13.302889"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Pclass and Fare\n",
+    "df[['Pclass', 'Fare']].groupby(['Pclass']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "_cell_guid": "95b35c28-1a17-4995-8d76-0160b07f4a68",
+    "_execution_state": "idle",
+    "_uuid": "a187382bb74abb543ec58f1b56c78a3c7767564b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.919302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.642476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.864050</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Fare\n",
+       "Pclass          \n",
+       "1       0.919302\n",
+       "2       0.642476\n",
+       "3       0.864050"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# divide the standard deviation by the mean. A lower ratio means a tighter \n",
+    "# distribution of Fare in each Pclass\n",
+    "df[['Pclass', 'Fare']].groupby(['Pclass']).std() / df[['Pclass', 'Fare']].groupby(['Pclass']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "_cell_guid": "9a40bab3-eee7-43e6-acdc-d70066aa8a34",
+    "_execution_state": "idle",
+    "_uuid": "47d8265715f80a20ede4648c53021ad5a509889e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# use boxplot to visualize the distribution of Fare for each Ticket\n",
+    "sns.boxplot('Ticket','Fare',data=df)\n",
+    "plt.ylim(0, 300) # ignore one data point with Fare > 500\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "_cell_guid": "0c52113f-bf33-4187-9393-49e050b0503c",
+    "_execution_state": "idle",
+    "_uuid": "3f7e7fb7ee1879e529ca16e7af2d72f4131f759f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ticket</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>65.771211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20.235194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15.380682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>16.764036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>19.262500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>19.153711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>9.197438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8.397900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>10.189681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>28.152273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>24.677246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>1.515000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>119.698253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>17.542900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>W</th>\n",
+       "      <td>31.056579</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Fare\n",
+       "Ticket            \n",
+       "1        65.771211\n",
+       "2        20.235194\n",
+       "3        15.380682\n",
+       "4        16.764036\n",
+       "5        19.262500\n",
+       "6        19.153711\n",
+       "7         9.197438\n",
+       "8         8.397900\n",
+       "9         7.750000\n",
+       "A        10.189681\n",
+       "C        28.152273\n",
+       "F        24.677246\n",
+       "L         1.515000\n",
+       "P       119.698253\n",
+       "S        17.542900\n",
+       "W        31.056579"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Ticket and Fare \n",
+    "# (we saw this earlier)\n",
+    "df[['Ticket', 'Fare']].groupby(['Ticket']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "_cell_guid": "cbd10230-dda2-4819-b330-3e67a6bef4a4",
+    "_execution_state": "idle",
+    "_uuid": "c6407a63d494490385a0024f572f3d3b7c1c5dc1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ticket</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.817411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.284139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.351244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.497578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.284367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.735873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.080078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.005961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>0.466846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.736299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>0.556023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>2.236068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>0.944596</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>1.024639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>W</th>\n",
+       "      <td>0.676999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Fare\n",
+       "Ticket          \n",
+       "1       0.817411\n",
+       "2       1.284139\n",
+       "3       1.351244\n",
+       "4       0.497578\n",
+       "5       0.284367\n",
+       "6       0.735873\n",
+       "7       0.080078\n",
+       "8       0.005961\n",
+       "9       0.000000\n",
+       "A       0.466846\n",
+       "C       0.736299\n",
+       "F       0.556023\n",
+       "L       2.236068\n",
+       "P       0.944596\n",
+       "S       1.024639\n",
+       "W       0.676999"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# divide the standard deviation by the mean. A lower ratio means a tighter \n",
+    "# distribution of Fare in each Ticket type\n",
+    "df[['Ticket', 'Fare']].groupby(['Ticket']).std() /  df[['Ticket', 'Fare']].groupby(['Ticket']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "_cell_guid": "43b41c45-b149-44da-a031-42b409966f28",
+    "_execution_state": "idle",
+    "_uuid": "84e953d33cf72578729f20c54cb8c2e8c29bcb54"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# use boxplot to visualize the distribution of Fare for each Embarked\n",
+    "sns.boxplot('Embarked','Fare',data=df)\n",
+    "plt.ylim(0, 300) # ignore one data point with Fare > 500\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "_cell_guid": "c4eb229d-67fc-423f-8d67-5d415e30ee53",
+    "_execution_state": "idle",
+    "_uuid": "c025b7ada92e44d73f768eaa49d39c49fb1dfd0c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Embarked</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>62.336267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>12.409012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>27.418824</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Fare\n",
+       "Embarked           \n",
+       "C         62.336267\n",
+       "Q         12.409012\n",
+       "S         27.418824"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Embarked and Fare\n",
+    "df[['Embarked', 'Fare']].groupby(['Embarked']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "_cell_guid": "e34bef31-0dea-4c44-877e-98a72bd53036",
+    "_execution_state": "idle",
+    "_uuid": "af43b5c1114f3912f0c6b6015e53c12affb86ce5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Embarked</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>1.350514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>1.097278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>1.352954</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Fare\n",
+       "Embarked          \n",
+       "C         1.350514\n",
+       "Q         1.097278\n",
+       "S         1.352954"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# divide the standard deviation by the mean. A lower ratio means a tighter \n",
+    "# distribution of Fare in each Embarked\n",
+    "df[['Embarked', 'Fare']].groupby(['Embarked']).std() /  df[['Embarked', 'Fare']].groupby(['Embarked']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "_cell_guid": "16822339-ab02-4799-b4ba-8c2ae824bb03",
+    "_execution_state": "idle",
+    "_uuid": "f277be6f1b7c6e16d77dc0353f885f28906e229a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>22.117887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>48.395408</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Survived       Fare\n",
+       "0       0.0  22.117887\n",
+       "1       1.0  48.395408"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guess_Fare = df.Fare.loc[ (df.Ticket == '3') & (df.Pclass == 3) & (df.Embarked == 'S')].median()\n",
+    "df.Fare.fillna(guess_Fare , inplace=True)\n",
+    "\n",
+    "# inspect the mean Fare values for people who died and survived\n",
+    "df[['Fare', 'Survived']].groupby(['Survived'],as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "_cell_guid": "e8477ab2-68ca-4632-835f-ba8df5a6ba36",
+    "_execution_state": "idle",
+    "_uuid": "2e55f9e20c72a0d8621b838ed15386c7ce3be9b4"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP-PC\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 483.875x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the distribution of Fare for people who survived and died\n",
+    "grid = sns.FacetGrid(df, hue='Survived', size=4, aspect=1.5)\n",
+    "grid.map(plt.hist, 'Fare', alpha=.5, bins=range(0,210,10))\n",
+    "grid.add_legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "_cell_guid": "0269e823-86a3-442b-b48c-dc659c8b2005",
+    "_execution_state": "idle",
+    "_uuid": "61e09e2b4fc7a0a1f31a3b066e80656794d73702"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the correlation between Fare and Survived using a scatter plot\n",
+    "df[['Fare', 'Survived']].groupby(['Fare'],as_index=False).mean().plot.scatter('Fare','Survived')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "_cell_guid": "dfa08997-c349-4a56-b69d-1b32b9945730",
+    "_execution_state": "idle",
+    "_uuid": "5cd244995e5442dc614ce8dbcf4907d66b285345"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare-bin</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.217877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.201087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.426901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.443243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.645349</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Fare-bin  Survived\n",
+       "0         1  0.217877\n",
+       "1         2  0.201087\n",
+       "2         3  0.426901\n",
+       "3         4  0.443243\n",
+       "4         5  0.645349"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# bin Fare into five intervals with equal amount of people\n",
+    "df['Fare-bin'] = pd.qcut(df.Fare,5,labels=[1,2,3,4,5]).astype(int)\n",
+    "\n",
+    "# inspect the correlation between Fare-bin and Survived\n",
+    "df[['Fare-bin', 'Survived']].groupby(['Fare-bin'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "_cell_guid": "cf7344e8-76ba-4ce7-807e-272f49272423",
+    "_execution_state": "idle",
+    "_uuid": "dc0da43c475aa71894141aa6578e22b01a99750e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1014"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Cabin.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "_cell_guid": "eed848fb-b155-41b2-be78-d04334111893",
+    "_execution_state": "idle",
+    "_uuid": "742f1b8d7a6e09087135754b14da0e9137b9a981"
+   },
+   "outputs": [],
+   "source": [
+    "df = df.drop(labels=['Cabin'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "a61221c4-6b5b-489b-a124-35f2b3fae207",
+    "_execution_state": "idle",
+    "_uuid": "3506dbb251461b09ede6f1ece58c7f15340c6628"
+   },
+   "source": [
+    "### Embarked"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "_cell_guid": "8a8baacb-bcc3-439f-a540-0f5baac20b91",
+    "_execution_state": "idle",
+    "_uuid": "e1d3efbfad7ee51433326e86de59e2b9e8d5fb21"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Embarked.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "_cell_guid": "f7df232f-1f39-4aa6-8285-dcaa0c320e16",
+    "_execution_state": "idle",
+    "_uuid": "b21097135f1b6199b0b341ef12a88398b0c8f7ad"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Title</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1307</td>\n",
+       "      <td>1309</td>\n",
+       "      <td>1309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>S</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Mr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>914</td>\n",
+       "      <td>429</td>\n",
+       "      <td>757</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Embarked Ticket Title\n",
+       "count      1307   1309  1309\n",
+       "unique        3     16     5\n",
+       "top           S      3    Mr\n",
+       "freq        914    429   757"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe(include=['O']) # S is the most common"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "_cell_guid": "d65d5808-4d12-4a6c-8aa5-d67bd800a6fe",
+    "_execution_state": "idle",
+    "_uuid": "0833afac0ae691222f5436be2645a8e9120ca736"
+   },
+   "outputs": [],
+   "source": [
+    "# fill the NAN\n",
+    "df.Embarked.fillna('S' , inplace=True )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "_cell_guid": "c5a5acd2-47f4-4eda-8cee-7f5759a80ec1",
+    "_execution_state": "idle",
+    "_uuid": "49fe53c86777c9b62b94a5b559ce15006368458d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Sex</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.553571</td>\n",
+       "      <td>1.851852</td>\n",
+       "      <td>62.336267</td>\n",
+       "      <td>32.332170</td>\n",
+       "      <td>0.418519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Q</td>\n",
+       "      <td>0.389610</td>\n",
+       "      <td>2.894309</td>\n",
+       "      <td>12.409012</td>\n",
+       "      <td>28.630000</td>\n",
+       "      <td>0.487805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>S</td>\n",
+       "      <td>0.339009</td>\n",
+       "      <td>2.344978</td>\n",
+       "      <td>27.512485</td>\n",
+       "      <td>29.298151</td>\n",
+       "      <td>0.319869</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Embarked  Survived    Pclass       Fare        Age       Sex\n",
+       "0        C  0.553571  1.851852  62.336267  32.332170  0.418519\n",
+       "1        Q  0.389610  2.894309  12.409012  28.630000  0.487805\n",
+       "2        S  0.339009  2.344978  27.512485  29.298151  0.319869"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Embarked and Survived as well as some other features\n",
+    "df[['Embarked', 'Survived','Pclass','Fare', 'Age', 'Sex']].groupby(['Embarked'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "_cell_guid": "2a43f956-9690-4869-bd67-442c338bc57a",
+    "_execution_state": "idle",
+    "_uuid": "1426a18aa9b7a7dd0f18b260ec13f2d245f3a5a4"
+   },
+   "outputs": [],
+   "source": [
+    "df = df.drop(labels='Embarked', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "_cell_guid": "c8fd62bb-30ea-4675-a593-771c6b8c8333",
+    "_execution_state": "idle",
+    "_uuid": "0972d4354355d154e63aa5ec97565260898749f5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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2XQWsbJZXAlcOOzZJGgf2o5K0MIsusJLsB/wOMFNVhwG7AKcD7wTOb2YVehA4sx+BSot0LPBq4Pgk65vHycC5wIlJ7gJObNYlSduzH5WkBeh1kotdgd2T/BD4aWATcDzwymb7GuAPgff2+DnSolTVp4F02XzCMGORpHFkPypJC7PoAquq7kvyp8DXgO8D1wA3AQ9V1SPNbhvpTOW6HWcVkgbHmQclSZLa0cspgnvRuQfGgcDPAXsAL55j15rr9c4qJEmSJGnS9DLJxQuBr1bVlqr6IXAF8DxgzyRbR8b2B+7vMUZJkiRJGgu9FFhfA45O8tPNDEMnAHcA1wGnNfs4q5AkSZKkqbHoAquqbgQ+CtwM3Nq812rgbODNSe4GnkJnaldJkiRJmng9zSJYVW8H3r5N8z3AUb28ryRJkiSNo55uNCxJkiRJ+gkLLEmSJEnqEwssSZIkSeoTCyxJalGSJyT5XJIvJrk9yX9r2g9McmOSu5J8KMnj245VkiTtnAWWJLXr34Hjq+pw4AjgpCRHA+8Ezq+qg4AHgTNbjFGSJM2TBZYktag6vtOs7tY8Cjiezq0wANYAp7YQniRJWiALLElqWZJdkqwHNgNrga8AD1XVI80uG4H92opPkiTNnwWWJLWsqh6tqiOA/encR/CZc+0212uTrEqyLsm6LVu2DDJMSZI0DxZYkjQiquoh4HrgaGDPJFtvBr8/cH+X16yuqpmqmlmyZMlwApUkSV3tuvNdJEmDkmQJ8MOqeijJ7sAL6UxwcR1wGnAZsBK4sr0opfacv/bOOdvPOvHgIUeicZZkF2AdcF9VndJ2PJpsFliS1K6lwJrmf/6PAz5cVR9PcgdwWZL/AXwBuLDNICVpzL0R2AA8ue1ANPkssCSpRVV1C3DkHO330LkeS5LUgyT7Ay8B3gG8ueVwNAUssDTRklwEnAJsrqrDmrY/BH4D2DojwNuq6hPtRChJo8++VGPuXcDvAU/qtkOSVcAqgGXLlg0prOHrdsqt+stJLjTpLgZOmqP9/Ko6onn4hUCSduxi7Es1hpJs/WHgph3t54RB6qepGcGyYp9OVXVDkuVtxyFJ48y+VGPsWOClSU4GngA8OcnfVNV/bjkuTbCpKbCkbbw+yWvozCj0lqp6cK6dPGVAknZop33ptPSjGk1V9VbgrQBJjgN+1+JKg9bTKYJJ9kzy0SRfSrIhyTFJ9k6yNsldzfNe/QpW6pP3Ak8HjgA2AX/WbUdPGZCkrubVl9qPSpo2vV6DdQFwdVU9AziczvSX5wDXVtVBwLXNujQyquqBqnq0qn4EvB9napOkBbMv1bipquu9B5aGYdEFVpInA8+nuTdLVf2gqh4CVgBrmt3WAKf2GqTUT0mWzlp9GXBbW7FI0riyL5WkufVyDdbT6EzN+tdJDgduonMTt32rahNAVW1Kss9cL/acbA1DkkuB44CnJtkIvB04LskRQAH3Ar/ZWoCSNAbsSyVp/nopsHYFng28oapuTHIBCzgdsKpWA6sBZmZmqoc4pK6q6ow5mi8ceiDz4EQTkkbVOPWlktS2XgqsjcDGqrqxWf8onQLrgSRLm9GrpcDmXoMcBzv6cnzWiQcPMRJJGn3d+kz7S0nSuFv0NVhV9Q3g60kOaZpOAO4ArgJWNm0rgSt7ilCSJEmSxkSv98F6A3BJkscD9wC/Tqdo+3CSM4GvAS/v8TMkSZIkaSz0VGBV1XpgZo5NJ/TyvpKk/vF0PEmShqfX+2BJkiRJkhoWWJIkSZLUJxZYkiRJktQnFliSJEmS1CcWWJIkSZLUJxZYkiRJktQnvd4HS9IUctpvSZKkuTmCJUmSJEl94giWJLUoyQHAB4GfBX4ErK6qC5LsDXwIWA7cC7yiqh5sK05J0ujxjJLR5AiWJLXrEeAtVfVM4GjgdUkOBc4Brq2qg4Brm3VJkjTiLLAkqUVVtamqbm6WHwY2APsBK4A1zW5rgFPbiVCSJC2EpwgOgcO3kuYjyXLgSOBGYN+q2gSdIizJPl1eswpYBbBs2bLhBCpJkrpyBEuSRkCSJwKXA2+qqm/P93VVtbqqZqpqZsmSJYMLUJIkzYsFliZakouSbE5y26y2vZOsTXJX87xXmzFKSXajU1xdUlVXNM0PJFnabF8KbG4rPsm+VJLmz1MENekuBv6CzixtW22dPODcJOc062e3EJtEkgAXAhuq6rxZm64CVgLnNs9XthBez7qdIq2xczH2pZI0L45gaaJV1Q3At7ZpdvIAjZJjgVcDxydZ3zxOplNYnZjkLuDEZl1qhX2pJM1fzyNYSXYB1gH3VdUpSQ4ELgP2Bm4GXl1VP+j1c6Q+mtfkAeAEAhq8qvo0kC6bTxhmLNICtToRixNISRpV/ThF8I10phV+crP+TuD8qrosyfuAM4H39uFzpKGrqtXAaoCZmZlqORxp4vmlefLYj0qaNj2dIphkf+AlwAea9QDHAx9tdvGUAY0iJw+QpN7Zl0rSHHq9ButdwO8BP2rWnwI8VFWPNOsb6dwwcztJViVZl2Tdli1begxDWpCtkwfAGE8eIEktsy+VpDksusBKcgqwuapumt08x65zng7gvVs0DEkuBT4DHJJkY5IzcfIASVoQ+1JJmr9ersE6FnhpM9vVE+hcg/UuYM8kuzajWPsD9/ceprQ4VXVGl01OHjAAi5mS22trpNFnXypJ87foEayqemtV7V9Vy4HTgX+sqlcB1wGnNbt5yoAkSZKkqTGI+2CdDbw5yd10rsm6cACfIUmSJEkjpx/TtFNV1wPXN8v3AEf1430nndMRS5IkSZOlLwWWJGn8+COPpEmX5ADgg8DP0pn1enVVXdBuVJp0FliSJEmaVI8Ab6mqm5M8CbgpydqquqPtwDS5BnENliRJktS6qtpUVTc3yw8DG+hyj1apXxzBkiRJ0sRLshw4Erhxjm2rgFUAy5YtG2pc87HQ26As5rYpC+Vp5t05giVJkqSJluSJwOXAm6rq29tur6rVVTVTVTNLliwZfoCaKBZYkiRJmlhJdqNTXF1SVVe0HY8mnwWWJEmSJlKS0Lkn64aqOq/teDQdLLAkSZI0qY4FXg0cn2R98zi57aA02ZzkQpIkSROpqj4NpO04NF0cwZIkSZKkPrHAkiRJkqQ+scCSJEmSpD7xGixJalGSi4BTgM1VdVjTtjfwIWA5cC/wiqp6cFgxDeMGlZIkTSpHsCSpXRcDJ23Tdg5wbVUdBFzbrEuSpDHgCJamVpJ7gYeBR4FHqmqm3Yg0jarqhiTLt2leARzXLK8BrgfOHlpQ0gLYl0rSY1lgadq9oKq+2XYQ0jb2rapNAFW1Kck+3XZMsgpYBbBs2bIhhSdtx75UkhqLPkUwyQFJrkuyIcntSd7YtO+dZG2Su5rnvfoXriRptqpaXVUzVTWzZMmStsORJGnq9XIN1iPAW6rqmcDRwOuSHIrXDmh8FHBNkpuaUYDtJFmVZF2SdVu2bBlyeJpiDyRZCtA8b245HmlHdtiX2o9KmjaLLrCqalNV3dwsPwxsAPajc+3Amma3NcCpvQYpDcixVfVs4MV0fiB4/rY7ODqgllwFrGyWVwJXthiLtDM77EvtRyVNm75cg9VcoH0kcCPzvHbA6wa66zZF8lknHjzkSCZbVd3fPG9O8jHgKOCGdqPStElyKZ0JLZ6aZCPwduBc4MNJzgS+Bry8vQilHbMvlaTH6rnASvJE4HLgTVX17STzel1VrQZWA8zMzFSvcUgLkWQP4HFV9XCz/CLgv7cclqZQVZ3RZdMJQw1EWgT7UknaXk8FVpLd6BRXl1TVFU3zA0mWNqNXXjugUbUv8LHmB4Fdgb+tqqvbDUmSxo59qSRtY9EFVjq96YXAhqo6b9amrdcOnIvXDmhEVdU9wOFtxyFJ48y+VJK218sI1rHAq4Fbk6xv2t6G1w5IkiRJC9btOvxxstC5BCZx7oFFF1hV9Wmg2wVXXjswRJOYmJIkSdI46uU+WJIkSZKkWfoyTbuGYxKGjSVJkqRJ5giWJEmSJPWJBZYkSZIk9YkFliRJkiT1iQWWJEmSJPWJBZYkSZIk9YmzCEqSRt5i7vfnPQIlSW1wBEuSJEmS+sQCS5IkSZL6xAJLkiRJkvrEAkuSJEmS+sQCS5IkSZL6ZOJmEew2a9Q0Wsy/hbNrSRoni+nnnF1QkjRIE1dgSaPOHwF645djSZI0yjxFUJIkSZL6ZGAjWElOAi4AdgE+UFXnDuqzpMUwRzXqzNHhcnR0ccxTjTpzVMM2kBGsJLsAfwm8GDgUOCPJoYP4LGkxzFGNOnNU48A81agzR9WGQY1gHQXcXVX3ACS5DFgB3DGgz9OATeAvu+aoRp05qnFgnmrUmaMaukEVWPsBX5+1vhF47uwdkqwCVjWr30ny5Tne56nANwcS4Whr7bjfPOD9d6DbMV9dVSf172N+bKc5CubpDvTtmPuVQ33MxR0ZZp6ao73ry3EPKbf6ZeT60nnmKPj3mhY7Omb70tHT2v/vW/5vuae+dFAFVuZoq8esVK0GVu/wTZJ1VTXTz8DGwTQedwvHvNMcBfO0m2k8Zhj6cZujPZrG4x7FvnQ+OQr+vabFKOYo2Jd2M43HDL0f96BmEdwIHDBrfX/g/gF9lrQY5qhGnTmqcWCeatSZoxq6QRVYnwcOSnJgkscDpwNXDeizpMUwRzXqzFGNA/NUo84c1dAN5BTBqnokyeuBT9KZEvOiqrp9EW+101MKJtQ0HvdQj7mPOQr+vabJ0I7bHO2LaTxu+9Lx4jEPmDnas2k8ZujxuFO13WmokiRJkqRFGNQpgpIkSZI0dSywJEmSJKlPRrbASnJSki8nuTvJOW3HMyhJ7k1ya5L1SdY1bXsnWZvkruZ5r7bj7FWSi5JsTnLbrLY5jzMdf9787W9J8uz2Iu9uWnIUpiNPJzFHYXrydBpyFCYzT6clR2E68tQcHW/TkKMw+DwdyQIryS7AXwIvBg4FzkhyaLtRDdQLquqIWfPtnwNcW1UHAdc26+PuYmDbG7N1O84XAwc1j1XAe4cU47xNYY7C5OfpxUxQjsJU5umk5yhMWJ5OYY7C5OfpxZij427ScxQGnKcjWWABRwF3V9U9VfUD4DJgRcsxDdMKYE2zvAY4tcVY+qKqbgC+tU1zt+NcAXywOj4L7Jlk6XAinbdpz1GYsDydwBwF83SichQmMk+nPUdhwvLUHJ1IE5WjMPg8HdUCaz/g67PWNzZtk6iAa5LclGRV07ZvVW0CaJ73aS26wep2nOPw9x+HGPtpWvN0nHMUxifOfpjWHIXxztNxiLGfpjVPzdHxMa05Cn3M04HcB6sPMkfbpM4nf2xV3Z9kH2Btki+1HdAIGIe//zjE2E/m6WONy99/XOLsB3N0e+Pw9x+HGPvJPH2scfj7j0OM/WSObm/BOTCqI1gbgQNmre8P3N9SLANVVfc3z5uBj9EZin5g69Bj87y5vQgHqttxjsPffxxi7JspztNxzlEYnzh7NsU5CuOdp+MQY99McZ6ao2NiinMU+pino1pgfR44KMmBSR4PnA5c1XJMfZdkjyRP2roMvAi4jc6xrmx2Wwlc2U6EA9ftOK8CXtPM2nI08G9bh2xHyFTkKEx9no5zjsKU5OmU5yiMd55ORY7C1OepOToGpjxHoZ95WlUj+QBOBu4EvgL8ftvxDOgYnwZ8sXncvvU4gafQmb3kruZ577Zj7cOxXgpsAn5I55eAM7sdJ52h2L9s/va3AjNtx9/lmCY+R5vjnIo8ncQcbWKd+Dydlhxtjmni8nQacrQ5zqnIU3N0fB/TkqPNMQ00T9O8UJIkSZLUo1E9RVCSJEmSxo4FliRJkiT1iQWWJEmSJPWJBZYkSZIk9YkFliRJkiT1iQVWC5K8LEkleUbbsUjdmKcadeaoRp05qnFgnvafBVY7zgA+TedmddKoMk816sxRjTpzVOPAPO0zC6whS/JE4Fg6NzQ7vWl7XJL3JLk9yceTfCLJac225yT5VJKbknwyydIWw9eUME816sxRjTpzVOPAPB0MC6zhOxW4uqruBL6V5NnAfwKWA/8B+C/AMQBJdgPeDZxWVc8BLgLe0UbQmjrmqUadOapRZ45qHJinA7Br2wFMoTOAdzXLlzXruwEfqar2rX05AAABPklEQVQfAd9Icl2z/RDgMGBtEoBdgE3DDVdTyjzVqDNHNerMUY0D83QALLCGKMlTgOOBw5IUncQs4GPdXgLcXlXHDClEyTzVyDNHNerMUY0D83RwPEVwuE4DPlhVP19Vy6vqAOCrwDeBX2nOed0XOK7Z/8vAkiQ/HppN8qw2AtdUMU816sxRjTpzVOPAPB0QC6zhOoPtfxW4HPg5YCNwG/BXwI3Av1XVD+gk/zuTfBFYDzxveOFqSpmnGnXmqEadOapxYJ4OSKqq7RhEZxaXqvpOM1z7OeDYqvpG23FJs5mnGnXmqEadOapxYJ72xmuwRsfHk+wJPB74I5NYI8o81agzRzXqzFGNA/O0B45gSZIkSVKfeA2WJEmSJPWJBZYkSZIk9YkFliRJkiT1iQWWJEmSJPWJBZYkSZIk9cn/B2h/ie+85Tu9AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 864x216 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the correlation between Title and Age\n",
+    "grid = sns.FacetGrid(df, col='Title', size=3, aspect=0.8, sharey=False)\n",
+    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "_cell_guid": "770f5c78-948f-4261-ae6f-dda0a05da8e6",
+    "_execution_state": "idle",
+    "_uuid": "9b312ecc51104501196e3f6806abc92d29e9eb46"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Title</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Master</th>\n",
+       "      <td>5.482642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Miss</th>\n",
+       "      <td>21.795236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mr</th>\n",
+       "      <td>32.252151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mrs</th>\n",
+       "      <td>36.930636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Others</th>\n",
+       "      <td>45.074074</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Age\n",
+       "Title            \n",
+       "Master   5.482642\n",
+       "Miss    21.795236\n",
+       "Mr      32.252151\n",
+       "Mrs     36.930636\n",
+       "Others  45.074074"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the mean Age for each Title\n",
+    "df[['Title', 'Age']].groupby(['Title']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "_cell_guid": "b81954da-2e36-410f-9505-c6b7ae26803f",
+    "_execution_state": "idle",
+    "_uuid": "ba25e608acce46fba9b0c623f8b62b91f29151a7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Title</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Master</th>\n",
+       "      <td>4.161554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Miss</th>\n",
+       "      <td>12.192794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mr</th>\n",
+       "      <td>12.422089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mrs</th>\n",
+       "      <td>12.872625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Others</th>\n",
+       "      <td>11.303253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Age\n",
+       "Title            \n",
+       "Master   4.161554\n",
+       "Miss    12.192794\n",
+       "Mr      12.422089\n",
+       "Mrs     12.872625\n",
+       "Others  11.303253"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the standard deviation of Age for each Title\n",
+    "df[['Title', 'Age']].groupby(['Title']).std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "_cell_guid": "38208976-cd0d-4756-aebc-5d980d7357a5",
+    "_execution_state": "idle",
+    "_uuid": "3d9ea40ed50748bfac7990788916d49eb3886174"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x216 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the correlation between Fare-bin and Age\n",
+    "grid = sns.FacetGrid(df, col='Fare-bin', size=3, aspect=0.8, sharey=False)\n",
+    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "_cell_guid": "d63fb4e1-0c8d-423d-bf93-5821b08fde51",
+    "_execution_state": "idle",
+    "_uuid": "d8472c4371963ccdc90498b657d3f7f26e9f1e99"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fare-bin</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27.848315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>27.898241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>26.144009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>30.728604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>35.877913</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Age\n",
+       "Fare-bin           \n",
+       "1         27.848315\n",
+       "2         27.898241\n",
+       "3         26.144009\n",
+       "4         30.728604\n",
+       "5         35.877913"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the mean Age for each Fare-bin\n",
+    "df[['Fare-bin', 'Age']].groupby(['Fare-bin']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "_cell_guid": "40a7c6d6-c3ae-46f0-baef-352ff8509783",
+    "_execution_state": "idle",
+    "_uuid": "3fb0839aa1bb049ea5998f458ad939272a736345"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fare-bin</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.812580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10.995650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13.523208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.466022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>15.150724</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Age\n",
+       "Fare-bin           \n",
+       "1         10.812580\n",
+       "2         10.995650\n",
+       "3         13.523208\n",
+       "4         17.466022\n",
+       "5         15.150724"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the standard deviation of Age for each Fare-bin\n",
+    "df[['Fare-bin', 'Age']].groupby(['Fare-bin']).std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "_cell_guid": "37c1feaf-efa8-467a-8fe3-90b8773bde01",
+    "_execution_state": "idle",
+    "_uuid": "cab042b668d7f35dd2b778162835bcb5a0bc1aed"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 691.2x432 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the correlation between SibSp and Age\n",
+    "grid = sns.FacetGrid(df, col='SibSp', col_wrap=4, size=3.0, aspect=0.8, sharey=False)\n",
+    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "_cell_guid": "ceab6b91-e6bd-4c0f-b7c2-31847b1ee61b",
+    "_execution_state": "idle",
+    "_uuid": "612aec4215b2e4c16806aa768ddd1f565fdfe78b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SibSp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30.921766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31.058071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23.569444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.312500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8.772727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>14.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age\n",
+       "SibSp           \n",
+       "0      30.921766\n",
+       "1      31.058071\n",
+       "2      23.569444\n",
+       "3      16.312500\n",
+       "4       8.772727\n",
+       "5      10.166667\n",
+       "8      14.500000"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the mean Age for each SibSp\n",
+    "df[['SibSp', 'Age']].groupby(['SibSp']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "_cell_guid": "eb7a277e-0e3c-4507-ba41-2c9d223f293a",
+    "_execution_state": "idle",
+    "_uuid": "d40475272e30ea20b5eeff47d49a5664a2a8303f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SibSp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13.059751</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.974482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>14.910640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.824938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8.005545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5.192944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age\n",
+       "SibSp           \n",
+       "0      13.059751\n",
+       "1      15.974482\n",
+       "2      14.910640\n",
+       "3      11.824938\n",
+       "4       8.005545\n",
+       "5       5.192944\n",
+       "8            NaN"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the standard deviation of Age for each SibSp\n",
+    "df[['SibSp', 'Age']].groupby(['SibSp']).std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "_cell_guid": "ab9d9000-de9f-4ca0-b817-c53184d74c9a",
+    "_execution_state": "idle",
+    "_uuid": "b2201e9af0c379e0b1bcc0e7a23e08043c241645"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 691.2x432 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the correlation between Parch and Age\n",
+    "grid = sns.FacetGrid(df, col='Parch', col_wrap=4, size=3.0, aspect=0.8, sharey=False)\n",
+    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "_cell_guid": "38a4399d-3e36-44ab-8269-a10d6b6aecc8",
+    "_execution_state": "idle",
+    "_uuid": "42db5280471f9bd3acefe047daccfefcd3bf1a00"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31.969401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>24.965625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.975979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.875000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>39.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>41.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age\n",
+       "Parch           \n",
+       "0      31.969401\n",
+       "1      24.965625\n",
+       "2      18.975979\n",
+       "3      38.875000\n",
+       "4      47.600000\n",
+       "5      39.333333\n",
+       "6      41.500000\n",
+       "9            NaN"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the mean Age for each Parch\n",
+    "df[['Parch', 'Age']].groupby(['Parch']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "_cell_guid": "4e914c54-23d4-46dc-9a7e-fc9e0e038254",
+    "_execution_state": "idle",
+    "_uuid": "79cb7ffd9ea2ac77fe8bae5d7264986be56604ec"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>12.422617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18.410885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>14.564066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.295815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14.432602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.032796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.121320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age\n",
+       "Parch           \n",
+       "0      12.422617\n",
+       "1      18.410885\n",
+       "2      14.564066\n",
+       "3      16.295815\n",
+       "4      14.432602\n",
+       "5       1.032796\n",
+       "6       2.121320\n",
+       "9            NaN"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the standard deviation of Age for each Parch\n",
+    "df[['Parch', 'Age']].groupby(['Parch']).std() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "_cell_guid": "020c9de0-ff1c-4eed-be20-cbff19cac018",
+    "_execution_state": "idle",
+    "_uuid": "9334adf52175fe53abf8138378f4a83efaf77ffb"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP-PC\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  self._setitem_with_indexer(indexer, value)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# notice that instead of using Title, we should use its corresponding dummy variables \n",
+    "df_sub = df[['Age','Master','Miss','Mr','Mrs','Others','Fare-bin','SibSp']]\n",
+    "\n",
+    "X_train  = df_sub.dropna().drop('Age', axis=1)\n",
+    "y_train  = df['Age'].dropna()\n",
+    "X_test = df_sub.loc[np.isnan(df.Age)].drop('Age', axis=1)\n",
+    "\n",
+    "regressor = RandomForestRegressor(n_estimators = 300)\n",
+    "regressor.fit(X_train, y_train)\n",
+    "y_pred = np.round(regressor.predict(X_test),1)\n",
+    "df.Age.loc[df.Age.isnull()] = y_pred\n",
+    "\n",
+    "df.Age.isnull().sum(axis=0) # no more NAN now"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "_cell_guid": "8a4ce8e1-b212-4d6a-8d8b-ad56bf8acf72",
+    "_execution_state": "idle",
+    "_uuid": "5f9658c201cd82bf031d49803d4045fe847d81e8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age-bin</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.675000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.452381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.402597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.315789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.427083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.375000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.125000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age-bin  Survived\n",
+       "0        1  0.675000\n",
+       "1        2  0.452381\n",
+       "2        3  0.402597\n",
+       "3        4  0.315789\n",
+       "4        5  0.427083\n",
+       "5        6  0.375000\n",
+       "6        7  0.125000"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bins = [ 0, 4, 12, 18, 30, 50, 65, 100] # This is somewhat arbitrary\n",
+    "age_index = (1,2,3,4,5,6,7) #('baby','child','teenager','young','mid-age','over-50','senior')\n",
+    "df['Age-bin'] = pd.cut(df.Age, bins, labels=age_index).astype(int)\n",
+    "\n",
+    "df[['Age-bin', 'Survived']].groupby(['Age-bin'],as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "_cell_guid": "4f3f0831-83e7-44e4-a69e-5f5fe024e122",
+    "_execution_state": "idle",
+    "_uuid": "a7a0e7795ab628123de85df8cb6c2eac84185165"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.630137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.464481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.239203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0.068966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.340426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>F</td>\n",
+       "      <td>0.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>L</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>P</td>\n",
+       "      <td>0.646154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>S</td>\n",
+       "      <td>0.323077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>W</td>\n",
+       "      <td>0.153846</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Ticket  Survived\n",
+       "0       1  0.630137\n",
+       "1       2  0.464481\n",
+       "2       3  0.239203\n",
+       "3       4  0.200000\n",
+       "4       5  0.000000\n",
+       "5       6  0.166667\n",
+       "6       7  0.111111\n",
+       "7       8  0.000000\n",
+       "8       9  1.000000\n",
+       "9       A  0.068966\n",
+       "10      C  0.340426\n",
+       "11      F  0.571429\n",
+       "12      L  0.250000\n",
+       "13      P  0.646154\n",
+       "14      S  0.323077\n",
+       "15      W  0.153846"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['Ticket', 'Survived']].groupby(['Ticket'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "_cell_guid": "7b0bc205-5f92-4ba1-9a52-8c6b2bb91093",
+    "_execution_state": "idle",
+    "_uuid": "1c5a38e61a105b5ad40fc1f159725c30c65e71bc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    429\n",
+       "2    278\n",
+       "1    210\n",
+       "P     98\n",
+       "S     98\n",
+       "C     77\n",
+       "A     42\n",
+       "W     19\n",
+       "7     13\n",
+       "F     13\n",
+       "4     11\n",
+       "6      9\n",
+       "L      5\n",
+       "5      3\n",
+       "9      2\n",
+       "8      2\n",
+       "Name: Ticket, dtype: int64"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Ticket'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "_cell_guid": "58fe643e-abf9-4761-bba1-674d3304aba2",
+    "_execution_state": "idle",
+    "_uuid": "7f12fa7d7d6ee803835e5768b9da2a92e1468969"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.630137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.464481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.239203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.340426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>P</td>\n",
+       "      <td>0.646154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>S</td>\n",
+       "      <td>0.323077</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Ticket  Survived\n",
+       "0      1  0.630137\n",
+       "1      2  0.464481\n",
+       "2      3  0.239203\n",
+       "3      4  0.166667\n",
+       "4      C  0.340426\n",
+       "5      P  0.646154\n",
+       "6      S  0.323077"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Ticket'] = df['Ticket'].replace(['A','W','F','L','5','6','7','8','9'], '4')\n",
+    "\n",
+    "# check the correlation again\n",
+    "df[['Ticket', 'Survived']].groupby(['Ticket'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "_cell_guid": "76238516-9a91-4238-8fc7-abdd64af5435",
+    "_execution_state": "idle",
+    "_uuid": "ac5457d8c3d71e35495d2aeaca2008cabc1b0bae"
+   },
+   "outputs": [],
+   "source": [
+    "# dummy encoding\n",
+    "df = pd.get_dummies(df,columns=['Ticket'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Master</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Family</th>\n",
+       "      <th>Fare-bin</th>\n",
+       "      <th>Age-bin</th>\n",
+       "      <th>Ticket_1</th>\n",
+       "      <th>Ticket_2</th>\n",
+       "      <th>Ticket_3</th>\n",
+       "      <th>Ticket_4</th>\n",
+       "      <th>Ticket_C</th>\n",
+       "      <th>Ticket_P</th>\n",
+       "      <th>Ticket_S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>28.8</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>54.0</td>\n",
+       "      <td>51.8625</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>11.1333</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>30.0708</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>16.7000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>58.0</td>\n",
+       "      <td>26.5500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>31.2750</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>7.8542</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>55.0</td>\n",
+       "      <td>16.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>29.1250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>32.3</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>18.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>33.0</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8.0292</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>35.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>31.3875</td>\n",
+       "      <td>5</td>\n",
+       "      <td>26</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>29.6</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>263.0000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>24.3</td>\n",
+       "      <td>7.8792</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>28.8</td>\n",
+       "      <td>7.8958</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1280</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1280</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1281</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1281</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>93.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1282</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1282</th>\n",
+       "      <td>51.0</td>\n",
+       "      <td>39.4000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1283</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>20.2500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1284</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1284</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1285</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1285</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>22.0250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1286</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1286</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>60.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1287</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1288</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1288</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>79.2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1289</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1289</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1290</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1290</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>7.7333</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1291</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1291</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>164.8667</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1292</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1292</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>21.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1293</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1293</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>59.4000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1294</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1294</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>47.1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1295</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1295</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>27.7208</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1296</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1296</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>13.8625</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1297</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1298</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1298</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>211.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1299</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>7.7208</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1300</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>13.7750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1301</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1302</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>90.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1303</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1303</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1304</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>28.8</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1305</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>108.9000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1306</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>38.5</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1307</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>28.8</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1308</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3.6</td>\n",
+       "      <td>22.3583</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1309</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Age      Fare  Parch  PassengerId  Pclass  Sex  SibSp  Survived  \\\n",
+       "0     22.0    7.2500      0            1       3    0      1       0.0   \n",
+       "1     38.0   71.2833      0            2       1    1      1       1.0   \n",
+       "2     26.0    7.9250      0            3       3    1      0       1.0   \n",
+       "3     35.0   53.1000      0            4       1    1      1       1.0   \n",
+       "4     35.0    8.0500      0            5       3    0      0       0.0   \n",
+       "5     28.8    8.4583      0            6       3    0      0       0.0   \n",
+       "6     54.0   51.8625      0            7       1    0      0       0.0   \n",
+       "7      2.0   21.0750      1            8       3    0      3       0.0   \n",
+       "8     27.0   11.1333      2            9       3    1      0       1.0   \n",
+       "9     14.0   30.0708      0           10       2    1      1       1.0   \n",
+       "10     4.0   16.7000      1           11       3    1      1       1.0   \n",
+       "11    58.0   26.5500      0           12       1    1      0       1.0   \n",
+       "12    20.0    8.0500      0           13       3    0      0       0.0   \n",
+       "13    39.0   31.2750      5           14       3    0      1       0.0   \n",
+       "14    14.0    7.8542      0           15       3    1      0       0.0   \n",
+       "15    55.0   16.0000      0           16       2    1      0       1.0   \n",
+       "16     2.0   29.1250      1           17       3    0      4       0.0   \n",
+       "17    32.3   13.0000      0           18       2    0      0       1.0   \n",
+       "18    31.0   18.0000      0           19       3    1      1       0.0   \n",
+       "19    33.0    7.2250      0           20       3    1      0       1.0   \n",
+       "20    35.0   26.0000      0           21       2    0      0       0.0   \n",
+       "21    34.0   13.0000      0           22       2    0      0       1.0   \n",
+       "22    15.0    8.0292      0           23       3    1      0       1.0   \n",
+       "23    28.0   35.5000      0           24       1    0      0       1.0   \n",
+       "24     8.0   21.0750      1           25       3    1      3       0.0   \n",
+       "25    38.0   31.3875      5           26       3    1      1       1.0   \n",
+       "26    29.6    7.2250      0           27       3    0      0       0.0   \n",
+       "27    19.0  263.0000      2           28       1    0      3       0.0   \n",
+       "28    24.3    7.8792      0           29       3    1      0       1.0   \n",
+       "29    28.8    7.8958      0           30       3    0      0       0.0   \n",
+       "...    ...       ...    ...          ...     ...  ...    ...       ...   \n",
+       "1279  21.0    7.7500      0         1280       3    0      0       NaN   \n",
+       "1280   6.0   21.0750      1         1281       3    0      3       NaN   \n",
+       "1281  23.0   93.5000      0         1282       1    0      0       NaN   \n",
+       "1282  51.0   39.4000      1         1283       1    1      0       NaN   \n",
+       "1283  13.0   20.2500      2         1284       3    0      0       NaN   \n",
+       "1284  47.0   10.5000      0         1285       2    0      0       NaN   \n",
+       "1285  29.0   22.0250      1         1286       3    0      3       NaN   \n",
+       "1286  18.0   60.0000      0         1287       1    1      1       NaN   \n",
+       "1287  24.0    7.2500      0         1288       3    0      0       NaN   \n",
+       "1288  48.0   79.2000      1         1289       1    1      1       NaN   \n",
+       "1289  22.0    7.7750      0         1290       3    0      0       NaN   \n",
+       "1290  31.0    7.7333      0         1291       3    0      0       NaN   \n",
+       "1291  30.0  164.8667      0         1292       1    1      0       NaN   \n",
+       "1292  38.0   21.0000      0         1293       2    0      1       NaN   \n",
+       "1293  22.0   59.4000      1         1294       1    1      0       NaN   \n",
+       "1294  17.0   47.1000      0         1295       1    0      0       NaN   \n",
+       "1295  43.0   27.7208      0         1296       1    0      1       NaN   \n",
+       "1296  20.0   13.8625      0         1297       2    0      0       NaN   \n",
+       "1297  23.0   10.5000      0         1298       2    0      1       NaN   \n",
+       "1298  50.0  211.5000      1         1299       1    0      1       NaN   \n",
+       "1299  23.0    7.7208      0         1300       3    1      0       NaN   \n",
+       "1300   3.0   13.7750      1         1301       3    1      1       NaN   \n",
+       "1301  23.0    7.7500      0         1302       3    1      0       NaN   \n",
+       "1302  37.0   90.0000      0         1303       1    1      1       NaN   \n",
+       "1303  28.0    7.7750      0         1304       3    1      0       NaN   \n",
+       "1304  28.8    8.0500      0         1305       3    0      0       NaN   \n",
+       "1305  39.0  108.9000      0         1306       1    1      0       NaN   \n",
+       "1306  38.5    7.2500      0         1307       3    0      0       NaN   \n",
+       "1307  28.8    8.0500      0         1308       3    0      0       NaN   \n",
+       "1308   3.6   22.3583      1         1309       3    0      1       NaN   \n",
+       "\n",
+       "       Title  Master  ...  Family  Fare-bin  Age-bin  Ticket_1  Ticket_2  \\\n",
+       "0         Mr       0  ...       2         1        4         0         0   \n",
+       "1        Mrs       0  ...       2         5        5         0         0   \n",
+       "2       Miss       0  ...       1         2        4         0         0   \n",
+       "3        Mrs       0  ...       2         5        5         1         0   \n",
+       "4         Mr       0  ...       1         2        5         0         0   \n",
+       "5         Mr       0  ...       1         2        4         0         0   \n",
+       "6         Mr       0  ...       1         5        6         1         0   \n",
+       "7     Master       1  ...       0         3        1         0         0   \n",
+       "8        Mrs       0  ...       3         3        4         0         0   \n",
+       "9        Mrs       0  ...       2         4        3         0         1   \n",
+       "10      Miss       0  ...       3         3        1         0         0   \n",
+       "11      Miss       0  ...       1         4        6         1         0   \n",
+       "12        Mr       0  ...       1         2        4         0         0   \n",
+       "13        Mr       0  ...       0         4        5         0         0   \n",
+       "14      Miss       0  ...       1         1        3         0         0   \n",
+       "15       Mrs       0  ...       1         3        6         0         1   \n",
+       "16    Master       1  ...       0         4        1         0         0   \n",
+       "17        Mr       0  ...       1         3        5         0         1   \n",
+       "18       Mrs       0  ...       2         3        5         0         0   \n",
+       "19       Mrs       0  ...       1         1        5         0         1   \n",
+       "20        Mr       0  ...       1         4        5         0         1   \n",
+       "21        Mr       0  ...       1         3        5         0         1   \n",
+       "22      Miss       0  ...       1         2        3         0         0   \n",
+       "23        Mr       0  ...       1         4        4         1         0   \n",
+       "24      Miss       0  ...       0         3        2         0         0   \n",
+       "25       Mrs       0  ...       0         4        5         0         0   \n",
+       "26        Mr       0  ...       1         1        4         0         1   \n",
+       "27        Mr       0  ...       0         5        4         1         0   \n",
+       "28      Miss       0  ...       1         2        4         0         0   \n",
+       "29        Mr       0  ...       1         2        4         0         0   \n",
+       "...      ...     ...  ...     ...       ...      ...       ...       ...   \n",
+       "1279      Mr       0  ...       1         1        4         0         0   \n",
+       "1280  Master       1  ...       0         3        2         0         0   \n",
+       "1281      Mr       0  ...       1         5        4         1         0   \n",
+       "1282     Mrs       0  ...       2         4        6         0         0   \n",
+       "1283  Master       1  ...       3         3        3         0         0   \n",
+       "1284      Mr       0  ...       1         2        5         0         0   \n",
+       "1285      Mr       0  ...       0         4        4         0         0   \n",
+       "1286     Mrs       0  ...       2         5        3         1         0   \n",
+       "1287      Mr       0  ...       1         1        4         0         0   \n",
+       "1288     Mrs       0  ...       3         5        5         1         0   \n",
+       "1289      Mr       0  ...       1         1        4         0         0   \n",
+       "1290      Mr       0  ...       1         1        5         0         1   \n",
+       "1291    Miss       0  ...       1         5        4         0         0   \n",
+       "1292      Mr       0  ...       2         3        5         0         1   \n",
+       "1293    Miss       0  ...       2         5        4         1         0   \n",
+       "1294      Mr       0  ...       1         5        3         1         0   \n",
+       "1295      Mr       0  ...       2         4        5         1         0   \n",
+       "1296      Mr       0  ...       1         3        4         0         0   \n",
+       "1297      Mr       0  ...       2         2        4         0         1   \n",
+       "1298      Mr       0  ...       3         5        5         1         0   \n",
+       "1299    Miss       0  ...       1         1        4         0         0   \n",
+       "1300    Miss       0  ...       3         3        1         0         0   \n",
+       "1301    Miss       0  ...       1         1        4         0         0   \n",
+       "1302     Mrs       0  ...       2         5        5         1         0   \n",
+       "1303    Miss       0  ...       1         1        4         0         0   \n",
+       "1304      Mr       0  ...       1         2        4         0         0   \n",
+       "1305  Others       0  ...       1         5        5         0         0   \n",
+       "1306      Mr       0  ...       1         1        5         0         0   \n",
+       "1307      Mr       0  ...       1         2        4         0         0   \n",
+       "1308  Master       1  ...       3         4        1         0         1   \n",
+       "\n",
+       "      Ticket_3  Ticket_4  Ticket_C  Ticket_P  Ticket_S  \n",
+       "0            0         1         0         0         0  \n",
+       "1            0         0         0         1         0  \n",
+       "2            0         0         0         0         1  \n",
+       "3            0         0         0         0         0  \n",
+       "4            1         0         0         0         0  \n",
+       "5            1         0         0         0         0  \n",
+       "6            0         0         0         0         0  \n",
+       "7            1         0         0         0         0  \n",
+       "8            1         0         0         0         0  \n",
+       "9            0         0         0         0         0  \n",
+       "10           0         0         0         1         0  \n",
+       "11           0         0         0         0         0  \n",
+       "12           0         1         0         0         0  \n",
+       "13           1         0         0         0         0  \n",
+       "14           1         0         0         0         0  \n",
+       "15           0         0         0         0         0  \n",
+       "16           1         0         0         0         0  \n",
+       "17           0         0         0         0         0  \n",
+       "18           1         0         0         0         0  \n",
+       "19           0         0         0         0         0  \n",
+       "20           0         0         0         0         0  \n",
+       "21           0         0         0         0         0  \n",
+       "22           1         0         0         0         0  \n",
+       "23           0         0         0         0         0  \n",
+       "24           1         0         0         0         0  \n",
+       "25           1         0         0         0         0  \n",
+       "26           0         0         0         0         0  \n",
+       "27           0         0         0         0         0  \n",
+       "28           1         0         0         0         0  \n",
+       "29           1         0         0         0         0  \n",
+       "...        ...       ...       ...       ...       ...  \n",
+       "1279         1         0         0         0         0  \n",
+       "1280         1         0         0         0         0  \n",
+       "1281         0         0         0         0         0  \n",
+       "1282         0         0         0         1         0  \n",
+       "1283         0         0         1         0         0  \n",
+       "1284         0         0         1         0         0  \n",
+       "1285         1         0         0         0         0  \n",
+       "1286         0         0         0         0         0  \n",
+       "1287         1         0         0         0         0  \n",
+       "1288         0         0         0         0         0  \n",
+       "1289         1         0         0         0         0  \n",
+       "1290         0         0         0         0         0  \n",
+       "1291         1         0         0         0         0  \n",
+       "1292         0         0         0         0         0  \n",
+       "1293         0         0         0         0         0  \n",
+       "1294         0         0         0         0         0  \n",
+       "1295         0         0         0         0         0  \n",
+       "1296         0         0         0         0         1  \n",
+       "1297         0         0         0         0         0  \n",
+       "1298         0         0         0         0         0  \n",
+       "1299         1         0         0         0         0  \n",
+       "1300         0         0         0         0         1  \n",
+       "1301         1         0         0         0         0  \n",
+       "1302         0         0         0         0         0  \n",
+       "1303         1         0         0         0         0  \n",
+       "1304         0         1         0         0         0  \n",
+       "1305         0         0         0         1         0  \n",
+       "1306         0         0         0         0         1  \n",
+       "1307         1         0         0         0         0  \n",
+       "1308         0         0         0         0         0  \n",
+       "\n",
+       "[1309 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "_cell_guid": "e859e686-72db-4608-a89e-24a9e9b52e47",
+    "_execution_state": "idle",
+    "_uuid": "df4d6e82a6a6fac2c995d55f829085f9afee3267"
+   },
+   "outputs": [],
+   "source": [
+    "df = df.drop(labels=['SibSp','Parch','Age','Fare','Title'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = df[0:891]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df1[0:891].drop(['Survived','PassengerId'], axis=1).values\n",
+    "Y = df1[0:891]['Survived'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state = 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP-PC\\Anaconda3\\lib\\site-packages\\sklearn\\svm\\base.py:196: FutureWarning: The default value of gamma will change from 'auto' to 'scale' in version 0.22 to account better for unscaled features. Set gamma explicitly to 'auto' or 'scale' to avoid this warning.\n",
+      "  \"avoid this warning.\", FutureWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
+       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
+       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
+       "  shrinking=True, tol=0.001, verbose=False)"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.svm import SVC\n",
+    "svclassifier = SVC(kernel='rbf')\n",
+    "svclassifier.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "y_pred = svclassifier.predict(X_test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.81      0.91      0.85       160\n",
+      "         1.0       0.83      0.68      0.74       108\n",
+      "\n",
+      "   micro avg       0.81      0.81      0.81       268\n",
+      "   macro avg       0.82      0.79      0.80       268\n",
+      "weighted avg       0.82      0.81      0.81       268\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "C_report = classification_report(y_test,y_pred)\n",
+    "print(C_report)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.8134328358208955\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "Accuracy = accuracy_score(y_test,y_pred)\n",
+    "print(\"Accuracy:\", Accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actual</th>\n",
+       "      <th>Predicted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
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+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
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+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
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+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
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+       "      <th>5</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
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+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>268 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Actual  Predicted\n",
+       "0       1.0        0.0\n",
+       "1       0.0        0.0\n",
+       "2       1.0        1.0\n",
+       "3       0.0        0.0\n",
+       "4       1.0        1.0\n",
+       "5       0.0        0.0\n",
+       "6       1.0        0.0\n",
+       "7       0.0        0.0\n",
+       "8       1.0        0.0\n",
+       "9       0.0        0.0\n",
+       "10      0.0        0.0\n",
+       "11      1.0        1.0\n",
+       "12      1.0        1.0\n",
+       "13      1.0        0.0\n",
+       "14      1.0        0.0\n",
+       "15      1.0        1.0\n",
+       "16      1.0        1.0\n",
+       "17      0.0        0.0\n",
+       "18      0.0        1.0\n",
+       "19      0.0        0.0\n",
+       "20      0.0        0.0\n",
+       "21      1.0        1.0\n",
+       "22      0.0        0.0\n",
+       "23      0.0        1.0\n",
+       "24      0.0        0.0\n",
+       "25      1.0        1.0\n",
+       "26      1.0        1.0\n",
+       "27      0.0        0.0\n",
+       "28      0.0        0.0\n",
+       "29      0.0        0.0\n",
+       "..      ...        ...\n",
+       "238     0.0        0.0\n",
+       "239     1.0        1.0\n",
+       "240     0.0        0.0\n",
+       "241     0.0        0.0\n",
+       "242     1.0        0.0\n",
+       "243     1.0        0.0\n",
+       "244     0.0        0.0\n",
+       "245     0.0        0.0\n",
+       "246     1.0        1.0\n",
+       "247     0.0        1.0\n",
+       "248     0.0        0.0\n",
+       "249     1.0        1.0\n",
+       "250     0.0        0.0\n",
+       "251     0.0        0.0\n",
+       "252     1.0        1.0\n",
+       "253     0.0        0.0\n",
+       "254     1.0        0.0\n",
+       "255     0.0        0.0\n",
+       "256     0.0        0.0\n",
+       "257     1.0        0.0\n",
+       "258     0.0        0.0\n",
+       "259     1.0        1.0\n",
+       "260     0.0        0.0\n",
+       "261     0.0        0.0\n",
+       "262     0.0        0.0\n",
+       "263     1.0        1.0\n",
+       "264     0.0        0.0\n",
+       "265     0.0        0.0\n",
+       "266     0.0        0.0\n",
+       "267     0.0        1.0\n",
+       "\n",
+       "[268 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 78,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2 = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_pred.flatten()})\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[145  15]\n",
+      " [ 35  73]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "print(confusion_matrix(y_test,y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cm = confusion_matrix(y_test,y_pred)\n",
+    "cm_df = pd.DataFrame(cm,\n",
+    "                     index = ['survived','Not'], \n",
+    "                     columns = ['survived','Not'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x324 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.8134328358208955\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(6,4.5))\n",
+    "sns.heatmap(cm_df, annot=True, fmt=\"d\")\n",
+    "plt.title('SVM'.format(accuracy_score(y_test, y_pred)))\n",
+    "plt.ylabel('True label')\n",
+    "plt.xlabel('Predicted label')\n",
+    "plt.savefig('plot_SVM.png', dpi=500, bbox_inches='tight')\n",
+    "plt.show()\n",
+    "print(\"Accuracy:\",accuracy_score(y_test, y_pred))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 90,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.6002386634844868"
+      ]
+     },
+     "execution_count": 90,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import cohen_kappa_score\n",
+    "cohen_kappa_score(y_test, y_pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/titanic-NN.ipynb b/titanic-NN.ipynb
new file mode 100644
index 000000000..c653d775d
--- /dev/null
+++ b/titanic-NN.ipynb
@@ -0,0 +1,10169 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "_cell_guid": "9e319230-1cc2-4b19-ad66-06a07e07fc60",
+    "_execution_state": "idle",
+    "_uuid": "03de849fb872e11b3db2a2c99563783ce6ba784f"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(891, 12) (418, 11) ['PassengerId' 'Survived' 'Pclass' 'Name' 'Sex' 'Age' 'SibSp' 'Parch'\n",
+      " 'Ticket' 'Fare' 'Cabin' 'Embarked']\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP-PC\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n",
+      "of pandas will change to not sort by default.\n",
+      "\n",
+      "To accept the future behavior, pass 'sort=False'.\n",
+      "\n",
+      "To retain the current behavior and silence the warning, pass 'sort=True'.\n",
+      "\n",
+      "  sort=sort)\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "%matplotlib inline\n",
+    "\n",
+    "# ignore Deprecation Warning\n",
+    "import warnings\n",
+    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
+    "\n",
+    "# Random Forest Regressor\n",
+    "from sklearn.ensemble import RandomForestRegressor\n",
+    "\n",
+    "# Neural Network\n",
+    "import keras \n",
+    "from keras.models import Sequential \n",
+    "from keras.layers import Dense\n",
+    "\n",
+    "# load the data\n",
+    "df_train = pd.read_csv('data/train.csv')\n",
+    "df_test = pd.read_csv('data/test.csv')\n",
+    "df = df_train.append(df_test , ignore_index = True)\n",
+    "\n",
+    "# some quick inspections\n",
+    "print(df_train.shape, df_test.shape, df_train.columns.values)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>575</th>\n",
+       "      <td>576</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Patchett, Mr. George</td>\n",
+       "      <td>male</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>358585</td>\n",
+       "      <td>14.5000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>548</th>\n",
+       "      <td>549</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Goldsmith, Mr. Frank John</td>\n",
+       "      <td>male</td>\n",
+       "      <td>33.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>363291</td>\n",
+       "      <td>20.5250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>565</th>\n",
+       "      <td>566</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Davies, Mr. Alfred J</td>\n",
+       "      <td>male</td>\n",
+       "      <td>24.0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/4 48871</td>\n",
+       "      <td>24.1500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>124</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Webber, Miss. Susan</td>\n",
+       "      <td>female</td>\n",
+       "      <td>32.5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27267</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>E101</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>756</th>\n",
+       "      <td>757</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Carlsson, Mr. August Sigfrid</td>\n",
+       "      <td>male</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>350042</td>\n",
+       "      <td>7.7958</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>783</th>\n",
+       "      <td>784</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Johnston, Mr. Andrew G</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>W./C. 6607</td>\n",
+       "      <td>23.4500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>584</th>\n",
+       "      <td>585</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Paulner, Mr. Uscher</td>\n",
+       "      <td>male</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3411</td>\n",
+       "      <td>8.7125</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>558</th>\n",
+       "      <td>559</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Taussig, Mrs. Emil (Tillie Mandelbaum)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>110413</td>\n",
+       "      <td>79.6500</td>\n",
+       "      <td>E67</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>91</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Christmann, Mr. Emil</td>\n",
+       "      <td>male</td>\n",
+       "      <td>29.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>343276</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>763</th>\n",
+       "      <td>764</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Carter, Mrs. William Ernest (Lucile Polk)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>36.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>113760</td>\n",
+       "      <td>120.0000</td>\n",
+       "      <td>B96 B98</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     PassengerId  Survived  Pclass                                       Name  \\\n",
+       "575          576         0       3                       Patchett, Mr. George   \n",
+       "548          549         0       3                  Goldsmith, Mr. Frank John   \n",
+       "565          566         0       3                       Davies, Mr. Alfred J   \n",
+       "123          124         1       2                        Webber, Miss. Susan   \n",
+       "756          757         0       3               Carlsson, Mr. August Sigfrid   \n",
+       "783          784         0       3                     Johnston, Mr. Andrew G   \n",
+       "584          585         0       3                        Paulner, Mr. Uscher   \n",
+       "558          559         1       1     Taussig, Mrs. Emil (Tillie Mandelbaum)   \n",
+       "90            91         0       3                       Christmann, Mr. Emil   \n",
+       "763          764         1       1  Carter, Mrs. William Ernest (Lucile Polk)   \n",
+       "\n",
+       "        Sex   Age  SibSp  Parch      Ticket      Fare    Cabin Embarked  \n",
+       "575    male  19.0      0      0      358585   14.5000      NaN        S  \n",
+       "548    male  33.0      1      1      363291   20.5250      NaN        S  \n",
+       "565    male  24.0      2      0   A/4 48871   24.1500      NaN        S  \n",
+       "123  female  32.5      0      0       27267   13.0000     E101        S  \n",
+       "756    male  28.0      0      0      350042    7.7958      NaN        S  \n",
+       "783    male   NaN      1      2  W./C. 6607   23.4500      NaN        S  \n",
+       "584    male   NaN      0      0        3411    8.7125      NaN        C  \n",
+       "558  female  39.0      1      1      110413   79.6500      E67        S  \n",
+       "90     male  29.0      0      0      343276    8.0500      NaN        S  \n",
+       "763  female  36.0      1      2      113760  120.0000  B96 B98        S  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_train.sample(10)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Pclass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "_cell_guid": "8ac53d3d-bf63-4b0a-b4dd-cf69ebb1b189",
+    "_execution_state": "idle",
+    "_uuid": "7a92a95fd0642b65500aa0fe2d5453f8f9cff24c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df['Pclass'].isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "_cell_guid": "366c466d-d5f1-41f2-9568-3abc426bfc75",
+    "_execution_state": "idle",
+    "_uuid": "d380019bd6179b6ddcde472ce10771788ae204f4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.629630</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.472826</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.242363</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Pclass  Survived\n",
+       "0       1  0.629630\n",
+       "1       2  0.472826\n",
+       "2       3  0.242363"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Pclass and Survived\n",
+    "df[['Pclass', 'Survived']].groupby(['Pclass'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "631684de-c518-485b-9456-34eff56235e0",
+    "_execution_state": "idle",
+    "_uuid": "12fb136756bac860d6e5ef19a0cd5083ccceba69"
+   },
+   "source": [
+    "We can see that a higher class (lower value) has a higher survival rate. This should be a very useful feature."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "cf76f8ac-cc51-4ffb-8066-b605c5aa971c",
+    "_execution_state": "idle",
+    "_uuid": "617016b19a60d3ae9b2b1dbfefb73f4fb56e13a9"
+   },
+   "source": [
+    "### Name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "_cell_guid": "fa11d6a2-ad3d-443b-8579-de525fdd20c1",
+    "_execution_state": "idle",
+    "_uuid": "a40e18c8e9fe2692dec94722cc6ab316d71b5003"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0                              Braund, Mr. Owen Harris\n",
+       "1    Cumings, Mrs. John Bradley (Florence Briggs Th...\n",
+       "2                               Heikkinen, Miss. Laina\n",
+       "3         Futrelle, Mrs. Jacques Heath (Lily May Peel)\n",
+       "4                             Allen, Mr. William Henry\n",
+       "5                                     Moran, Mr. James\n",
+       "6                              McCarthy, Mr. Timothy J\n",
+       "7                       Palsson, Master. Gosta Leonard\n",
+       "8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)\n",
+       "9                  Nasser, Mrs. Nicholas (Adele Achem)\n",
+       "Name: Name, dtype: object"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Name.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "_cell_guid": "bb1fc2fe-1947-4f89-99fa-d621711615fc",
+    "_execution_state": "idle",
+    "_uuid": "93e6890cecf2d3ae919ec30b24588d780092720d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Mr              757\n",
+       "Miss            260\n",
+       "Mrs             197\n",
+       "Master           61\n",
+       "Rev               8\n",
+       "Dr                8\n",
+       "Col               4\n",
+       "Ms                2\n",
+       "Major             2\n",
+       "Mlle              2\n",
+       "Capt              1\n",
+       "Lady              1\n",
+       "Dona              1\n",
+       "Mme               1\n",
+       "Jonkheer          1\n",
+       "the Countess      1\n",
+       "Sir               1\n",
+       "Don               1\n",
+       "Name: Title, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Title'] = df.Name.map( lambda x: x.split(',')[1].split( '.' )[0].strip())\n",
+    "\n",
+    "# inspect the amount of people for each title\n",
+    "df['Title'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "55379dcf-99ec-4919-a624-05e06254859b",
+    "_execution_state": "idle",
+    "_uuid": "0070b1a5c3e802ebdad33ca5b960653be96c5281"
+   },
+   "source": [
+    "Looks like the main ones are \"Master\", \"Miss\", \"Mr\", \"Mrs\". Some of the others can be be merged into some of these four categories. For the rest, I'll just call them \"Others\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "_cell_guid": "67b67929-d7c3-4d52-9cde-eb37b4ef256d",
+    "_execution_state": "idle",
+    "_uuid": "a7840964bab69268bcc0e2516770f7faf00aad1d"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP-PC\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  self._setitem_with_indexer(indexer, value)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Master</td>\n",
+       "      <td>0.575000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0.701087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0.156673</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0.796875</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Others</td>\n",
+       "      <td>0.318182</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Title  Survived\n",
+       "0  Master  0.575000\n",
+       "1    Miss  0.701087\n",
+       "2      Mr  0.156673\n",
+       "3     Mrs  0.796875\n",
+       "4  Others  0.318182"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Title'] = df['Title'].replace('Mlle', 'Miss')\n",
+    "df['Title'] = df['Title'].replace(['Mme','Lady','Ms'], 'Mrs')\n",
+    "df.Title.loc[ (df.Title !=  'Master') & (df.Title !=  'Mr') & (df.Title !=  'Miss') \n",
+    "             & (df.Title !=  'Mrs')] = 'Others'\n",
+    "\n",
+    "# inspect the correlation between Title and Survived\n",
+    "df[['Title', 'Survived']].groupby(['Title'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "_cell_guid": "18a55b3d-677f-4611-a41e-be3f9ff49c67",
+    "_execution_state": "idle",
+    "_uuid": "77a517250b3a9b32da2436b8522e864e4e3e7016"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Mr        757\n",
+       "Miss      262\n",
+       "Mrs       201\n",
+       "Master     61\n",
+       "Others     28\n",
+       "Name: Title, dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the amount of people for each title\n",
+    "df['Title'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "_cell_guid": "516ceeba-2dab-4fff-8f5d-4fe94f4eddc8",
+    "_execution_state": "idle",
+    "_uuid": "3f031015c4e518d5b0b1874bac37a0d899eb2a60"
+   },
+   "outputs": [],
+   "source": [
+    "df = pd.concat([df, pd.get_dummies(df['Title'])], axis=1).drop(labels=['Name'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Master</th>\n",
+       "      <th>Miss</th>\n",
+       "      <th>Mr</th>\n",
+       "      <th>Mrs</th>\n",
+       "      <th>Others</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>330877</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>54.0</td>\n",
+       "      <td>E46</td>\n",
+       "      <td>S</td>\n",
+       "      <td>51.8625</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>17463</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>11.1333</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>347742</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>30.0708</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>237736</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>G6</td>\n",
+       "      <td>S</td>\n",
+       "      <td>16.7000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>PP 9549</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>58.0</td>\n",
+       "      <td>C103</td>\n",
+       "      <td>S</td>\n",
+       "      <td>26.5500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>113783</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>A/5. 2151</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>31.2750</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>347082</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.8542</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>350406</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>55.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>16.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>248706</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>29.1250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>382652</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>244373</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>18.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>345763</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>2649</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>239865</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>D56</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>248698</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>8.0292</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>330923</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>A6</td>\n",
+       "      <td>S</td>\n",
+       "      <td>35.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>113788</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>31.3875</td>\n",
+       "      <td>5</td>\n",
+       "      <td>26</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>347077</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2631</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>C23 C25 C27</td>\n",
+       "      <td>S</td>\n",
+       "      <td>263.0000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>19950</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.8792</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>330959</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.8958</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>349216</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1280</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>364858</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1280</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1281</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>349909</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1281</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>B24</td>\n",
+       "      <td>S</td>\n",
+       "      <td>93.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1282</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>12749</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1282</th>\n",
+       "      <td>51.0</td>\n",
+       "      <td>D28</td>\n",
+       "      <td>S</td>\n",
+       "      <td>39.4000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1283</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PC 17592</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>20.2500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1284</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C.A. 2673</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1284</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1285</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C.A. 30769</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1285</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>22.0250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1286</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>315153</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1286</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>C31</td>\n",
+       "      <td>S</td>\n",
+       "      <td>60.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13695</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1287</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1288</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>371109</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1288</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>B41</td>\n",
+       "      <td>C</td>\n",
+       "      <td>79.2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1289</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>13567</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1289</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1290</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>347065</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1290</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.7333</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1291</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>21332</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1291</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>C7</td>\n",
+       "      <td>S</td>\n",
+       "      <td>164.8667</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1292</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>36928</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1292</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>21.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1293</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28664</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1293</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>59.4000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1294</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>112378</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1294</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>47.1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1295</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>113059</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1295</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>D40</td>\n",
+       "      <td>C</td>\n",
+       "      <td>27.7208</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1296</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>17765</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1296</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>D38</td>\n",
+       "      <td>C</td>\n",
+       "      <td>13.8625</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1297</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SC/PARIS 2166</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1298</td>\n",
+       "      <td>2</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>28666</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1298</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>C80</td>\n",
+       "      <td>C</td>\n",
+       "      <td>211.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1299</td>\n",
+       "      <td>1</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>113503</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.7208</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>334915</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1300</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>13.7750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1301</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SOTON/O.Q. 3101315</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1302</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>365237</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>C78</td>\n",
+       "      <td>Q</td>\n",
+       "      <td>90.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1303</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>19928</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1303</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1304</td>\n",
+       "      <td>3</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>347086</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1305</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>A.5. 3236</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>C105</td>\n",
+       "      <td>C</td>\n",
+       "      <td>108.9000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1306</td>\n",
+       "      <td>1</td>\n",
+       "      <td>female</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>PC 17758</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>38.5</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1307</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>SOTON/O.Q. 3101262</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1308</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>359309</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>C</td>\n",
+       "      <td>22.3583</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1309</td>\n",
+       "      <td>3</td>\n",
+       "      <td>male</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2668</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Age        Cabin Embarked      Fare  Parch  PassengerId  Pclass  \\\n",
+       "0     22.0          NaN        S    7.2500      0            1       3   \n",
+       "1     38.0          C85        C   71.2833      0            2       1   \n",
+       "2     26.0          NaN        S    7.9250      0            3       3   \n",
+       "3     35.0         C123        S   53.1000      0            4       1   \n",
+       "4     35.0          NaN        S    8.0500      0            5       3   \n",
+       "5      NaN          NaN        Q    8.4583      0            6       3   \n",
+       "6     54.0          E46        S   51.8625      0            7       1   \n",
+       "7      2.0          NaN        S   21.0750      1            8       3   \n",
+       "8     27.0          NaN        S   11.1333      2            9       3   \n",
+       "9     14.0          NaN        C   30.0708      0           10       2   \n",
+       "10     4.0           G6        S   16.7000      1           11       3   \n",
+       "11    58.0         C103        S   26.5500      0           12       1   \n",
+       "12    20.0          NaN        S    8.0500      0           13       3   \n",
+       "13    39.0          NaN        S   31.2750      5           14       3   \n",
+       "14    14.0          NaN        S    7.8542      0           15       3   \n",
+       "15    55.0          NaN        S   16.0000      0           16       2   \n",
+       "16     2.0          NaN        Q   29.1250      1           17       3   \n",
+       "17     NaN          NaN        S   13.0000      0           18       2   \n",
+       "18    31.0          NaN        S   18.0000      0           19       3   \n",
+       "19     NaN          NaN        C    7.2250      0           20       3   \n",
+       "20    35.0          NaN        S   26.0000      0           21       2   \n",
+       "21    34.0          D56        S   13.0000      0           22       2   \n",
+       "22    15.0          NaN        Q    8.0292      0           23       3   \n",
+       "23    28.0           A6        S   35.5000      0           24       1   \n",
+       "24     8.0          NaN        S   21.0750      1           25       3   \n",
+       "25    38.0          NaN        S   31.3875      5           26       3   \n",
+       "26     NaN          NaN        C    7.2250      0           27       3   \n",
+       "27    19.0  C23 C25 C27        S  263.0000      2           28       1   \n",
+       "28     NaN          NaN        Q    7.8792      0           29       3   \n",
+       "29     NaN          NaN        S    7.8958      0           30       3   \n",
+       "...    ...          ...      ...       ...    ...          ...     ...   \n",
+       "1279  21.0          NaN        Q    7.7500      0         1280       3   \n",
+       "1280   6.0          NaN        S   21.0750      1         1281       3   \n",
+       "1281  23.0          B24        S   93.5000      0         1282       1   \n",
+       "1282  51.0          D28        S   39.4000      1         1283       1   \n",
+       "1283  13.0          NaN        S   20.2500      2         1284       3   \n",
+       "1284  47.0          NaN        S   10.5000      0         1285       2   \n",
+       "1285  29.0          NaN        S   22.0250      1         1286       3   \n",
+       "1286  18.0          C31        S   60.0000      0         1287       1   \n",
+       "1287  24.0          NaN        Q    7.2500      0         1288       3   \n",
+       "1288  48.0          B41        C   79.2000      1         1289       1   \n",
+       "1289  22.0          NaN        S    7.7750      0         1290       3   \n",
+       "1290  31.0          NaN        Q    7.7333      0         1291       3   \n",
+       "1291  30.0           C7        S  164.8667      0         1292       1   \n",
+       "1292  38.0          NaN        S   21.0000      0         1293       2   \n",
+       "1293  22.0          NaN        C   59.4000      1         1294       1   \n",
+       "1294  17.0          NaN        S   47.1000      0         1295       1   \n",
+       "1295  43.0          D40        C   27.7208      0         1296       1   \n",
+       "1296  20.0          D38        C   13.8625      0         1297       2   \n",
+       "1297  23.0          NaN        S   10.5000      0         1298       2   \n",
+       "1298  50.0          C80        C  211.5000      1         1299       1   \n",
+       "1299   NaN          NaN        Q    7.7208      0         1300       3   \n",
+       "1300   3.0          NaN        S   13.7750      1         1301       3   \n",
+       "1301   NaN          NaN        Q    7.7500      0         1302       3   \n",
+       "1302  37.0          C78        Q   90.0000      0         1303       1   \n",
+       "1303  28.0          NaN        S    7.7750      0         1304       3   \n",
+       "1304   NaN          NaN        S    8.0500      0         1305       3   \n",
+       "1305  39.0         C105        C  108.9000      0         1306       1   \n",
+       "1306  38.5          NaN        S    7.2500      0         1307       3   \n",
+       "1307   NaN          NaN        S    8.0500      0         1308       3   \n",
+       "1308   NaN          NaN        C   22.3583      1         1309       3   \n",
+       "\n",
+       "         Sex  SibSp  Survived              Ticket   Title  Master  Miss  Mr  \\\n",
+       "0       male      1       0.0           A/5 21171      Mr       0     0   1   \n",
+       "1     female      1       1.0            PC 17599     Mrs       0     0   0   \n",
+       "2     female      0       1.0    STON/O2. 3101282    Miss       0     1   0   \n",
+       "3     female      1       1.0              113803     Mrs       0     0   0   \n",
+       "4       male      0       0.0              373450      Mr       0     0   1   \n",
+       "5       male      0       0.0              330877      Mr       0     0   1   \n",
+       "6       male      0       0.0               17463      Mr       0     0   1   \n",
+       "7       male      3       0.0              349909  Master       1     0   0   \n",
+       "8     female      0       1.0              347742     Mrs       0     0   0   \n",
+       "9     female      1       1.0              237736     Mrs       0     0   0   \n",
+       "10    female      1       1.0             PP 9549    Miss       0     1   0   \n",
+       "11    female      0       1.0              113783    Miss       0     1   0   \n",
+       "12      male      0       0.0           A/5. 2151      Mr       0     0   1   \n",
+       "13      male      1       0.0              347082      Mr       0     0   1   \n",
+       "14    female      0       0.0              350406    Miss       0     1   0   \n",
+       "15    female      0       1.0              248706     Mrs       0     0   0   \n",
+       "16      male      4       0.0              382652  Master       1     0   0   \n",
+       "17      male      0       1.0              244373      Mr       0     0   1   \n",
+       "18    female      1       0.0              345763     Mrs       0     0   0   \n",
+       "19    female      0       1.0                2649     Mrs       0     0   0   \n",
+       "20      male      0       0.0              239865      Mr       0     0   1   \n",
+       "21      male      0       1.0              248698      Mr       0     0   1   \n",
+       "22    female      0       1.0              330923    Miss       0     1   0   \n",
+       "23      male      0       1.0              113788      Mr       0     0   1   \n",
+       "24    female      3       0.0              349909    Miss       0     1   0   \n",
+       "25    female      1       1.0              347077     Mrs       0     0   0   \n",
+       "26      male      0       0.0                2631      Mr       0     0   1   \n",
+       "27      male      3       0.0               19950      Mr       0     0   1   \n",
+       "28    female      0       1.0              330959    Miss       0     1   0   \n",
+       "29      male      0       0.0              349216      Mr       0     0   1   \n",
+       "...      ...    ...       ...                 ...     ...     ...   ...  ..   \n",
+       "1279    male      0       NaN              364858      Mr       0     0   1   \n",
+       "1280    male      3       NaN              349909  Master       1     0   0   \n",
+       "1281    male      0       NaN               12749      Mr       0     0   1   \n",
+       "1282  female      0       NaN            PC 17592     Mrs       0     0   0   \n",
+       "1283    male      0       NaN           C.A. 2673  Master       1     0   0   \n",
+       "1284    male      0       NaN          C.A. 30769      Mr       0     0   1   \n",
+       "1285    male      3       NaN              315153      Mr       0     0   1   \n",
+       "1286  female      1       NaN               13695     Mrs       0     0   0   \n",
+       "1287    male      0       NaN              371109      Mr       0     0   1   \n",
+       "1288  female      1       NaN               13567     Mrs       0     0   0   \n",
+       "1289    male      0       NaN              347065      Mr       0     0   1   \n",
+       "1290    male      0       NaN               21332      Mr       0     0   1   \n",
+       "1291  female      0       NaN               36928    Miss       0     1   0   \n",
+       "1292    male      1       NaN               28664      Mr       0     0   1   \n",
+       "1293  female      0       NaN              112378    Miss       0     1   0   \n",
+       "1294    male      0       NaN              113059      Mr       0     0   1   \n",
+       "1295    male      1       NaN               17765      Mr       0     0   1   \n",
+       "1296    male      0       NaN       SC/PARIS 2166      Mr       0     0   1   \n",
+       "1297    male      1       NaN               28666      Mr       0     0   1   \n",
+       "1298    male      1       NaN              113503      Mr       0     0   1   \n",
+       "1299  female      0       NaN              334915    Miss       0     1   0   \n",
+       "1300  female      1       NaN  SOTON/O.Q. 3101315    Miss       0     1   0   \n",
+       "1301  female      0       NaN              365237    Miss       0     1   0   \n",
+       "1302  female      1       NaN               19928     Mrs       0     0   0   \n",
+       "1303  female      0       NaN              347086    Miss       0     1   0   \n",
+       "1304    male      0       NaN           A.5. 3236      Mr       0     0   1   \n",
+       "1305  female      0       NaN            PC 17758  Others       0     0   0   \n",
+       "1306    male      0       NaN  SOTON/O.Q. 3101262      Mr       0     0   1   \n",
+       "1307    male      0       NaN              359309      Mr       0     0   1   \n",
+       "1308    male      1       NaN                2668  Master       1     0   0   \n",
+       "\n",
+       "      Mrs  Others  \n",
+       "0       0       0  \n",
+       "1       1       0  \n",
+       "2       0       0  \n",
+       "3       1       0  \n",
+       "4       0       0  \n",
+       "5       0       0  \n",
+       "6       0       0  \n",
+       "7       0       0  \n",
+       "8       1       0  \n",
+       "9       1       0  \n",
+       "10      0       0  \n",
+       "11      0       0  \n",
+       "12      0       0  \n",
+       "13      0       0  \n",
+       "14      0       0  \n",
+       "15      1       0  \n",
+       "16      0       0  \n",
+       "17      0       0  \n",
+       "18      1       0  \n",
+       "19      1       0  \n",
+       "20      0       0  \n",
+       "21      0       0  \n",
+       "22      0       0  \n",
+       "23      0       0  \n",
+       "24      0       0  \n",
+       "25      1       0  \n",
+       "26      0       0  \n",
+       "27      0       0  \n",
+       "28      0       0  \n",
+       "29      0       0  \n",
+       "...   ...     ...  \n",
+       "1279    0       0  \n",
+       "1280    0       0  \n",
+       "1281    0       0  \n",
+       "1282    1       0  \n",
+       "1283    0       0  \n",
+       "1284    0       0  \n",
+       "1285    0       0  \n",
+       "1286    1       0  \n",
+       "1287    0       0  \n",
+       "1288    1       0  \n",
+       "1289    0       0  \n",
+       "1290    0       0  \n",
+       "1291    0       0  \n",
+       "1292    0       0  \n",
+       "1293    0       0  \n",
+       "1294    0       0  \n",
+       "1295    0       0  \n",
+       "1296    0       0  \n",
+       "1297    0       0  \n",
+       "1298    0       0  \n",
+       "1299    0       0  \n",
+       "1300    0       0  \n",
+       "1301    0       0  \n",
+       "1302    1       0  \n",
+       "1303    0       0  \n",
+       "1304    0       0  \n",
+       "1305    0       1  \n",
+       "1306    0       0  \n",
+       "1307    0       0  \n",
+       "1308    0       0  \n",
+       "\n",
+       "[1309 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "58e43a50-20cc-40d4-bae8-ab455cd52887",
+    "_execution_state": "idle",
+    "_uuid": "60486db5b7e05da01f617a8555289670e1ab9c3a"
+   },
+   "source": [
+    "### Sex"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "_cell_guid": "79870c7d-e04f-4269-bf4d-ff314bfc1e96",
+    "_execution_state": "idle",
+    "_uuid": "c8262045e243302a4d4e84426c4b8c2932a0702b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Sex.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "_cell_guid": "fbf9e34d-01cd-4004-a509-cbc94b66edbc",
+    "_execution_state": "idle",
+    "_uuid": "ff1e3b6673013d9ab3d16f113c3bc57520572d4a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>female</td>\n",
+       "      <td>0.742038</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>male</td>\n",
+       "      <td>0.188908</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Sex  Survived\n",
+       "0  female  0.742038\n",
+       "1    male  0.188908"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Sex and Survived\n",
+    "df[['Sex', 'Survived']].groupby(['Sex'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "_cell_guid": "b083a394-d2d4-4449-b745-77854545c825",
+    "_execution_state": "idle",
+    "_uuid": "4d5e24fded5c0296f278a2b8100c896c88050635"
+   },
+   "outputs": [],
+   "source": [
+    "# map the two genders to 0 and 1\n",
+    "df.Sex = df.Sex.map({'male':0, 'female':1})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "a2e1256c-73d3-4f73-b51c-eb7aaca1790a",
+    "_execution_state": "idle",
+    "_uuid": "025f3222286d5591c0600ef07ed6ed0c8f1a3309"
+   },
+   "source": [
+    "### Age"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "_cell_guid": "8ba1f221-c912-4eac-a944-6b0afa88a382",
+    "_execution_state": "idle",
+    "_uuid": "30bc1b17267fb3eb668f4d383b649ecca05051a3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "263"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Age.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "441e1d2e-1b66-4482-ba21-987ac1d96b34",
+    "_execution_state": "idle",
+    "_uuid": "b812f4eb80b8b2d4116dafbc70991f6c9a6d8c1e"
+   },
+   "source": [
+    "### SibSp and Parch"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "_cell_guid": "a4e895a6-47cf-4641-8228-14ea595a9ea2",
+    "_execution_state": "idle",
+    "_uuid": "a6cc2c00d763ac3f55f349f21d5679dfd9b5359b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 0)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.SibSp.isnull().sum(axis=0), df.Parch.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "_cell_guid": "cca00b7a-da36-4903-a025-ef4c50dab61e",
+    "_execution_state": "idle",
+    "_uuid": "03437dedc00ad11570635e56ae79faee105bd808"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Family</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.303538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.552795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.578431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.724138</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.136364</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>11</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Family  Survived\n",
+       "0       1  0.303538\n",
+       "1       2  0.552795\n",
+       "2       3  0.578431\n",
+       "3       4  0.724138\n",
+       "4       5  0.200000\n",
+       "5       6  0.136364\n",
+       "6       7  0.333333\n",
+       "7       8  0.000000\n",
+       "8      11  0.000000"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# create a new feature \"Family\"\n",
+    "df['Family'] = df['SibSp'] + df['Parch'] + 1\n",
+    "\n",
+    "# inspect the correlation between Family and Survived\n",
+    "df[['Family', 'Survived']].groupby(['Family'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "_cell_guid": "5217e017-3029-4fbe-afc6-3315d5937431",
+    "_execution_state": "idle",
+    "_uuid": "5099b2a2783647bce53f71acb07af846641b3c87"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1     790\n",
+       "2     235\n",
+       "3     159\n",
+       "4      43\n",
+       "6      25\n",
+       "5      22\n",
+       "7      16\n",
+       "11     11\n",
+       "8       8\n",
+       "Name: Family, dtype: int64"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the amount of people for each Family size\n",
+    "df['Family'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "_cell_guid": "88730abd-facd-4842-ba22-6fab5e2ec332",
+    "_execution_state": "idle",
+    "_uuid": "2df0bad861e11098213fcbb90a7787504c793a9b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Family</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\n",
+       "      <td>0.161290</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.303538</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.552795</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.578431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.724138</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Family  Survived\n",
+       "0       0  0.161290\n",
+       "1       1  0.303538\n",
+       "2       2  0.552795\n",
+       "3       3  0.578431\n",
+       "4       4  0.724138"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Family = df.Family.map(lambda x: 0 if x > 4 else x)\n",
+    "df[['Family', 'Survived']].groupby(['Family'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "_cell_guid": "d26de4ea-d9c9-460d-8a2f-f3e388de9b83",
+    "_execution_state": "idle",
+    "_uuid": "b4055838fa086398aa33e6804cfc2ec871d72fc3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1    790\n",
+       "2    235\n",
+       "3    159\n",
+       "0     82\n",
+       "4     43\n",
+       "Name: Family, dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Family'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "380e18ed-ce81-4a4c-ac47-bc8ac4db8122",
+    "_execution_state": "idle",
+    "_uuid": "9b02b9181f1224830ad4a93cb0477aa278ec4d38"
+   },
+   "source": [
+    "### Ticket"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "_cell_guid": "3e355a5b-65dc-4072-9cf8-2aa9e2c5c311",
+    "_execution_state": "idle",
+    "_uuid": "09ad80a90051b181a3ffd1dd7deb7671dbce32b8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Ticket.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "_cell_guid": "dce618df-3f32-45a2-814b-ef392c2248b3",
+    "_execution_state": "idle",
+    "_uuid": "c8f76441795cf10e50a38c538da303a488741d08"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0            A/5 21171\n",
+       "1             PC 17599\n",
+       "2     STON/O2. 3101282\n",
+       "3               113803\n",
+       "4               373450\n",
+       "5               330877\n",
+       "6                17463\n",
+       "7               349909\n",
+       "8               347742\n",
+       "9               237736\n",
+       "10             PP 9549\n",
+       "11              113783\n",
+       "12           A/5. 2151\n",
+       "13              347082\n",
+       "14              350406\n",
+       "15              248706\n",
+       "16              382652\n",
+       "17              244373\n",
+       "18              345763\n",
+       "19                2649\n",
+       "Name: Ticket, dtype: object"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Ticket.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "_cell_guid": "78913d9b-373b-41ad-bb86-2c4e3b18e183",
+    "_execution_state": "idle",
+    "_uuid": "4200860d04ec1319a5fa456a11ffe1f7138b7eb8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.630137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.464481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.239203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0.068966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.340426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>F</td>\n",
+       "      <td>0.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>L</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>P</td>\n",
+       "      <td>0.646154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>S</td>\n",
+       "      <td>0.323077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>W</td>\n",
+       "      <td>0.153846</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Ticket  Survived\n",
+       "0       1  0.630137\n",
+       "1       2  0.464481\n",
+       "2       3  0.239203\n",
+       "3       4  0.200000\n",
+       "4       5  0.000000\n",
+       "5       6  0.166667\n",
+       "6       7  0.111111\n",
+       "7       8  0.000000\n",
+       "8       9  1.000000\n",
+       "9       A  0.068966\n",
+       "10      C  0.340426\n",
+       "11      F  0.571429\n",
+       "12      L  0.250000\n",
+       "13      P  0.646154\n",
+       "14      S  0.323077\n",
+       "15      W  0.153846"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Ticket = df.Ticket.map(lambda x: x[0])\n",
+    "\n",
+    "# inspect the correlation between Ticket and Survived\n",
+    "df[['Ticket', 'Survived']].groupby(['Ticket'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "_cell_guid": "efd9bf4b-03bb-4f87-b709-f9662a1b2e51",
+    "_execution_state": "idle",
+    "_uuid": "e0a4426fc762e668c82b46fe9bd41fdb92786fa4"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    429\n",
+       "2    278\n",
+       "1    210\n",
+       "P     98\n",
+       "S     98\n",
+       "C     77\n",
+       "A     42\n",
+       "W     19\n",
+       "7     13\n",
+       "F     13\n",
+       "4     11\n",
+       "6      9\n",
+       "L      5\n",
+       "5      3\n",
+       "9      2\n",
+       "8      2\n",
+       "Name: Ticket, dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the amount of people for each type of tickets\n",
+    "df['Ticket'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "_cell_guid": "7d2ecc98-5a2a-4066-87ca-1fe5ca6594d2",
+    "_execution_state": "idle",
+    "_uuid": "7c911250fe2a3ab1ed23f97acce1cdb855cbc181"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>65.771211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>20.235194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>15.380682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>16.764036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>19.262500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>19.153711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>9.197438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>8.397900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>7.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A</td>\n",
+       "      <td>10.189681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>C</td>\n",
+       "      <td>28.152273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>F</td>\n",
+       "      <td>24.677246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>L</td>\n",
+       "      <td>1.515000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>P</td>\n",
+       "      <td>119.698253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>S</td>\n",
+       "      <td>17.542900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>W</td>\n",
+       "      <td>31.056579</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Ticket        Fare\n",
+       "0       1   65.771211\n",
+       "1       2   20.235194\n",
+       "2       3   15.380682\n",
+       "3       4   16.764036\n",
+       "4       5   19.262500\n",
+       "5       6   19.153711\n",
+       "6       7    9.197438\n",
+       "7       8    8.397900\n",
+       "8       9    7.750000\n",
+       "9       A   10.189681\n",
+       "10      C   28.152273\n",
+       "11      F   24.677246\n",
+       "12      L    1.515000\n",
+       "13      P  119.698253\n",
+       "14      S   17.542900\n",
+       "15      W   31.056579"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['Ticket', 'Fare']].groupby(['Ticket'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "_cell_guid": "21e8a215-01a0-45d0-bc41-20f4a25aa509",
+    "_execution_state": "idle",
+    "_uuid": "d809da11e565b2524707b4086121c45f880a5554"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Pclass</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1.147619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2.309353</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>2.904429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>2.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>2.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>C</td>\n",
+       "      <td>2.558442</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>F</td>\n",
+       "      <td>1.846154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>L</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>P</td>\n",
+       "      <td>1.102041</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>S</td>\n",
+       "      <td>2.561224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>W</td>\n",
+       "      <td>2.315789</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Ticket    Pclass\n",
+       "0       1  1.147619\n",
+       "1       2  2.309353\n",
+       "2       3  2.904429\n",
+       "3       4  3.000000\n",
+       "4       5  2.333333\n",
+       "5       6  2.111111\n",
+       "6       7  3.000000\n",
+       "7       8  3.000000\n",
+       "8       9  3.000000\n",
+       "9       A  3.000000\n",
+       "10      C  2.558442\n",
+       "11      F  1.846154\n",
+       "12      L  3.000000\n",
+       "13      P  1.102041\n",
+       "14      S  2.561224\n",
+       "15      W  2.315789"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['Ticket', 'Pclass']].groupby(['Ticket'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "4eab8333-25fd-46d1-a307-ac60f4c3f277",
+    "_execution_state": "idle",
+    "_uuid": "08aba02f0f1b6560033ddfcde7796ea2bdb5da7b"
+   },
+   "source": [
+    "### Fare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "_cell_guid": "c245d17f-4245-4afa-a779-9256ce72057b",
+    "_execution_state": "idle",
+    "_uuid": "2f3a3c7dd4140e492f7b56ea21a25b0e54b3af8c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Fare.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {
+    "_cell_guid": "fcedc1d2-1fc9-4d15-bda6-4e0c7a8c1b79",
+    "_execution_state": "idle",
+    "_uuid": "279a4049e39c1350bbfb47815f4dc8bd64d84d76"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1043    3\n",
+       "Name: Ticket, dtype: object"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Ticket[df.Fare.isnull()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "_cell_guid": "4809801d-6cae-47d3-bd02-9a9c615f99c6",
+    "_execution_state": "idle",
+    "_uuid": "056b56f133204ca80786d24925efbc93c3b7f399"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1043    3\n",
+       "Name: Pclass, dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Pclass[df.Fare.isnull()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "_cell_guid": "9a90fe36-97a8-4378-8a1b-a11324d429e5",
+    "_execution_state": "idle",
+    "_uuid": "7ca9047deed61252837c0c85afa81ffcac8766c1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1043    NaN\n",
+       "Name: Cabin, dtype: object"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Cabin[df.Fare.isnull()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "_cell_guid": "2d51f02f-bcb3-424d-a18b-a251e63aacc9",
+    "_execution_state": "idle",
+    "_uuid": "481f6e7941608bff1133d8adf1354229b1a115fc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1043    S\n",
+       "Name: Embarked, dtype: object"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.Embarked[df.Fare.isnull()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "_cell_guid": "6f9d5841-adf8-4c9c-9c59-39715827a0ad",
+    "_execution_state": "idle",
+    "_uuid": "0835a6f504f77c8d42245b5a9346e169bdc302a3"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# use boxplot to visualize the distribution of Fare for each Pclass\n",
+    "sns.boxplot('Pclass','Fare',data=df)\n",
+    "plt.ylim(0, 300) # ignore one data point with Fare > 500\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {
+    "_cell_guid": "137bcdac-da05-49e6-8aed-6b4061f40c16",
+    "_execution_state": "idle",
+    "_uuid": "13c768161562d3c2c77a3f0ec8647e9dbf3eb146"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>87.508992</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>21.179196</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13.302889</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Fare\n",
+       "Pclass           \n",
+       "1       87.508992\n",
+       "2       21.179196\n",
+       "3       13.302889"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Pclass and Fare\n",
+    "df[['Pclass', 'Fare']].groupby(['Pclass']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {
+    "_cell_guid": "95b35c28-1a17-4995-8d76-0160b07f4a68",
+    "_execution_state": "idle",
+    "_uuid": "a187382bb74abb543ec58f1b56c78a3c7767564b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Pclass</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.919302</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>0.642476</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.864050</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Fare\n",
+       "Pclass          \n",
+       "1       0.919302\n",
+       "2       0.642476\n",
+       "3       0.864050"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# divide the standard deviation by the mean. A lower ratio means a tighter \n",
+    "# distribution of Fare in each Pclass\n",
+    "df[['Pclass', 'Fare']].groupby(['Pclass']).std() / df[['Pclass', 'Fare']].groupby(['Pclass']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {
+    "_cell_guid": "9a40bab3-eee7-43e6-acdc-d70066aa8a34",
+    "_execution_state": "idle",
+    "_uuid": "47d8265715f80a20ede4648c53021ad5a509889e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# use boxplot to visualize the distribution of Fare for each Ticket\n",
+    "sns.boxplot('Ticket','Fare',data=df)\n",
+    "plt.ylim(0, 300) # ignore one data point with Fare > 500\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {
+    "_cell_guid": "0c52113f-bf33-4187-9393-49e050b0503c",
+    "_execution_state": "idle",
+    "_uuid": "3f7e7fb7ee1879e529ca16e7af2d72f4131f759f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ticket</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>65.771211</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>20.235194</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>15.380682</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>16.764036</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>19.262500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>19.153711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>9.197438</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>8.397900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>7.750000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>10.189681</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>28.152273</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>24.677246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>1.515000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>119.698253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>17.542900</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>W</th>\n",
+       "      <td>31.056579</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Fare\n",
+       "Ticket            \n",
+       "1        65.771211\n",
+       "2        20.235194\n",
+       "3        15.380682\n",
+       "4        16.764036\n",
+       "5        19.262500\n",
+       "6        19.153711\n",
+       "7         9.197438\n",
+       "8         8.397900\n",
+       "9         7.750000\n",
+       "A        10.189681\n",
+       "C        28.152273\n",
+       "F        24.677246\n",
+       "L         1.515000\n",
+       "P       119.698253\n",
+       "S        17.542900\n",
+       "W        31.056579"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Ticket and Fare \n",
+    "# (we saw this earlier)\n",
+    "df[['Ticket', 'Fare']].groupby(['Ticket']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "metadata": {
+    "_cell_guid": "cbd10230-dda2-4819-b330-3e67a6bef4a4",
+    "_execution_state": "idle",
+    "_uuid": "c6407a63d494490385a0024f572f3d3b7c1c5dc1"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Ticket</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.817411</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.284139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1.351244</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>0.497578</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.284367</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>0.735873</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.080078</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>0.005961</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>0.466846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>0.736299</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>F</th>\n",
+       "      <td>0.556023</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>L</th>\n",
+       "      <td>2.236068</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>P</th>\n",
+       "      <td>0.944596</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>1.024639</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>W</th>\n",
+       "      <td>0.676999</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Fare\n",
+       "Ticket          \n",
+       "1       0.817411\n",
+       "2       1.284139\n",
+       "3       1.351244\n",
+       "4       0.497578\n",
+       "5       0.284367\n",
+       "6       0.735873\n",
+       "7       0.080078\n",
+       "8       0.005961\n",
+       "9       0.000000\n",
+       "A       0.466846\n",
+       "C       0.736299\n",
+       "F       0.556023\n",
+       "L       2.236068\n",
+       "P       0.944596\n",
+       "S       1.024639\n",
+       "W       0.676999"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# divide the standard deviation by the mean. A lower ratio means a tighter \n",
+    "# distribution of Fare in each Ticket type\n",
+    "df[['Ticket', 'Fare']].groupby(['Ticket']).std() /  df[['Ticket', 'Fare']].groupby(['Ticket']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {
+    "_cell_guid": "43b41c45-b149-44da-a031-42b409966f28",
+    "_execution_state": "idle",
+    "_uuid": "84e953d33cf72578729f20c54cb8c2e8c29bcb54"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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zVJkFhKSbJL0k6fGyZUdJuk/StuTxHclySVohqUvSFkmnZVWXmZmNTJYdxPeBswctuxK4PyJmAfcnY4CPA7OSr0XAygzrMjOzEcgsICLiQeCVQYvPAdqT5+3AuWXLb45+PwOmSzouq9rMzGx4430M4piIeAEgeTw6WX488FzZet3Jsv1IWiSpU1Ln9u3bMy3WzKyaFeUgtVKWRdqKEbEqIpoioqm+vj7jsvLjW46aWd7GOyBeLO06Sh5fSpZ3AzPK1msAnh/n2grFtxw1s7yNd0CsBxYmzxcCd5QtvyA5m+l04LXSrqhq5FuOmlkRZHma61rgp8C7JXVLuhD4KvAxSduAjyVjgLuBp4AuYDXwX7OqqxL4lqNm+eno6ODMM89k8+bNeZeSu8xmc42I+Qd46aMp6wZwSVa1VBrfctQsP21tbfT19bF06VLuvvvuvMvJVVEOUluZSZMmDTk2s2x0dHTQ09MDQE9PT9V3EQ6IApo+ffqQYzPLRltb24Bxtd+wywFRQC+//PKQYzPLRql7ONC42jggCkjSkGMzy0ZdXd2Q42rjgCig/mP2Bx6bWTYG72Kq9jMIHRBmZonm5uZ9XUNdXR1z5szJuaJ8OSDMzMq0tbVRU1NT9d0DZHgdhJlZJWpubmbTpk15l1EI7iDMzCyVOwgrvBUrVtDV1ZV3GQdl27ZtAFx22WU5VzJyjY2NFVWvZc8BYYXX1dXFrx//BSfU7c27lBGbvKe/Od/19MM5VzIyz/b4an3bnwPCKsIJdXtZ0lTdFy1l6drO6j7f39L5GEQBHXbYYQPGkydPzqkSM6tmDogC2rNnz4Dx7t27c6rEzKqZA8LMzFI5IMzMLJUDwszMUjkgzMwslQOigHxHOTMrAgdEAe3du3fIsZnZeHBAmJlZKgeEmZmlckCYmVkqB4SZmaXyZH2HKIupqCdPnjxgeo3JkyeP6TTMntbZzEbCHUQBzZw5c8ixmdl4cAdxiLL6S7y1tZXdu3fzrne9i+9973uZfIaZ2VBy6SAkPS3pMUmPSupMlh0l6T5J25LHd+RRW1HMnDmTmpoarr322rxLMbMqlecuprkRMTsimpLxlcD9ETELuD8ZV62pU6dy6qmn0tjYmHcpZlalinQM4hygPXneDpybYy1mZlUvr4AI4B8kbZa0KFl2TES8AJA8Hp1TbWZmRn4HqT8SEc9LOhq4T9IvR/rGJFAWAZxwwglZ1WdmVvVy6SAi4vnk8SVgHdAMvCjpOIDk8aUDvHdVRDRFRFN9ff14lWxmVnXGPSAkHSnpX5WeA38CPA6sBxYmqy0E7hjv2szM7G157GI6BlgnqfT5/yci7pH0MHCbpAuBZ4HP5FCbmZklxj0gIuIp4AMpy18GPjre9ZiZWboineZqZmYF4oAwM7NUDggzM0vlgDAzs1QOCDMzS+WAMDOzVA4IM7Mya9asoaWlhbVr1+ZdSu4cEGZmZVavXg3AypUrc64kfw4IM7PEmjVrBoyrvYvwLUet8Lq7u/n9G5O4trMu71ImrGfemMSR3d15l5G7UvdQsnLlSubPn59TNfmrioBYsWIFXV1deZdxULZt2wZkd8/rrDQ2NlZczWaWrioCoquri0cee4K+qUflXcqIaXcAsPk3/y/nSkau5s1XMtluQ0MDu3pfYElTTybbN7i2s44jGhryLsMKpioCAqBv6lHsOuWTeZcxoR3xxJ15l2B2SCZNmsTevXsHjKuZD1KbmSXKwyFtXG0cEGZmlsoBYWaWmDJlypDjauOAMDNLLFu2bMB4+fLlOVVSDA4IM7NEc3PzgPGcOXNyqqQYHBBmZolrrrlmwHhwR1FtHBBmZomNGzcOGN933305VVIMVXEdRHd3NzVvvubz9DNW8+bLdHf3ZrLtZ3sqa6qNF9/s/9vrmKl9OVcyMs/2TOLkvIuwwqmKgLDK1tjYmHcJB213MlXKETNn5VzJyJxM5X2fx2sKnbGeOqaSpqOpioBoaGjgxbdqfSV1xo544k4aGo4d8+1Wyn+mcqWaV6xYkXMl+cvqF3l3dzc7d+4c8+0OVpoXbax0d3dn8v3IIniqIiDMLD9dXV08svURmD7GGxYwdYy3ORXYXjauhx7Gdg6wHnrY/i/bh1/xYPxubDdXUjUBUfPmKxV1DEK7XgcgjpiWcyUj1z9Z39h3EFbZuru7oZfMfollRlROzb3J93mMVUVAVNq+VYBt294AYNZJlfQL99iK/F5btqZPn57JrqC33nqLvr6xPwmgj/5t1qgGMpiKqaamhsMPP3xsNzq5//s81qoiILwP2yw/N910UybbzfLYBvQfu8yCD1KbVYAsz4LJ8oZPlfQLJkv+HmSvcBfKSTpb0q8kdUm6Mu96zEZjypQpVT/Rm1W+QnUQkiYB3wE+BnQDD0taHxFP5FuZTUT+C9RsaIUKCKAZ6IqIpwAk3QqcAxQ2ILLaTeFdFGaWN0VE3jXsI+nTwNkR8Z+T8QLgQxFxadk6i4BFyfDdwK/GvdDx805gR95F2Kj551e5JvrP7g8jon64lYrWQShl2YAEi4hVwKrxKSdfkjojoinvOmx0/POrXP7Z9SvaQepuYEbZuAF4PqdazMyqWtEC4mFglqQTJU0GzgfW51yTmVlVKtQupojolXQpcC8wCbgpIrbmXFaeqmJX2gTmn1/l8s+Ogh2kNjOz4ijaLiYzMysIB4SZmaVyQBSQpC9L2ippi6RHJX0o75ps5CQdK+lWSb+R9ISkuyX5jp4VQFKDpDskbZP0lKRvSxrjqVcrhwOiYCR9GPgkcFpEnAq0As/lW5WNlCQB64BNEXFSRJwCLAaOybcyG07ys7sd+PuImAXMAqYAX8u1sBwV6iwmA+A4YEdEvAUQERP5as6JaC6wJyL+prQgIh7NsR4bubOAXRHxtwARsVfSF4FnJH05Isb21nIVwB1E8fwDMEPSryV9V9IZeRdkB+X9wOa8i7BReR+DfnYR8TrwNFCVd8JyQBRM8lfKHPrnm9oO/EDS53Ityqw6iEFT+5Qtr0oOiAKKiL0RsSkirgEuBf4875psxLbSH/BWebYCA+ZfkjSN/uNHE3lS0ANyQBSMpHdLmlW2aDbwTF712EF7ADhc0kWlBZL+yLsKK8L9wFRJF8C++9NcD3w7Isb+ptoVwAFRPHVAe3J65BbgFKAt35JspKJ/aoJ5wMeS01y30v/z86STBVf2s/u0pG3Ay0BfRFyXb2X58VQbZmYpJP07YC1wXkRU5YkHDggzM0vlXUxmZpbKAWFmZqkcEGZmlsoBYWZmqRwQVpUk7U1myi19XXkQ7z1T0p2H+PmbJDUNv2Y2n282Ep6sz6rVzoiYnccHJxdgmRWeOwizMpKelrRc0k8ldUo6TdK9yUVv/6Vs1WmS1iUXNP6NpJrk/SuT922V9JVB271a0kPAZ8qW10hql3RtMv6T5LN/IemHkuqS5WdL+mXy/vPG5ZthVc8BYdVqyqBdTH9Z9tpzEfFh4P8C3wc+DZwO/HXZOs3AFcC/BU7i7V/aX46IJuBU4AxJp5a9Z1dE/HFE3JqMa4FbgF9HxBJJ7wSWAK0RcRrQCfyVpCOA1cCfAf8eOHaMvgdmQ/IuJqtWQ+1iWp88PgbURcQbwBuSdkmanrzWERFPAUhaC/wx8HfAX0haRP//rePonyplS/KeHwz6nBuA28qmcjg9Wf8f++9dw2Tgp8B7gN9GxLbk8/43/bP9mmXKAWG2v7eSx76y56Vx6f/M4CkIQtKJwH8D/igiXpX0feCIsnV+P+g9/wTMlXR9ROyif1rp+yJifvlKkmanfJ5Z5ryLyWx0miWdmBx7+EvgIWAa/SHwmqRjgI8Ps40bgbuBH0qqBX4GfERSI4Ckqcm9rH8JnCjppOR981O3ZjbG3EFYtZoiqfxWoPdExIhPdaV/189X6T8G8SCwLiL6JD1C/30FngL+cbiNRMTXJf0BsAb4LPA5YK2kw5NVlkTEr5PdVndJ2kF/GL3/IGo1GxVP1mdmZqm8i8nMzFI5IMzMLJUDwszMUjkgzMwslQPCzMxSOSDMzCyVA8LMzFL9f1LBoZmsHDwLAAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# use boxplot to visualize the distribution of Fare for each Embarked\n",
+    "sns.boxplot('Embarked','Fare',data=df)\n",
+    "plt.ylim(0, 300) # ignore one data point with Fare > 500\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {
+    "_cell_guid": "c4eb229d-67fc-423f-8d67-5d415e30ee53",
+    "_execution_state": "idle",
+    "_uuid": "c025b7ada92e44d73f768eaa49d39c49fb1dfd0c"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Embarked</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>62.336267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>12.409012</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>27.418824</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "               Fare\n",
+       "Embarked           \n",
+       "C         62.336267\n",
+       "Q         12.409012\n",
+       "S         27.418824"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Embarked and Fare\n",
+    "df[['Embarked', 'Fare']].groupby(['Embarked']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {
+    "_cell_guid": "e34bef31-0dea-4c44-877e-98a72bd53036",
+    "_execution_state": "idle",
+    "_uuid": "af43b5c1114f3912f0c6b6015e53c12affb86ce5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Embarked</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>1.350514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Q</th>\n",
+       "      <td>1.097278</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>S</th>\n",
+       "      <td>1.352954</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Fare\n",
+       "Embarked          \n",
+       "C         1.350514\n",
+       "Q         1.097278\n",
+       "S         1.352954"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# divide the standard deviation by the mean. A lower ratio means a tighter \n",
+    "# distribution of Fare in each Embarked\n",
+    "df[['Embarked', 'Fare']].groupby(['Embarked']).std() /  df[['Embarked', 'Fare']].groupby(['Embarked']).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "458e4c5d-5a34-4c88-8200-deff90767bba",
+    "_execution_state": "idle",
+    "_uuid": "145eb63ced56b167bab74eb977a86df69680a106"
+   },
+   "source": [
+    "Looks like Fare indeed has correlation with these three features. I'll guess the missing value using the median value of (Pcalss = 3) & (Ticket = 3) & (Embarked = S)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {
+    "_cell_guid": "16822339-ab02-4799-b4ba-8c2ae824bb03",
+    "_execution_state": "idle",
+    "_uuid": "f277be6f1b7c6e16d77dc0353f885f28906e229a"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>22.117887</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>48.395408</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Survived       Fare\n",
+       "0       0.0  22.117887\n",
+       "1       1.0  48.395408"
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "guess_Fare = df.Fare.loc[ (df.Ticket == '3') & (df.Pclass == 3) & (df.Embarked == 'S')].median()\n",
+    "df.Fare.fillna(guess_Fare , inplace=True)\n",
+    "\n",
+    "# inspect the mean Fare values for people who died and survived\n",
+    "df[['Fare', 'Survived']].groupby(['Survived'],as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {
+    "_cell_guid": "e8477ab2-68ca-4632-835f-ba8df5a6ba36",
+    "_execution_state": "idle",
+    "_uuid": "2e55f9e20c72a0d8621b838ed15386c7ce3be9b4"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP-PC\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:230: UserWarning: The `size` paramter has been renamed to `height`; please update your code.\n",
+      "  warnings.warn(msg, UserWarning)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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\n",
+      "text/plain": [
+       "<Figure size 483.875x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the distribution of Fare for people who survived and died\n",
+    "grid = sns.FacetGrid(df, hue='Survived', size=4, aspect=1.5)\n",
+    "grid.map(plt.hist, 'Fare', alpha=.5, bins=range(0,210,10))\n",
+    "grid.add_legend()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {
+    "_cell_guid": "0269e823-86a3-442b-b48c-dc659c8b2005",
+    "_execution_state": "idle",
+    "_uuid": "61e09e2b4fc7a0a1f31a3b066e80656794d73702"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the correlation between Fare and Survived using a scatter plot\n",
+    "df[['Fare', 'Survived']].groupby(['Fare'],as_index=False).mean().plot.scatter('Fare','Survived')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "bf22eb7f-78ae-4f28-9f2c-af8f754dadf2",
+    "_execution_state": "idle",
+    "_uuid": "92da518eed3f5e86bf7c05216f2697397b01bd9b"
+   },
+   "source": [
+    "We can see that people with lower Fare are less likely to survive. But this is certainly not a smooth curve if we don't bin the data. It would be better to feed machine learning algorithms with intervals of Fare, because using the original Fare values would likely cause over-fitting. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {
+    "_cell_guid": "dfa08997-c349-4a56-b69d-1b32b9945730",
+    "_execution_state": "idle",
+    "_uuid": "5cd244995e5442dc614ce8dbcf4907d66b285345"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Fare-bin</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.217877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.201087</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.426901</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.443243</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.645349</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Fare-bin  Survived\n",
+       "0         1  0.217877\n",
+       "1         2  0.201087\n",
+       "2         3  0.426901\n",
+       "3         4  0.443243\n",
+       "4         5  0.645349"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# bin Fare into five intervals with equal amount of people\n",
+    "df['Fare-bin'] = pd.qcut(df.Fare,5,labels=[1,2,3,4,5]).astype(int)\n",
+    "\n",
+    "# inspect the correlation between Fare-bin and Survived\n",
+    "df[['Fare-bin', 'Survived']].groupby(['Fare-bin'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "c309b91d-cfa8-40fd-815f-1a41692a0b37",
+    "_execution_state": "idle",
+    "_uuid": "e715870983cdd7ce85a3c92a79672b247c6130c2"
+   },
+   "source": [
+    "Now the correlation is clear after binning the data!\n",
+    "\n",
+    "### Cabin"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {
+    "_cell_guid": "cf7344e8-76ba-4ce7-807e-272f49272423",
+    "_execution_state": "idle",
+    "_uuid": "dc0da43c475aa71894141aa6578e22b01a99750e"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1014"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Cabin.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {
+    "_cell_guid": "eed848fb-b155-41b2-be78-d04334111893",
+    "_execution_state": "idle",
+    "_uuid": "742f1b8d7a6e09087135754b14da0e9137b9a981"
+   },
+   "outputs": [],
+   "source": [
+    "df = df.drop(labels=['Cabin'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "a61221c4-6b5b-489b-a124-35f2b3fae207",
+    "_execution_state": "idle",
+    "_uuid": "3506dbb251461b09ede6f1ece58c7f15340c6628"
+   },
+   "source": [
+    "### Embarked"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {
+    "_cell_guid": "8a8baacb-bcc3-439f-a540-0f5baac20b91",
+    "_execution_state": "idle",
+    "_uuid": "e1d3efbfad7ee51433326e86de59e2b9e8d5fb21"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# check if there is any NAN\n",
+    "df.Embarked.isnull().sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {
+    "_cell_guid": "f7df232f-1f39-4aa6-8285-dcaa0c320e16",
+    "_execution_state": "idle",
+    "_uuid": "b21097135f1b6199b0b341ef12a88398b0c8f7ad"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Title</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1307</td>\n",
+       "      <td>1309</td>\n",
+       "      <td>1309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>unique</th>\n",
+       "      <td>3</td>\n",
+       "      <td>16</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>top</th>\n",
+       "      <td>S</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Mr</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>914</td>\n",
+       "      <td>429</td>\n",
+       "      <td>757</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Embarked Ticket Title\n",
+       "count      1307   1309  1309\n",
+       "unique        3     16     5\n",
+       "top           S      3    Mr\n",
+       "freq        914    429   757"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe(include=['O']) # S is the most common"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {
+    "_cell_guid": "d65d5808-4d12-4a6c-8aa5-d67bd800a6fe",
+    "_execution_state": "idle",
+    "_uuid": "0833afac0ae691222f5436be2645a8e9120ca736"
+   },
+   "outputs": [],
+   "source": [
+    "# fill the NAN\n",
+    "df.Embarked.fillna('S' , inplace=True )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {
+    "_cell_guid": "c5a5acd2-47f4-4eda-8cee-7f5759a80ec1",
+    "_execution_state": "idle",
+    "_uuid": "49fe53c86777c9b62b94a5b559ce15006368458d"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Embarked</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Sex</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.553571</td>\n",
+       "      <td>1.851852</td>\n",
+       "      <td>62.336267</td>\n",
+       "      <td>32.332170</td>\n",
+       "      <td>0.418519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Q</td>\n",
+       "      <td>0.389610</td>\n",
+       "      <td>2.894309</td>\n",
+       "      <td>12.409012</td>\n",
+       "      <td>28.630000</td>\n",
+       "      <td>0.487805</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>S</td>\n",
+       "      <td>0.339009</td>\n",
+       "      <td>2.344978</td>\n",
+       "      <td>27.512485</td>\n",
+       "      <td>29.298151</td>\n",
+       "      <td>0.319869</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Embarked  Survived    Pclass       Fare        Age       Sex\n",
+       "0        C  0.553571  1.851852  62.336267  32.332170  0.418519\n",
+       "1        Q  0.389610  2.894309  12.409012  28.630000  0.487805\n",
+       "2        S  0.339009  2.344978  27.512485  29.298151  0.319869"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the correlation between Embarked and Survived as well as some other features\n",
+    "df[['Embarked', 'Survived','Pclass','Fare', 'Age', 'Sex']].groupby(['Embarked'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {
+    "_cell_guid": "2a43f956-9690-4869-bd67-442c338bc57a",
+    "_execution_state": "idle",
+    "_uuid": "1426a18aa9b7a7dd0f18b260ec13f2d245f3a5a4"
+   },
+   "outputs": [],
+   "source": [
+    "df = df.drop(labels='Embarked', axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {
+    "_cell_guid": "c8fd62bb-30ea-4675-a593-771c6b8c8333",
+    "_execution_state": "idle",
+    "_uuid": "0972d4354355d154e63aa5ec97565260898749f5"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x216 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the correlation between Title and Age\n",
+    "grid = sns.FacetGrid(df, col='Title', size=3, aspect=0.8, sharey=False)\n",
+    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {
+    "_cell_guid": "770f5c78-948f-4261-ae6f-dda0a05da8e6",
+    "_execution_state": "idle",
+    "_uuid": "9b312ecc51104501196e3f6806abc92d29e9eb46"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Title</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Master</th>\n",
+       "      <td>5.482642</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Miss</th>\n",
+       "      <td>21.795236</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mr</th>\n",
+       "      <td>32.252151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mrs</th>\n",
+       "      <td>36.930636</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Others</th>\n",
+       "      <td>45.074074</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Age\n",
+       "Title            \n",
+       "Master   5.482642\n",
+       "Miss    21.795236\n",
+       "Mr      32.252151\n",
+       "Mrs     36.930636\n",
+       "Others  45.074074"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the mean Age for each Title\n",
+    "df[['Title', 'Age']].groupby(['Title']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {
+    "_cell_guid": "b81954da-2e36-410f-9505-c6b7ae26803f",
+    "_execution_state": "idle",
+    "_uuid": "ba25e608acce46fba9b0c623f8b62b91f29151a7"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Title</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Master</th>\n",
+       "      <td>4.161554</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Miss</th>\n",
+       "      <td>12.192794</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mr</th>\n",
+       "      <td>12.422089</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mrs</th>\n",
+       "      <td>12.872625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Others</th>\n",
+       "      <td>11.303253</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Age\n",
+       "Title            \n",
+       "Master   4.161554\n",
+       "Miss    12.192794\n",
+       "Mr      12.422089\n",
+       "Mrs     12.872625\n",
+       "Others  11.303253"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the standard deviation of Age for each Title\n",
+    "df[['Title', 'Age']].groupby(['Title']).std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {
+    "_cell_guid": "38208976-cd0d-4756-aebc-5d980d7357a5",
+    "_execution_state": "idle",
+    "_uuid": "3d9ea40ed50748bfac7990788916d49eb3886174"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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fBt7Q9nc9Ffg37S5bgLfRjNgiSZK0LAeUkDREK23Bejfwm8Aj7fqTgfur6qF2fQdLTOqWZFOS7Um279mzZ03BSpIkSdI0W7YFK8lLgd1VdVOSU+Y3L7JrLXZ8VW0GNgPMzc0tuo8kSZKklVuqBVjdW0kXwZOBX0jyEuBxwBNpWrQOT3Jw24p1NLBzfGFKkiRJ0vRbtsCqqjcDbwZoW7B+vapeleRjwNk0IwmeC1w1xjglSZI6s7/WAu8Zk7TQWiYafhPNgBdfpbkn64OjCUmSJEmS+umAJhququuA69rndwMnjj4kafjsNy1p2iU5BvgQ8OM0g1xtrqr3JDkCuAzYSDOB6yuq6r6u4pSkabOWFixJkjRcDwFvrKrjgZOA1yZ5OnABsK2dB3Nbuy5JallgSZKkfVTVrqq6uX3+AHAnzZQsZ9LMf0n7eFY3EUrSdDqgLoKSJGn2JNkIPBu4ETiqqnZBU4QlOXKJYzYBmwA2bNgwmUCnjBMpS7PJFixJkrSkJIcBHwdeX1XfXelxVbW5quaqam7dunXjC1CSpowFliRJWlSSQ2iKqw9X1SfazfcmWd++vh7Y3VV8EjQDsiS5NsmdSe5I8rp2+xFJtia5q318UtexajZYYEmSpH0kCc0ULHdW1bsWvHQ1zfyX4DyYmg4OyKKpYoElSZIWczLwauDUJLe0y0uAC4HTk9wFnN6uS51xQBZNGwe50GAkeRxwPfAjNLl9RVW9NcmxwKXAEcDNwKur6vvdRSpJ06+qbgCyxMunTTKWaefchtNjNQOySKNmgaUh+R5walU92N43cEOS/wO8Abioqi5N8t+B84D3dRmoJEkarb0HZGl6uS57zERHu1xNMW4B3z92EdRgVOPBdvWQdingVOCKdrtdBCRJGpjVDsjiaJcaBwssDUqSg5LcQnMR3Qp8Dbi/qh5qd9lB0y977+M2JdmeZPuePXsmF7AkSVoTB2TRtLGLoAalqh4GTkhyOHAlcPxiuy1y3GZgM8Dc3Nw+r0t9sr/uJAc6waldUyT1wPyALLe1X7ICvIVmAJbLk5wHfB14eUfxacZYYGmQqur+JNfRDNd6eJKD21aso4GdnQYnSZJGxgFZxmepL9kO9Mu6WWMXQQ1GknVtyxVJDgV+jmao1muBs9vd7CIgSZKksZn5Fiwr80FZD2xJchDNlweXV9U1Sb4EXJrkHcAXaPppS5IkSSM38wWWhqOqbqWZ+2Lv7XcDJ04+IkmSJM2aZQssJ2+VpputsP2S5BjgQ8CPA48Am6vqPUmOAC4DNgL3AK+oqvu6ilPqktc1SX22knuw5idvfRZwAnBGkpOAd9JM3noccB/N5K2SpP17CHhjVR1PMwjLa5M8HbgA2NZeU7e165IkqWeWLbCcvFWSRqeqdlXVze3zB2gGYnkKcCbNtRS8pkqS1FsrGkVwtZO3tsc6gaskLSLJRpr7Bm8EjqqqXdAUYcCRSxzjNVWSpCm2okEuVjt5a3usE7hK0l6SHAZ8HHh9VX03WWoKl0fzmiodGCfLljRpBzQPVlXdD1zHgslb25ecvFWSVijJITTF1Yer6hPt5nuTrG9fX0/TY0CSJPXMsgWWk7dK0uikaar6IHBnVb1rwUtX01xLwWuqpkCSi5PsTnL7gm1vS/LNJLe0y0u6jFGSptFKugg6easkjc7JwKuB29p7WwHeAlwIXJ7kPODrwMs7im9qOFR35y4B3kszrcBCF1XV700+HEnqh2ULrL5N3mpfa0nTrKpuAJa64eq0ScYi7U9VXd8OxCJJOgAHdA+WJEmaeb+W5Na2C+GTltrJES8lzaoVjSIoSdK42POgV94HvJ1m5OC3A78P/NJiOzripSYlycXAS4HdVfXMdtvbgH8PzFf3b6mqT3YToWaNLViSJGlFqureqnq4qh4B/pgpvFVAM+kS4IxFtl9UVSe0i8WVJsYCS5Ikrcj8VAKtlwG3L7WvNClVdT3wna7jkOZZYEmSpH0k+SjwGeBpSXa0I1z+TpLbktwKvBA4v9Mgpf1b9n5B7xXUOHgPliRJ2kdVnbPIZqdkUV+s6H5B7xXUONiCJUmSpEHxfkF1yQJLkiRJg+L9guqSXQQlSZLUW+39gqcAP5ZkB/BW4JQkJ9B0EbwH+JXOApwSTokxORZYkiRJ6i3vF9S0sYugBiPJMUmuTXJnkjuSvK7dfkSSrUnuah8XHUlIkiRJWisLLA3JQ8Abq+p44CTgtUmeDlwAbKuq44Bt7bokSZI0chZYGoyq2lVVN7fPHwDuBJ4CnAlsaXfbApzVTYSSJEkaOgssDVKSjcCzgRuBo6pqFzRFGHDkIvs70aAkSZLWzEEuNDhJDgM+Dry+qr6bZNljnGhQkvqtTyOkLRXr+ac/dcKRSBoHW7A0KEkOoSmuPlxVn2g33zs/H0b7uLur+CRJkjRsyxZYjsymvkjTVPVB4M6qeteCl64Gzm2fnwtcNenYJEmSNBtW0kVwfmS2m5M8AbgpyVbgNTQjs12Y5AKakdneNL5QpWWdDLwauC3JLe22twAXApcnOQ/4OvDyjuKTJEnqPbu57t+yBVY7KMD8AAEPJFk4Mtsp7W5bgOuwwFKHquoGYKkbrk6bZCySJEmaTQc0yMX+RmZLss/IbO0xm4BNABs2bFhLrBNlZS5JkiTpQK14kIu9R2Zb6XFVtbmq5qpqbt26dauJUZIkTViSi5PsTnL7gm3efy1Jy1hRC9b+RmZrW68cmU2SpGG5BHgv8KEF2y7A+68lLcEeYI2VjCLoyGySJM2Yqroe+M5em8+kue+a9vGsiQYlLcLWVk2blbRgOTKbJEmCFd5/Df29B3ta2TKwX5dga6umyEpGEXRkNkmSdECqajOwGWBubq46DkcDVlXXtwOxLeRo1+rMige5kCRJM+/e9r5rvP9aU+5Rra3Akq2t0qhZYEmSpJXy/msNSpJNSbYn2b5nz56uw9FAWGBJkqR9JPko8BngaUl2tPdcXwicnuQu4PR2XZpGK2ptdTohjcMBTTQsSVq7JBcDLwV2V9Uz221HAJcBG4F7gFdU1X2jfm9vlNdKVdU5S7zk/dfqg/nW1guxtVUTZguWJE3eJcAZe22bH/HqOGBbuy5JWoatrZo2tmBJA2VLxfRyxCtJGh1bWzVtbMGSpOmwohGvvCFbkqTpZguWJPWIcwtpli3VMj8UQz8/aVbYgiVJ08H5hSRJGgBbsA7Q/r5d8t4WSWvgiFeSNBC2Rs42W7AkacIc8UqSpOGyBUuSJswRryRJGi5bsCRJkiRpRCywNBhJLk6yO8ntC7YdkWRrkrvaxyd1GaMkSZKGzS6CGpJLgPcCH1qw7QJgW1VdmOSCdt3JW6W9eEO2JEmjYQuWBqOqrge+s9fmM4Et7fMtwFkTDUqSJEkzZdkCy25X6rmjqmoXQPt45GI7JdmUZHuS7Xv27JlogJIkSRqOlbRgXQKcsde2+W5XxwHb2nWpt6pqc1XNVdXcunXrug5HkiRJPbVsgWW3K/XcvUnWA7SPuzuOR5J6L8k9SW5LckuS7V3HI0nTZLWDXDyq21WSRbtdQdP1CtgEsGHDhlW+nbRqVwPn0kzaei5wVbfhdG9/gxmcf/pTJxiJpJ57YVV9q+sgpP1Jcg/wAPAw8FBVzXUbkWbB2Ae5sOuVJiXJR4HPAE9LsiPJeTSF1elJ7gJOb9clSdLseGFVnWBxpUlZbQvWvUnWt61XdrtaxlItBrYWjFZVnbPES6dNNBBJY7WaIeW93o5cAZ9OUsD7q2rz3jvYg0XSrFptC9Z8tyuw25UkSbPm5Kp6DvBi4LVJXrD3DvZg0ZSY/zLgprbofxRHEdY4rGSYdrtdSZKkH6iqne3jbuBK4MRuI5KWtN8vA/wiQOOwbBdBu12t3Gq6rUiS1CdJHg88pqoeaJ+/CPivHYclLWrhlwFJ5r8MuL7bqDR0Yx/kQpIkDcpRwA1Jvgh8DviTqvpUxzFJ+0jy+CRPmH9O82XA7d1GpVmw2kEuJEnSDKqqu4FndR2HtAJHAVcmgeYz70f8MkCTYIElSZKkwRnllwGOCK0DYRdBSZIkSRoRW7AkSYPnt8+SpEmxBUuSJEmSRsQCS5IkSZJGxAJLkiRJkkbEe7AkLetA71/xfhdJkjSrbMGSJEmSpBGxBUs6AENvmVnq/CRJ0r78f3NlVvN76vNnK1uwJEmSJGlELLAkSZIkaUTsIihp1ewaIUmS9Gi9LLCG8qFuEvfzDP2eIUmSZtmBfiY60NFf93eMNE59/gzbywJLkqRR6PN/4JKk6bSmAivJGcB7gIOAD1TVhSOJqjWUlqoDNavnPU7jztWl+Ld8tFkbRWg1uspV6UCYp+oLc1VdWPUgF0kOAv4QeDHwdOCcJE8fVWDSqJir6gtzVX1gnqovzFV1ZS2jCJ4IfLWq7q6q7wOXAmeOJixppMxV9YW5qj4wT9UX5qo6sZYugk8BvrFgfQfwz/feKckmYFO7+mCSLy/ys34M+NYaYumrzs77DV286Q8tdd6fqqozxvB+y+bqCvMUloi949/nJJirjzbtueo1dY169m96f+c9jlz1//+169U1dYT/Hvp6TQVzdWpM6Pq8plxdS4GVRbbVPhuqNgOb9/uDku1VNbeGWHrJ857cWy6y7VG5upI8Bf9mXccxaX3NVf9es2Ua8xTM1f3xvCf3lots8///A+B5r85augjuAI5ZsH40sHMNP08aF3NVfWGuqg/MU/WFuapOrKXA+jxwXJJjkzwWeCVw9WjCkkbKXFVfmKvqA/NUfWGuqhOr7iJYVQ8l+TXgT2mGvry4qu5Y5Y9btml2oDzvCTBXR8LznoAR5qp/r9nS1zwF/2azxlztH897FVK1T7dpSZIkSdIqrKWLoCRJkiRpAQssSZIkSRqRzgusJGck+XKSrya5oOt4xinJPUluS3JLku3ttiOSbE1yV/v4pK7jXKskFyfZneT2BdsWPc80/qD9+9+a5DndRb4083R4eQrmat/NSq4OMU/BXDVX+5Gr5unw8hTGn6udFlhJDgL+EHgx8HTgnCRP7zKmCXhhVZ2wYGz9C4BtVXUcsK1d77tLgL0nYVvqPF8MHNcum4D3TSjGFTNPgWHmKZirQzALuXoJA8pTMFfbdXN1ynPVPAWGmacw5lztugXrROCrVXV3VX0fuBQ4s+OYJu1MYEv7fAtwVoexjERVXQ98Z6/NS53nmcCHqvFZ4PAk6ycT6YqZpwPMUzBXB2pwuTrAPAVzFczVPuSqeTrAPIXx52rXBdZTgG8sWN/RbhuqAj6d5KYkm9ptR1XVLoD28cjOohuvpc6zDznQhxhHaZbzFMzVPpnlXO1znkJ/4hwVc5Ve5mofYhylWc5TGGGurnoerBHJItuGPG78yVW1M8mRwNYkf9l1QFOgDznQhxhHyTxdXB/yoA8xjpK5uq++5EBf4hwVc3VffciBPsQ4Subp4g44D7puwdoBHLNg/WhgZ0exjF1V7WwfdwNX0jQ93zvfzNg+7u4uwrFa6jz7kAN9iHFkZjxPwVztjRnP1T7nKfQnzpEwV3ubq32IcWRmPE9hhLnadYH1eeC4JMcmeSzwSuDqjmMaiySPT/KE+efAi4Dbac733Ha3c4Gruolw7JY6z6uBX2xHaDkJ+Jv55tkpYp7OTp6CudoL5mqv8xTMVXO1H7lqns5OnsIoc7WqOl2AlwBfAb4G/Oeu4xnjef4U8MV2uWP+XIEn04xUclf7eETXsY7gXD8K7AL+gabqP2+p86Rpdv3D9u9/GzDXdfxLnJN5OrA8bc/LXO3pMku5OsQ8bWM1V83Vqc9V83R4edqe11hzNe2BkiRJkqQ16rqLoCRJkiQNhgWWJEmSJI2IBZYkSZIkjYgFliRJkiSNiAWWJEmSJI2IBdYYJXlZkkry013HIu2Puaq+MFfVB+ap+sJcHQ8LrPE6B7iBZmI6aZqZq+oLc1V9YJ6qL8zVMbDAGpMkhwEn00xc9sp222OS/FGSO5Jck+STSc5uX3tukj9PclOSP02yvsPwNUPMVfWFuao+ME/VF+bq+Fhgjc9ZwKeq6ivAd5I8B/iXwEbgnwK/DDwPIMkhwH8Dzq6q5wIXA7/dRdCaSeaq+sJcVR+Yp+oLc3VMDu46gAE7B3itcKNSAAABSElEQVR3+/zSdv0Q4GNV9Qjw10mubV9/GvBMYGsSgIOAXZMNVzPMXFVfmKvqA/NUfWGujokF1hgkeTJwKvDMJEWThAVcudQhwB1V9bwJhSgB5qr6w1xVH5in6gtzdbzsIjgeZwMfqqqfrKqNVXUM8FfAt4B/1fZvPQo4pd3/y8C6JD9ohk3yjC4C18wxV9UX5qr6wDxVX5irY2SBNR7nsO83AB8HfgLYAdwOvB+4Efibqvo+TaK/M8kXgVuA508uXM0wc1V9Ya6qD8xT9YW5Okapqq5jmClJDquqB9um2c8BJ1fVX3cdl7Q3c1V9Ya6qD8xT9YW5unbegzV51yQ5HHgs8HYTVlPMXFVfmKvqA/NUfWGurpEtWJIkSZI0It6DJUmSJEkjYoElSZIkSSNigSVJkiRJI2KBJUmSJEkjYoElSZIkSSPy/wGWTk6BpNHf4gAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 864x216 with 5 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the correlation between Fare-bin and Age\n",
+    "grid = sns.FacetGrid(df, col='Fare-bin', size=3, aspect=0.8, sharey=False)\n",
+    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "metadata": {
+    "_cell_guid": "d63fb4e1-0c8d-423d-bf93-5821b08fde51",
+    "_execution_state": "idle",
+    "_uuid": "d8472c4371963ccdc90498b657d3f7f26e9f1e99"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fare-bin</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>27.848315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>27.898241</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>26.144009</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>30.728604</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>35.877913</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Age\n",
+       "Fare-bin           \n",
+       "1         27.848315\n",
+       "2         27.898241\n",
+       "3         26.144009\n",
+       "4         30.728604\n",
+       "5         35.877913"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the mean Age for each Fare-bin\n",
+    "df[['Fare-bin', 'Age']].groupby(['Fare-bin']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {
+    "_cell_guid": "40a7c6d6-c3ae-46f0-baef-352ff8509783",
+    "_execution_state": "idle",
+    "_uuid": "3fb0839aa1bb049ea5998f458ad939272a736345"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Fare-bin</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.812580</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>10.995650</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>13.523208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>17.466022</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>15.150724</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                Age\n",
+       "Fare-bin           \n",
+       "1         10.812580\n",
+       "2         10.995650\n",
+       "3         13.523208\n",
+       "4         17.466022\n",
+       "5         15.150724"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the standard deviation of Age for each Fare-bin\n",
+    "df[['Fare-bin', 'Age']].groupby(['Fare-bin']).std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {
+    "_cell_guid": "37c1feaf-efa8-467a-8fe3-90b8773bde01",
+    "_execution_state": "idle",
+    "_uuid": "cab042b668d7f35dd2b778162835bcb5a0bc1aed"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 691.2x432 with 7 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the correlation between SibSp and Age\n",
+    "grid = sns.FacetGrid(df, col='SibSp', col_wrap=4, size=3.0, aspect=0.8, sharey=False)\n",
+    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "_cell_guid": "ceab6b91-e6bd-4c0f-b7c2-31847b1ee61b",
+    "_execution_state": "idle",
+    "_uuid": "612aec4215b2e4c16806aa768ddd1f565fdfe78b"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SibSp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>30.921766</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>31.058071</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>23.569444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.312500</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8.772727</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>14.500000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age\n",
+       "SibSp           \n",
+       "0      30.921766\n",
+       "1      31.058071\n",
+       "2      23.569444\n",
+       "3      16.312500\n",
+       "4       8.772727\n",
+       "5      10.166667\n",
+       "8      14.500000"
+      ]
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the mean Age for each SibSp\n",
+    "df[['SibSp', 'Age']].groupby(['SibSp']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {
+    "_cell_guid": "eb7a277e-0e3c-4507-ba41-2c9d223f293a",
+    "_execution_state": "idle",
+    "_uuid": "d40475272e30ea20b5eeff47d49a5664a2a8303f"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>SibSp</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>13.059751</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>15.974482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>14.910640</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>11.824938</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>8.005545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>5.192944</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age\n",
+       "SibSp           \n",
+       "0      13.059751\n",
+       "1      15.974482\n",
+       "2      14.910640\n",
+       "3      11.824938\n",
+       "4       8.005545\n",
+       "5       5.192944\n",
+       "8            NaN"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the standard deviation of Age for each SibSp\n",
+    "df[['SibSp', 'Age']].groupby(['SibSp']).std()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {
+    "_cell_guid": "ab9d9000-de9f-4ca0-b817-c53184d74c9a",
+    "_execution_state": "idle",
+    "_uuid": "b2201e9af0c379e0b1bcc0e7a23e08043c241645"
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 691.2x432 with 8 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# visualize the correlation between Parch and Age\n",
+    "grid = sns.FacetGrid(df, col='Parch', col_wrap=4, size=3.0, aspect=0.8, sharey=False)\n",
+    "grid.map(plt.hist, 'Age', alpha=.5, bins=range(0,105,5))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "metadata": {
+    "_cell_guid": "38a4399d-3e36-44ab-8269-a10d6b6aecc8",
+    "_execution_state": "idle",
+    "_uuid": "42db5280471f9bd3acefe047daccfefcd3bf1a00"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>31.969401</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>24.965625</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>18.975979</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>38.875000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>47.600000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>39.333333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>41.500000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age\n",
+       "Parch           \n",
+       "0      31.969401\n",
+       "1      24.965625\n",
+       "2      18.975979\n",
+       "3      38.875000\n",
+       "4      47.600000\n",
+       "5      39.333333\n",
+       "6      41.500000\n",
+       "9            NaN"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the mean Age for each Parch\n",
+    "df[['Parch', 'Age']].groupby(['Parch']).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {
+    "_cell_guid": "4e914c54-23d4-46dc-9a7e-fc9e0e038254",
+    "_execution_state": "idle",
+    "_uuid": "79cb7ffd9ea2ac77fe8bae5d7264986be56604ec"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Parch</th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>12.422617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>18.410885</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>14.564066</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>16.295815</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>14.432602</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>1.032796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2.121320</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             Age\n",
+       "Parch           \n",
+       "0      12.422617\n",
+       "1      18.410885\n",
+       "2      14.564066\n",
+       "3      16.295815\n",
+       "4      14.432602\n",
+       "5       1.032796\n",
+       "6       2.121320\n",
+       "9            NaN"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# inspect the standard deviation of Age for each Parch\n",
+    "df[['Parch', 'Age']].groupby(['Parch']).std() "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {
+    "_cell_guid": "020c9de0-ff1c-4eed-be20-cbff19cac018",
+    "_execution_state": "idle",
+    "_uuid": "9334adf52175fe53abf8138378f4a83efaf77ffb"
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\HP-PC\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:190: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame\n",
+      "\n",
+      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
+      "  self._setitem_with_indexer(indexer, value)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "0"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# notice that instead of using Title, we should use its corresponding dummy variables \n",
+    "df_sub = df[['Age','Master','Miss','Mr','Mrs','Others','Fare-bin','SibSp']]\n",
+    "\n",
+    "X_train  = df_sub.dropna().drop('Age', axis=1)\n",
+    "y_train  = df['Age'].dropna()\n",
+    "X_test = df_sub.loc[np.isnan(df.Age)].drop('Age', axis=1)\n",
+    "\n",
+    "regressor = RandomForestRegressor(n_estimators = 300)\n",
+    "regressor.fit(X_train, y_train)\n",
+    "y_pred = np.round(regressor.predict(X_test),1)\n",
+    "df.Age.loc[df.Age.isnull()] = y_pred\n",
+    "\n",
+    "df.Age.isnull().sum(axis=0) # no more NAN now"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {
+    "_cell_guid": "8a4ce8e1-b212-4d6a-8d8b-ad56bf8acf72",
+    "_execution_state": "idle",
+    "_uuid": "5f9658c201cd82bf031d49803d4045fe847d81e8"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age-bin</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.675000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.452381</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.402597</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.315789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.427083</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.375000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.125000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Age-bin  Survived\n",
+       "0        1  0.675000\n",
+       "1        2  0.452381\n",
+       "2        3  0.402597\n",
+       "3        4  0.315789\n",
+       "4        5  0.427083\n",
+       "5        6  0.375000\n",
+       "6        7  0.125000"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "bins = [ 0, 4, 12, 18, 30, 50, 65, 100] # This is somewhat arbitrary\n",
+    "age_index = (1,2,3,4,5,6,7) #('baby','child','teenager','young','mid-age','over-50','senior')\n",
+    "df['Age-bin'] = pd.cut(df.Age, bins, labels=age_index).astype(int)\n",
+    "\n",
+    "df[['Age-bin', 'Survived']].groupby(['Age-bin'],as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {
+    "_cell_guid": "4f3f0831-83e7-44e4-a69e-5f5fe024e122",
+    "_execution_state": "idle",
+    "_uuid": "a7a0e7795ab628123de85df8cb6c2eac84185165"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.630137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.464481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.239203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.200000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>0.111111</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>A</td>\n",
+       "      <td>0.068966</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.340426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>F</td>\n",
+       "      <td>0.571429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>L</td>\n",
+       "      <td>0.250000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>P</td>\n",
+       "      <td>0.646154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>S</td>\n",
+       "      <td>0.323077</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>W</td>\n",
+       "      <td>0.153846</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Ticket  Survived\n",
+       "0       1  0.630137\n",
+       "1       2  0.464481\n",
+       "2       3  0.239203\n",
+       "3       4  0.200000\n",
+       "4       5  0.000000\n",
+       "5       6  0.166667\n",
+       "6       7  0.111111\n",
+       "7       8  0.000000\n",
+       "8       9  1.000000\n",
+       "9       A  0.068966\n",
+       "10      C  0.340426\n",
+       "11      F  0.571429\n",
+       "12      L  0.250000\n",
+       "13      P  0.646154\n",
+       "14      S  0.323077\n",
+       "15      W  0.153846"
+      ]
+     },
+     "execution_count": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[['Ticket', 'Survived']].groupby(['Ticket'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {
+    "_cell_guid": "7b0bc205-5f92-4ba1-9a52-8c6b2bb91093",
+    "_execution_state": "idle",
+    "_uuid": "1c5a38e61a105b5ad40fc1f159725c30c65e71bc"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3    429\n",
+       "2    278\n",
+       "1    210\n",
+       "P     98\n",
+       "S     98\n",
+       "C     77\n",
+       "A     42\n",
+       "W     19\n",
+       "7     13\n",
+       "F     13\n",
+       "4     11\n",
+       "6      9\n",
+       "L      5\n",
+       "5      3\n",
+       "9      2\n",
+       "8      2\n",
+       "Name: Ticket, dtype: int64"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Ticket'].value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {
+    "_cell_guid": "58fe643e-abf9-4761-bba1-674d3304aba2",
+    "_execution_state": "idle",
+    "_uuid": "7f12fa7d7d6ee803835e5768b9da2a92e1468969"
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Survived</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0.630137</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>0.464481</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>0.239203</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>0.166667</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C</td>\n",
+       "      <td>0.340426</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>P</td>\n",
+       "      <td>0.646154</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>S</td>\n",
+       "      <td>0.323077</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Ticket  Survived\n",
+       "0      1  0.630137\n",
+       "1      2  0.464481\n",
+       "2      3  0.239203\n",
+       "3      4  0.166667\n",
+       "4      C  0.340426\n",
+       "5      P  0.646154\n",
+       "6      S  0.323077"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['Ticket'] = df['Ticket'].replace(['A','W','F','L','5','6','7','8','9'], '4')\n",
+    "\n",
+    "# check the correlation again\n",
+    "df[['Ticket', 'Survived']].groupby(['Ticket'], as_index=False).mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {
+    "_cell_guid": "76238516-9a91-4238-8fc7-abdd64af5435",
+    "_execution_state": "idle",
+    "_uuid": "ac5457d8c3d71e35495d2aeaca2008cabc1b0bae"
+   },
+   "outputs": [],
+   "source": [
+    "# dummy encoding\n",
+    "df = pd.get_dummies(df,columns=['Ticket'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Master</th>\n",
+       "      <th>...</th>\n",
+       "      <th>Family</th>\n",
+       "      <th>Fare-bin</th>\n",
+       "      <th>Age-bin</th>\n",
+       "      <th>Ticket_1</th>\n",
+       "      <th>Ticket_2</th>\n",
+       "      <th>Ticket_3</th>\n",
+       "      <th>Ticket_4</th>\n",
+       "      <th>Ticket_C</th>\n",
+       "      <th>Ticket_P</th>\n",
+       "      <th>Ticket_S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>26.0</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>28.8</td>\n",
+       "      <td>8.4583</td>\n",
+       "      <td>0</td>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>54.0</td>\n",
+       "      <td>51.8625</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>27.0</td>\n",
+       "      <td>11.1333</td>\n",
+       "      <td>2</td>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>30.0708</td>\n",
+       "      <td>0</td>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>16.7000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>58.0</td>\n",
+       "      <td>26.5500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>31.2750</td>\n",
+       "      <td>5</td>\n",
+       "      <td>14</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>14.0</td>\n",
+       "      <td>7.8542</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>55.0</td>\n",
+       "      <td>16.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>2.0</td>\n",
+       "      <td>29.1250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>32.4</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>18.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>34.2</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>35.0</td>\n",
+       "      <td>26.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>34.0</td>\n",
+       "      <td>13.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>15.0</td>\n",
+       "      <td>8.0292</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>35.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>24</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>8.0</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>31.3875</td>\n",
+       "      <td>5</td>\n",
+       "      <td>26</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>29.5</td>\n",
+       "      <td>7.2250</td>\n",
+       "      <td>0</td>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>19.0</td>\n",
+       "      <td>263.0000</td>\n",
+       "      <td>2</td>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>24.5</td>\n",
+       "      <td>7.8792</td>\n",
+       "      <td>0</td>\n",
+       "      <td>29</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>28.8</td>\n",
+       "      <td>7.8958</td>\n",
+       "      <td>0</td>\n",
+       "      <td>30</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1280</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1280</th>\n",
+       "      <td>6.0</td>\n",
+       "      <td>21.0750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1281</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1281</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>93.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1282</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1282</th>\n",
+       "      <td>51.0</td>\n",
+       "      <td>39.4000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1283</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>13.0</td>\n",
+       "      <td>20.2500</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1284</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1284</th>\n",
+       "      <td>47.0</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1285</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1285</th>\n",
+       "      <td>29.0</td>\n",
+       "      <td>22.0250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1286</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1286</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>60.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1287</th>\n",
+       "      <td>24.0</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1288</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1288</th>\n",
+       "      <td>48.0</td>\n",
+       "      <td>79.2000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1289</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1289</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1290</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1290</th>\n",
+       "      <td>31.0</td>\n",
+       "      <td>7.7333</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1291</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1291</th>\n",
+       "      <td>30.0</td>\n",
+       "      <td>164.8667</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1292</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1292</th>\n",
+       "      <td>38.0</td>\n",
+       "      <td>21.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1293</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1293</th>\n",
+       "      <td>22.0</td>\n",
+       "      <td>59.4000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1294</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1294</th>\n",
+       "      <td>17.0</td>\n",
+       "      <td>47.1000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1295</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1295</th>\n",
+       "      <td>43.0</td>\n",
+       "      <td>27.7208</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1296</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1296</th>\n",
+       "      <td>20.0</td>\n",
+       "      <td>13.8625</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1297</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297</th>\n",
+       "      <td>23.0</td>\n",
+       "      <td>10.5000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1298</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1298</th>\n",
+       "      <td>50.0</td>\n",
+       "      <td>211.5000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1299</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>22.9</td>\n",
+       "      <td>7.7208</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1300</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1300</th>\n",
+       "      <td>3.0</td>\n",
+       "      <td>13.7750</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1301</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>22.9</td>\n",
+       "      <td>7.7500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1302</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>37.0</td>\n",
+       "      <td>90.0000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1303</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mrs</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1303</th>\n",
+       "      <td>28.0</td>\n",
+       "      <td>7.7750</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1304</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Miss</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>28.8</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1305</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>39.0</td>\n",
+       "      <td>108.9000</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1306</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Others</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>38.5</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1307</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>28.8</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1308</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Mr</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>3.7</td>\n",
+       "      <td>22.3583</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1309</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Master</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 24 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Age      Fare  Parch  PassengerId  Pclass  Sex  SibSp  Survived  \\\n",
+       "0     22.0    7.2500      0            1       3    0      1       0.0   \n",
+       "1     38.0   71.2833      0            2       1    1      1       1.0   \n",
+       "2     26.0    7.9250      0            3       3    1      0       1.0   \n",
+       "3     35.0   53.1000      0            4       1    1      1       1.0   \n",
+       "4     35.0    8.0500      0            5       3    0      0       0.0   \n",
+       "5     28.8    8.4583      0            6       3    0      0       0.0   \n",
+       "6     54.0   51.8625      0            7       1    0      0       0.0   \n",
+       "7      2.0   21.0750      1            8       3    0      3       0.0   \n",
+       "8     27.0   11.1333      2            9       3    1      0       1.0   \n",
+       "9     14.0   30.0708      0           10       2    1      1       1.0   \n",
+       "10     4.0   16.7000      1           11       3    1      1       1.0   \n",
+       "11    58.0   26.5500      0           12       1    1      0       1.0   \n",
+       "12    20.0    8.0500      0           13       3    0      0       0.0   \n",
+       "13    39.0   31.2750      5           14       3    0      1       0.0   \n",
+       "14    14.0    7.8542      0           15       3    1      0       0.0   \n",
+       "15    55.0   16.0000      0           16       2    1      0       1.0   \n",
+       "16     2.0   29.1250      1           17       3    0      4       0.0   \n",
+       "17    32.4   13.0000      0           18       2    0      0       1.0   \n",
+       "18    31.0   18.0000      0           19       3    1      1       0.0   \n",
+       "19    34.2    7.2250      0           20       3    1      0       1.0   \n",
+       "20    35.0   26.0000      0           21       2    0      0       0.0   \n",
+       "21    34.0   13.0000      0           22       2    0      0       1.0   \n",
+       "22    15.0    8.0292      0           23       3    1      0       1.0   \n",
+       "23    28.0   35.5000      0           24       1    0      0       1.0   \n",
+       "24     8.0   21.0750      1           25       3    1      3       0.0   \n",
+       "25    38.0   31.3875      5           26       3    1      1       1.0   \n",
+       "26    29.5    7.2250      0           27       3    0      0       0.0   \n",
+       "27    19.0  263.0000      2           28       1    0      3       0.0   \n",
+       "28    24.5    7.8792      0           29       3    1      0       1.0   \n",
+       "29    28.8    7.8958      0           30       3    0      0       0.0   \n",
+       "...    ...       ...    ...          ...     ...  ...    ...       ...   \n",
+       "1279  21.0    7.7500      0         1280       3    0      0       NaN   \n",
+       "1280   6.0   21.0750      1         1281       3    0      3       NaN   \n",
+       "1281  23.0   93.5000      0         1282       1    0      0       NaN   \n",
+       "1282  51.0   39.4000      1         1283       1    1      0       NaN   \n",
+       "1283  13.0   20.2500      2         1284       3    0      0       NaN   \n",
+       "1284  47.0   10.5000      0         1285       2    0      0       NaN   \n",
+       "1285  29.0   22.0250      1         1286       3    0      3       NaN   \n",
+       "1286  18.0   60.0000      0         1287       1    1      1       NaN   \n",
+       "1287  24.0    7.2500      0         1288       3    0      0       NaN   \n",
+       "1288  48.0   79.2000      1         1289       1    1      1       NaN   \n",
+       "1289  22.0    7.7750      0         1290       3    0      0       NaN   \n",
+       "1290  31.0    7.7333      0         1291       3    0      0       NaN   \n",
+       "1291  30.0  164.8667      0         1292       1    1      0       NaN   \n",
+       "1292  38.0   21.0000      0         1293       2    0      1       NaN   \n",
+       "1293  22.0   59.4000      1         1294       1    1      0       NaN   \n",
+       "1294  17.0   47.1000      0         1295       1    0      0       NaN   \n",
+       "1295  43.0   27.7208      0         1296       1    0      1       NaN   \n",
+       "1296  20.0   13.8625      0         1297       2    0      0       NaN   \n",
+       "1297  23.0   10.5000      0         1298       2    0      1       NaN   \n",
+       "1298  50.0  211.5000      1         1299       1    0      1       NaN   \n",
+       "1299  22.9    7.7208      0         1300       3    1      0       NaN   \n",
+       "1300   3.0   13.7750      1         1301       3    1      1       NaN   \n",
+       "1301  22.9    7.7500      0         1302       3    1      0       NaN   \n",
+       "1302  37.0   90.0000      0         1303       1    1      1       NaN   \n",
+       "1303  28.0    7.7750      0         1304       3    1      0       NaN   \n",
+       "1304  28.8    8.0500      0         1305       3    0      0       NaN   \n",
+       "1305  39.0  108.9000      0         1306       1    1      0       NaN   \n",
+       "1306  38.5    7.2500      0         1307       3    0      0       NaN   \n",
+       "1307  28.8    8.0500      0         1308       3    0      0       NaN   \n",
+       "1308   3.7   22.3583      1         1309       3    0      1       NaN   \n",
+       "\n",
+       "       Title  Master  ...  Family  Fare-bin  Age-bin  Ticket_1  Ticket_2  \\\n",
+       "0         Mr       0  ...       2         1        4         0         0   \n",
+       "1        Mrs       0  ...       2         5        5         0         0   \n",
+       "2       Miss       0  ...       1         2        4         0         0   \n",
+       "3        Mrs       0  ...       2         5        5         1         0   \n",
+       "4         Mr       0  ...       1         2        5         0         0   \n",
+       "5         Mr       0  ...       1         2        4         0         0   \n",
+       "6         Mr       0  ...       1         5        6         1         0   \n",
+       "7     Master       1  ...       0         3        1         0         0   \n",
+       "8        Mrs       0  ...       3         3        4         0         0   \n",
+       "9        Mrs       0  ...       2         4        3         0         1   \n",
+       "10      Miss       0  ...       3         3        1         0         0   \n",
+       "11      Miss       0  ...       1         4        6         1         0   \n",
+       "12        Mr       0  ...       1         2        4         0         0   \n",
+       "13        Mr       0  ...       0         4        5         0         0   \n",
+       "14      Miss       0  ...       1         1        3         0         0   \n",
+       "15       Mrs       0  ...       1         3        6         0         1   \n",
+       "16    Master       1  ...       0         4        1         0         0   \n",
+       "17        Mr       0  ...       1         3        5         0         1   \n",
+       "18       Mrs       0  ...       2         3        5         0         0   \n",
+       "19       Mrs       0  ...       1         1        5         0         1   \n",
+       "20        Mr       0  ...       1         4        5         0         1   \n",
+       "21        Mr       0  ...       1         3        5         0         1   \n",
+       "22      Miss       0  ...       1         2        3         0         0   \n",
+       "23        Mr       0  ...       1         4        4         1         0   \n",
+       "24      Miss       0  ...       0         3        2         0         0   \n",
+       "25       Mrs       0  ...       0         4        5         0         0   \n",
+       "26        Mr       0  ...       1         1        4         0         1   \n",
+       "27        Mr       0  ...       0         5        4         1         0   \n",
+       "28      Miss       0  ...       1         2        4         0         0   \n",
+       "29        Mr       0  ...       1         2        4         0         0   \n",
+       "...      ...     ...  ...     ...       ...      ...       ...       ...   \n",
+       "1279      Mr       0  ...       1         1        4         0         0   \n",
+       "1280  Master       1  ...       0         3        2         0         0   \n",
+       "1281      Mr       0  ...       1         5        4         1         0   \n",
+       "1282     Mrs       0  ...       2         4        6         0         0   \n",
+       "1283  Master       1  ...       3         3        3         0         0   \n",
+       "1284      Mr       0  ...       1         2        5         0         0   \n",
+       "1285      Mr       0  ...       0         4        4         0         0   \n",
+       "1286     Mrs       0  ...       2         5        3         1         0   \n",
+       "1287      Mr       0  ...       1         1        4         0         0   \n",
+       "1288     Mrs       0  ...       3         5        5         1         0   \n",
+       "1289      Mr       0  ...       1         1        4         0         0   \n",
+       "1290      Mr       0  ...       1         1        5         0         1   \n",
+       "1291    Miss       0  ...       1         5        4         0         0   \n",
+       "1292      Mr       0  ...       2         3        5         0         1   \n",
+       "1293    Miss       0  ...       2         5        4         1         0   \n",
+       "1294      Mr       0  ...       1         5        3         1         0   \n",
+       "1295      Mr       0  ...       2         4        5         1         0   \n",
+       "1296      Mr       0  ...       1         3        4         0         0   \n",
+       "1297      Mr       0  ...       2         2        4         0         1   \n",
+       "1298      Mr       0  ...       3         5        5         1         0   \n",
+       "1299    Miss       0  ...       1         1        4         0         0   \n",
+       "1300    Miss       0  ...       3         3        1         0         0   \n",
+       "1301    Miss       0  ...       1         1        4         0         0   \n",
+       "1302     Mrs       0  ...       2         5        5         1         0   \n",
+       "1303    Miss       0  ...       1         1        4         0         0   \n",
+       "1304      Mr       0  ...       1         2        4         0         0   \n",
+       "1305  Others       0  ...       1         5        5         0         0   \n",
+       "1306      Mr       0  ...       1         1        5         0         0   \n",
+       "1307      Mr       0  ...       1         2        4         0         0   \n",
+       "1308  Master       1  ...       3         4        1         0         1   \n",
+       "\n",
+       "      Ticket_3  Ticket_4  Ticket_C  Ticket_P  Ticket_S  \n",
+       "0            0         1         0         0         0  \n",
+       "1            0         0         0         1         0  \n",
+       "2            0         0         0         0         1  \n",
+       "3            0         0         0         0         0  \n",
+       "4            1         0         0         0         0  \n",
+       "5            1         0         0         0         0  \n",
+       "6            0         0         0         0         0  \n",
+       "7            1         0         0         0         0  \n",
+       "8            1         0         0         0         0  \n",
+       "9            0         0         0         0         0  \n",
+       "10           0         0         0         1         0  \n",
+       "11           0         0         0         0         0  \n",
+       "12           0         1         0         0         0  \n",
+       "13           1         0         0         0         0  \n",
+       "14           1         0         0         0         0  \n",
+       "15           0         0         0         0         0  \n",
+       "16           1         0         0         0         0  \n",
+       "17           0         0         0         0         0  \n",
+       "18           1         0         0         0         0  \n",
+       "19           0         0         0         0         0  \n",
+       "20           0         0         0         0         0  \n",
+       "21           0         0         0         0         0  \n",
+       "22           1         0         0         0         0  \n",
+       "23           0         0         0         0         0  \n",
+       "24           1         0         0         0         0  \n",
+       "25           1         0         0         0         0  \n",
+       "26           0         0         0         0         0  \n",
+       "27           0         0         0         0         0  \n",
+       "28           1         0         0         0         0  \n",
+       "29           1         0         0         0         0  \n",
+       "...        ...       ...       ...       ...       ...  \n",
+       "1279         1         0         0         0         0  \n",
+       "1280         1         0         0         0         0  \n",
+       "1281         0         0         0         0         0  \n",
+       "1282         0         0         0         1         0  \n",
+       "1283         0         0         1         0         0  \n",
+       "1284         0         0         1         0         0  \n",
+       "1285         1         0         0         0         0  \n",
+       "1286         0         0         0         0         0  \n",
+       "1287         1         0         0         0         0  \n",
+       "1288         0         0         0         0         0  \n",
+       "1289         1         0         0         0         0  \n",
+       "1290         0         0         0         0         0  \n",
+       "1291         1         0         0         0         0  \n",
+       "1292         0         0         0         0         0  \n",
+       "1293         0         0         0         0         0  \n",
+       "1294         0         0         0         0         0  \n",
+       "1295         0         0         0         0         0  \n",
+       "1296         0         0         0         0         1  \n",
+       "1297         0         0         0         0         0  \n",
+       "1298         0         0         0         0         0  \n",
+       "1299         1         0         0         0         0  \n",
+       "1300         0         0         0         0         1  \n",
+       "1301         1         0         0         0         0  \n",
+       "1302         0         0         0         0         0  \n",
+       "1303         1         0         0         0         0  \n",
+       "1304         0         1         0         0         0  \n",
+       "1305         0         0         0         1         0  \n",
+       "1306         0         0         0         0         1  \n",
+       "1307         1         0         0         0         0  \n",
+       "1308         0         0         0         0         0  \n",
+       "\n",
+       "[1309 rows x 24 columns]"
+      ]
+     },
+     "execution_count": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {
+    "_cell_guid": "e859e686-72db-4608-a89e-24a9e9b52e47",
+    "_execution_state": "idle",
+    "_uuid": "df4d6e82a6a6fac2c995d55f829085f9afee3267"
+   },
+   "outputs": [],
+   "source": [
+    "df = df.drop(labels=['SibSp','Parch','Age','Fare','Title'], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>PassengerId</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Master</th>\n",
+       "      <th>Miss</th>\n",
+       "      <th>Mr</th>\n",
+       "      <th>Mrs</th>\n",
+       "      <th>Others</th>\n",
+       "      <th>Family</th>\n",
+       "      <th>Fare-bin</th>\n",
+       "      <th>Age-bin</th>\n",
+       "      <th>Ticket_1</th>\n",
+       "      <th>Ticket_2</th>\n",
+       "      <th>Ticket_3</th>\n",
+       "      <th>Ticket_4</th>\n",
+       "      <th>Ticket_C</th>\n",
+       "      <th>Ticket_P</th>\n",
+       "      <th>Ticket_S</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
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+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>13</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>14</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>15</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16</td>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>18</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>19</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>20</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>21</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>22</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>23</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>24</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>25</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>26</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>27</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>29</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>30</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1279</th>\n",
+       "      <td>1280</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1280</th>\n",
+       "      <td>1281</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1281</th>\n",
+       "      <td>1282</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1282</th>\n",
+       "      <td>1283</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>6</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1283</th>\n",
+       "      <td>1284</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1284</th>\n",
+       "      <td>1285</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1285</th>\n",
+       "      <td>1286</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>4</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1286</th>\n",
+       "      <td>1287</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1287</th>\n",
+       "      <td>1288</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1288</th>\n",
+       "      <td>1289</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1289</th>\n",
+       "      <td>1290</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1290</th>\n",
+       "      <td>1291</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1291</th>\n",
+       "      <td>1292</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1292</th>\n",
+       "      <td>1293</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1293</th>\n",
+       "      <td>1294</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1294</th>\n",
+       "      <td>1295</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1295</th>\n",
+       "      <td>1296</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1296</th>\n",
+       "      <td>1297</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1297</th>\n",
+       "      <td>1298</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1298</th>\n",
+       "      <td>1299</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1299</th>\n",
+       "      <td>1300</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1300</th>\n",
+       "      <td>1301</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1301</th>\n",
+       "      <td>1302</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1302</th>\n",
+       "      <td>1303</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>2</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1303</th>\n",
+       "      <td>1304</td>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1304</th>\n",
+       "      <td>1305</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1305</th>\n",
+       "      <td>1306</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1306</th>\n",
+       "      <td>1307</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1307</th>\n",
+       "      <td>1308</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1308</th>\n",
+       "      <td>1309</td>\n",
+       "      <td>3</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1309 rows × 19 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      PassengerId  Pclass  Sex  Survived  Master  Miss  Mr  Mrs  Others  \\\n",
+       "0               1       3    0       0.0       0     0   1    0       0   \n",
+       "1               2       1    1       1.0       0     0   0    1       0   \n",
+       "2               3       3    1       1.0       0     1   0    0       0   \n",
+       "3               4       1    1       1.0       0     0   0    1       0   \n",
+       "4               5       3    0       0.0       0     0   1    0       0   \n",
+       "5               6       3    0       0.0       0     0   1    0       0   \n",
+       "6               7       1    0       0.0       0     0   1    0       0   \n",
+       "7               8       3    0       0.0       1     0   0    0       0   \n",
+       "8               9       3    1       1.0       0     0   0    1       0   \n",
+       "9              10       2    1       1.0       0     0   0    1       0   \n",
+       "10             11       3    1       1.0       0     1   0    0       0   \n",
+       "11             12       1    1       1.0       0     1   0    0       0   \n",
+       "12             13       3    0       0.0       0     0   1    0       0   \n",
+       "13             14       3    0       0.0       0     0   1    0       0   \n",
+       "14             15       3    1       0.0       0     1   0    0       0   \n",
+       "15             16       2    1       1.0       0     0   0    1       0   \n",
+       "16             17       3    0       0.0       1     0   0    0       0   \n",
+       "17             18       2    0       1.0       0     0   1    0       0   \n",
+       "18             19       3    1       0.0       0     0   0    1       0   \n",
+       "19             20       3    1       1.0       0     0   0    1       0   \n",
+       "20             21       2    0       0.0       0     0   1    0       0   \n",
+       "21             22       2    0       1.0       0     0   1    0       0   \n",
+       "22             23       3    1       1.0       0     1   0    0       0   \n",
+       "23             24       1    0       1.0       0     0   1    0       0   \n",
+       "24             25       3    1       0.0       0     1   0    0       0   \n",
+       "25             26       3    1       1.0       0     0   0    1       0   \n",
+       "26             27       3    0       0.0       0     0   1    0       0   \n",
+       "27             28       1    0       0.0       0     0   1    0       0   \n",
+       "28             29       3    1       1.0       0     1   0    0       0   \n",
+       "29             30       3    0       0.0       0     0   1    0       0   \n",
+       "...           ...     ...  ...       ...     ...   ...  ..  ...     ...   \n",
+       "1279         1280       3    0       NaN       0     0   1    0       0   \n",
+       "1280         1281       3    0       NaN       1     0   0    0       0   \n",
+       "1281         1282       1    0       NaN       0     0   1    0       0   \n",
+       "1282         1283       1    1       NaN       0     0   0    1       0   \n",
+       "1283         1284       3    0       NaN       1     0   0    0       0   \n",
+       "1284         1285       2    0       NaN       0     0   1    0       0   \n",
+       "1285         1286       3    0       NaN       0     0   1    0       0   \n",
+       "1286         1287       1    1       NaN       0     0   0    1       0   \n",
+       "1287         1288       3    0       NaN       0     0   1    0       0   \n",
+       "1288         1289       1    1       NaN       0     0   0    1       0   \n",
+       "1289         1290       3    0       NaN       0     0   1    0       0   \n",
+       "1290         1291       3    0       NaN       0     0   1    0       0   \n",
+       "1291         1292       1    1       NaN       0     1   0    0       0   \n",
+       "1292         1293       2    0       NaN       0     0   1    0       0   \n",
+       "1293         1294       1    1       NaN       0     1   0    0       0   \n",
+       "1294         1295       1    0       NaN       0     0   1    0       0   \n",
+       "1295         1296       1    0       NaN       0     0   1    0       0   \n",
+       "1296         1297       2    0       NaN       0     0   1    0       0   \n",
+       "1297         1298       2    0       NaN       0     0   1    0       0   \n",
+       "1298         1299       1    0       NaN       0     0   1    0       0   \n",
+       "1299         1300       3    1       NaN       0     1   0    0       0   \n",
+       "1300         1301       3    1       NaN       0     1   0    0       0   \n",
+       "1301         1302       3    1       NaN       0     1   0    0       0   \n",
+       "1302         1303       1    1       NaN       0     0   0    1       0   \n",
+       "1303         1304       3    1       NaN       0     1   0    0       0   \n",
+       "1304         1305       3    0       NaN       0     0   1    0       0   \n",
+       "1305         1306       1    1       NaN       0     0   0    0       1   \n",
+       "1306         1307       3    0       NaN       0     0   1    0       0   \n",
+       "1307         1308       3    0       NaN       0     0   1    0       0   \n",
+       "1308         1309       3    0       NaN       1     0   0    0       0   \n",
+       "\n",
+       "      Family  Fare-bin  Age-bin  Ticket_1  Ticket_2  Ticket_3  Ticket_4  \\\n",
+       "0          2         1        4         0         0         0         1   \n",
+       "1          2         5        5         0         0         0         0   \n",
+       "2          1         2        4         0         0         0         0   \n",
+       "3          2         5        5         1         0         0         0   \n",
+       "4          1         2        5         0         0         1         0   \n",
+       "5          1         2        4         0         0         1         0   \n",
+       "6          1         5        6         1         0         0         0   \n",
+       "7          0         3        1         0         0         1         0   \n",
+       "8          3         3        4         0         0         1         0   \n",
+       "9          2         4        3         0         1         0         0   \n",
+       "10         3         3        1         0         0         0         0   \n",
+       "11         1         4        6         1         0         0         0   \n",
+       "12         1         2        4         0         0         0         1   \n",
+       "13         0         4        5         0         0         1         0   \n",
+       "14         1         1        3         0         0         1         0   \n",
+       "15         1         3        6         0         1         0         0   \n",
+       "16         0         4        1         0         0         1         0   \n",
+       "17         1         3        5         0         1         0         0   \n",
+       "18         2         3        5         0         0         1         0   \n",
+       "19         1         1        5         0         1         0         0   \n",
+       "20         1         4        5         0         1         0         0   \n",
+       "21         1         3        5         0         1         0         0   \n",
+       "22         1         2        3         0         0         1         0   \n",
+       "23         1         4        4         1         0         0         0   \n",
+       "24         0         3        2         0         0         1         0   \n",
+       "25         0         4        5         0         0         1         0   \n",
+       "26         1         1        4         0         1         0         0   \n",
+       "27         0         5        4         1         0         0         0   \n",
+       "28         1         2        4         0         0         1         0   \n",
+       "29         1         2        4         0         0         1         0   \n",
+       "...      ...       ...      ...       ...       ...       ...       ...   \n",
+       "1279       1         1        4         0         0         1         0   \n",
+       "1280       0         3        2         0         0         1         0   \n",
+       "1281       1         5        4         1         0         0         0   \n",
+       "1282       2         4        6         0         0         0         0   \n",
+       "1283       3         3        3         0         0         0         0   \n",
+       "1284       1         2        5         0         0         0         0   \n",
+       "1285       0         4        4         0         0         1         0   \n",
+       "1286       2         5        3         1         0         0         0   \n",
+       "1287       1         1        4         0         0         1         0   \n",
+       "1288       3         5        5         1         0         0         0   \n",
+       "1289       1         1        4         0         0         1         0   \n",
+       "1290       1         1        5         0         1         0         0   \n",
+       "1291       1         5        4         0         0         1         0   \n",
+       "1292       2         3        5         0         1         0         0   \n",
+       "1293       2         5        4         1         0         0         0   \n",
+       "1294       1         5        3         1         0         0         0   \n",
+       "1295       2         4        5         1         0         0         0   \n",
+       "1296       1         3        4         0         0         0         0   \n",
+       "1297       2         2        4         0         1         0         0   \n",
+       "1298       3         5        5         1         0         0         0   \n",
+       "1299       1         1        4         0         0         1         0   \n",
+       "1300       3         3        1         0         0         0         0   \n",
+       "1301       1         1        4         0         0         1         0   \n",
+       "1302       2         5        5         1         0         0         0   \n",
+       "1303       1         1        4         0         0         1         0   \n",
+       "1304       1         2        4         0         0         0         1   \n",
+       "1305       1         5        5         0         0         0         0   \n",
+       "1306       1         1        5         0         0         0         0   \n",
+       "1307       1         2        4         0         0         1         0   \n",
+       "1308       3         4        1         0         1         0         0   \n",
+       "\n",
+       "      Ticket_C  Ticket_P  Ticket_S  \n",
+       "0            0         0         0  \n",
+       "1            0         1         0  \n",
+       "2            0         0         1  \n",
+       "3            0         0         0  \n",
+       "4            0         0         0  \n",
+       "5            0         0         0  \n",
+       "6            0         0         0  \n",
+       "7            0         0         0  \n",
+       "8            0         0         0  \n",
+       "9            0         0         0  \n",
+       "10           0         1         0  \n",
+       "11           0         0         0  \n",
+       "12           0         0         0  \n",
+       "13           0         0         0  \n",
+       "14           0         0         0  \n",
+       "15           0         0         0  \n",
+       "16           0         0         0  \n",
+       "17           0         0         0  \n",
+       "18           0         0         0  \n",
+       "19           0         0         0  \n",
+       "20           0         0         0  \n",
+       "21           0         0         0  \n",
+       "22           0         0         0  \n",
+       "23           0         0         0  \n",
+       "24           0         0         0  \n",
+       "25           0         0         0  \n",
+       "26           0         0         0  \n",
+       "27           0         0         0  \n",
+       "28           0         0         0  \n",
+       "29           0         0         0  \n",
+       "...        ...       ...       ...  \n",
+       "1279         0         0         0  \n",
+       "1280         0         0         0  \n",
+       "1281         0         0         0  \n",
+       "1282         0         1         0  \n",
+       "1283         1         0         0  \n",
+       "1284         1         0         0  \n",
+       "1285         0         0         0  \n",
+       "1286         0         0         0  \n",
+       "1287         0         0         0  \n",
+       "1288         0         0         0  \n",
+       "1289         0         0         0  \n",
+       "1290         0         0         0  \n",
+       "1291         0         0         0  \n",
+       "1292         0         0         0  \n",
+       "1293         0         0         0  \n",
+       "1294         0         0         0  \n",
+       "1295         0         0         0  \n",
+       "1296         0         0         1  \n",
+       "1297         0         0         0  \n",
+       "1298         0         0         0  \n",
+       "1299         0         0         0  \n",
+       "1300         0         0         1  \n",
+       "1301         0         0         0  \n",
+       "1302         0         0         0  \n",
+       "1303         0         0         0  \n",
+       "1304         0         0         0  \n",
+       "1305         0         1         0  \n",
+       "1306         0         0         1  \n",
+       "1307         0         0         0  \n",
+       "1308         0         0         0  \n",
+       "\n",
+       "[1309 rows x 19 columns]"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1 = df[0:891]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "X = df1[0:891].drop(['Survived','PassengerId'], axis=1).values\n",
+    "Y = df1[0:891]['Survived'].values"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sklearn.model_selection import train_test_split\n",
+    "X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=0.3, random_state = 2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {
+    "_cell_guid": "1b0b8eed-36fc-4d4c-8ef1-1dc96becdc39",
+    "_execution_state": "idle",
+    "_uuid": "1dfd64f73918b8cacdb76f40a0a969c46accaefc"
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "_________________________________________________________________\n",
+      "Layer (type)                 Output Shape              Param #   \n",
+      "=================================================================\n",
+      "dense_1 (Dense)              (None, 16)                288       \n",
+      "_________________________________________________________________\n",
+      "dense_2 (Dense)              (None, 12)                204       \n",
+      "_________________________________________________________________\n",
+      "dense_3 (Dense)              (None, 1)                 13        \n",
+      "=================================================================\n",
+      "Total params: 505\n",
+      "Trainable params: 505\n",
+      "Non-trainable params: 0\n",
+      "_________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Initialising the NN\n",
+    "model = Sequential()\n",
+    "\n",
+    "# layers\n",
+    "model.add(Dense(16, kernel_initializer = 'uniform', activation = 'relu', input_dim = 17))\n",
+    "model.add(Dense(12, kernel_initializer = 'uniform', activation = 'relu'))\n",
+    "model.add(Dense(1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n",
+    "\n",
+    "# summary\n",
+    "model.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Epoch 1/200\n",
+      "623/623 [==============================] - 0s 715us/step - loss: 0.6916 - acc: 0.6340\n",
+      "Epoch 2/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.6862 - acc: 0.6244\n",
+      "Epoch 3/200\n",
+      "623/623 [==============================] - 0s 51us/step - loss: 0.6725 - acc: 0.6276\n",
+      "Epoch 4/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.6409 - acc: 0.6918\n",
+      "Epoch 5/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.5856 - acc: 0.7881\n",
+      "Epoch 6/200\n",
+      "623/623 [==============================] - 0s 51us/step - loss: 0.5237 - acc: 0.8058\n",
+      "Epoch 7/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.4724 - acc: 0.8202\n",
+      "Epoch 8/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.4390 - acc: 0.8331\n",
+      "Epoch 9/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.4260 - acc: 0.8331\n",
+      "Epoch 10/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.4171 - acc: 0.8315\n",
+      "Epoch 11/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.4105 - acc: 0.8363\n",
+      "Epoch 12/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.4068 - acc: 0.8347\n",
+      "Epoch 13/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.4064 - acc: 0.8331\n",
+      "Epoch 14/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.4027 - acc: 0.8379\n",
+      "Epoch 15/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.4018 - acc: 0.8347\n",
+      "Epoch 16/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.4053 - acc: 0.8283\n",
+      "Epoch 17/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.4000 - acc: 0.8266\n",
+      "Epoch 18/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3981 - acc: 0.8331\n",
+      "Epoch 19/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3968 - acc: 0.8411\n",
+      "Epoch 20/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3970 - acc: 0.8395\n",
+      "Epoch 21/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3962 - acc: 0.8427\n",
+      "Epoch 22/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3955 - acc: 0.8395\n",
+      "Epoch 23/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3946 - acc: 0.8443\n",
+      "Epoch 24/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3928 - acc: 0.8411\n",
+      "Epoch 25/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3938 - acc: 0.8523\n",
+      "Epoch 26/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3915 - acc: 0.8427\n",
+      "Epoch 27/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3960 - acc: 0.8347\n",
+      "Epoch 28/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3946 - acc: 0.8411\n",
+      "Epoch 29/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3898 - acc: 0.8427\n",
+      "Epoch 30/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3926 - acc: 0.8427\n",
+      "Epoch 31/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3947 - acc: 0.8459\n",
+      "Epoch 32/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3879 - acc: 0.8555\n",
+      "Epoch 33/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3883 - acc: 0.8475\n",
+      "Epoch 34/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3874 - acc: 0.8459\n",
+      "Epoch 35/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3874 - acc: 0.8443\n",
+      "Epoch 36/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3901 - acc: 0.8491\n",
+      "Epoch 37/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3901 - acc: 0.8347\n",
+      "Epoch 38/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3870 - acc: 0.8491\n",
+      "Epoch 39/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3869 - acc: 0.8459\n",
+      "Epoch 40/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3864 - acc: 0.8491\n",
+      "Epoch 41/200\n",
+      "623/623 [==============================] - 0s 32us/step - loss: 0.3848 - acc: 0.8491\n",
+      "Epoch 42/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3858 - acc: 0.8459\n",
+      "Epoch 43/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3845 - acc: 0.8459\n",
+      "Epoch 44/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3848 - acc: 0.8395\n",
+      "Epoch 45/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3852 - acc: 0.8507\n",
+      "Epoch 46/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3829 - acc: 0.8539\n",
+      "Epoch 47/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3843 - acc: 0.8507\n",
+      "Epoch 48/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3832 - acc: 0.8507\n",
+      "Epoch 49/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3821 - acc: 0.8491\n",
+      "Epoch 50/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3822 - acc: 0.8443\n",
+      "Epoch 51/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3829 - acc: 0.8411\n",
+      "Epoch 52/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3825 - acc: 0.8491\n",
+      "Epoch 53/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3807 - acc: 0.8523\n",
+      "Epoch 54/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3806 - acc: 0.8475\n",
+      "Epoch 55/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3847 - acc: 0.8379\n",
+      "Epoch 56/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3796 - acc: 0.8507\n",
+      "Epoch 57/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3829 - acc: 0.8475\n",
+      "Epoch 58/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3842 - acc: 0.8523\n",
+      "Epoch 59/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3809 - acc: 0.8491\n",
+      "Epoch 60/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3800 - acc: 0.8459\n",
+      "Epoch 61/200\n",
+      "623/623 [==============================] - 0s 32us/step - loss: 0.3798 - acc: 0.8507\n",
+      "Epoch 62/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3793 - acc: 0.8491\n",
+      "Epoch 63/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3792 - acc: 0.8507\n",
+      "Epoch 64/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3786 - acc: 0.8523\n",
+      "Epoch 65/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3789 - acc: 0.8507\n",
+      "Epoch 66/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3785 - acc: 0.8507\n",
+      "Epoch 67/200\n",
+      "623/623 [==============================] - 0s 32us/step - loss: 0.3791 - acc: 0.8507\n",
+      "Epoch 68/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3783 - acc: 0.8523\n",
+      "Epoch 69/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3774 - acc: 0.8523\n",
+      "Epoch 70/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3776 - acc: 0.8507\n",
+      "Epoch 71/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3780 - acc: 0.8523\n",
+      "Epoch 72/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3779 - acc: 0.8491\n",
+      "Epoch 73/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3789 - acc: 0.8411\n",
+      "Epoch 74/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3784 - acc: 0.8539\n",
+      "Epoch 75/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3769 - acc: 0.8523\n",
+      "Epoch 76/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3773 - acc: 0.8523\n",
+      "Epoch 77/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3765 - acc: 0.8539\n",
+      "Epoch 78/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3765 - acc: 0.8459\n",
+      "Epoch 79/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3762 - acc: 0.8523\n",
+      "Epoch 80/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3758 - acc: 0.8555\n",
+      "Epoch 81/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3766 - acc: 0.8507\n",
+      "Epoch 82/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3765 - acc: 0.8523\n",
+      "Epoch 83/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3755 - acc: 0.8523\n",
+      "Epoch 84/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3786 - acc: 0.8443\n",
+      "Epoch 85/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3769 - acc: 0.8491\n",
+      "Epoch 86/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3769 - acc: 0.8539\n",
+      "Epoch 87/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3747 - acc: 0.8523\n",
+      "Epoch 88/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3759 - acc: 0.8523\n",
+      "Epoch 89/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3767 - acc: 0.8411\n",
+      "Epoch 90/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3750 - acc: 0.8523\n",
+      "Epoch 91/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3746 - acc: 0.8523\n",
+      "Epoch 92/200\n",
+      "623/623 [==============================] - 0s 32us/step - loss: 0.3755 - acc: 0.8539\n",
+      "Epoch 93/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3777 - acc: 0.8411\n",
+      "Epoch 94/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3765 - acc: 0.8491\n",
+      "Epoch 95/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3741 - acc: 0.8507\n",
+      "Epoch 96/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3744 - acc: 0.8539\n",
+      "Epoch 97/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3753 - acc: 0.8507\n",
+      "Epoch 98/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3739 - acc: 0.8507\n",
+      "Epoch 99/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3734 - acc: 0.8539\n",
+      "Epoch 100/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3737 - acc: 0.8539\n",
+      "Epoch 101/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3744 - acc: 0.8587\n",
+      "Epoch 102/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3731 - acc: 0.8587\n",
+      "Epoch 103/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3737 - acc: 0.8539\n",
+      "Epoch 104/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3727 - acc: 0.8555\n",
+      "Epoch 105/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3752 - acc: 0.8539\n",
+      "Epoch 106/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3723 - acc: 0.8507\n",
+      "Epoch 107/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3720 - acc: 0.8555\n",
+      "Epoch 108/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3735 - acc: 0.8587\n",
+      "Epoch 109/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3714 - acc: 0.8555\n",
+      "Epoch 110/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3724 - acc: 0.8555\n",
+      "Epoch 111/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3732 - acc: 0.8571\n",
+      "Epoch 112/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3717 - acc: 0.8507\n",
+      "Epoch 113/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3730 - acc: 0.8379\n",
+      "Epoch 114/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3704 - acc: 0.8507\n",
+      "Epoch 115/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3708 - acc: 0.8523\n",
+      "Epoch 116/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3716 - acc: 0.8571\n",
+      "Epoch 117/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3700 - acc: 0.8491\n",
+      "Epoch 118/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3696 - acc: 0.8523\n",
+      "Epoch 119/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3697 - acc: 0.8539\n",
+      "Epoch 120/200\n",
+      "623/623 [==============================] - 0s 32us/step - loss: 0.3697 - acc: 0.8571\n",
+      "Epoch 121/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3689 - acc: 0.8539\n",
+      "Epoch 122/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3697 - acc: 0.8491\n",
+      "Epoch 123/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3688 - acc: 0.8491\n",
+      "Epoch 124/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3701 - acc: 0.8507\n",
+      "Epoch 125/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3681 - acc: 0.8587\n",
+      "Epoch 126/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3690 - acc: 0.8539\n",
+      "Epoch 127/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3678 - acc: 0.8571\n",
+      "Epoch 128/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3670 - acc: 0.8523\n",
+      "Epoch 129/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3678 - acc: 0.8555\n",
+      "Epoch 130/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3681 - acc: 0.8539\n",
+      "Epoch 131/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3686 - acc: 0.8555\n",
+      "Epoch 132/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3672 - acc: 0.8523\n",
+      "Epoch 133/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3669 - acc: 0.8555\n",
+      "Epoch 134/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3700 - acc: 0.8395\n",
+      "Epoch 135/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3681 - acc: 0.8539\n",
+      "Epoch 136/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3662 - acc: 0.8539\n",
+      "Epoch 137/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3676 - acc: 0.8523\n",
+      "Epoch 138/200\n",
+      "623/623 [==============================] - 0s 32us/step - loss: 0.3672 - acc: 0.8443\n",
+      "Epoch 139/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3650 - acc: 0.8555\n",
+      "Epoch 140/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3650 - acc: 0.8571\n",
+      "Epoch 141/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3651 - acc: 0.8555\n",
+      "Epoch 142/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3647 - acc: 0.8555\n",
+      "Epoch 143/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3677 - acc: 0.8571\n",
+      "Epoch 144/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3635 - acc: 0.8555\n",
+      "Epoch 145/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3631 - acc: 0.8587\n",
+      "Epoch 146/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3645 - acc: 0.8539\n",
+      "Epoch 147/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3625 - acc: 0.8555\n",
+      "Epoch 148/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3633 - acc: 0.8571\n",
+      "Epoch 149/200\n",
+      "623/623 [==============================] - 0s 32us/step - loss: 0.3628 - acc: 0.8491\n",
+      "Epoch 150/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3687 - acc: 0.8507\n",
+      "Epoch 151/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3627 - acc: 0.8491\n",
+      "Epoch 152/200\n",
+      "623/623 [==============================] - 0s 32us/step - loss: 0.3609 - acc: 0.8571\n",
+      "Epoch 153/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3610 - acc: 0.8571\n",
+      "Epoch 154/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3622 - acc: 0.8539\n",
+      "Epoch 155/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3616 - acc: 0.8571\n",
+      "Epoch 156/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3625 - acc: 0.8571\n",
+      "Epoch 157/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3601 - acc: 0.8555\n",
+      "Epoch 158/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3595 - acc: 0.8571\n",
+      "Epoch 159/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3592 - acc: 0.8587\n",
+      "Epoch 160/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3591 - acc: 0.8587\n",
+      "Epoch 161/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3595 - acc: 0.8571\n",
+      "Epoch 162/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3590 - acc: 0.8539\n",
+      "Epoch 163/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3583 - acc: 0.8571\n",
+      "Epoch 164/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3591 - acc: 0.8555\n",
+      "Epoch 165/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3593 - acc: 0.8571\n",
+      "Epoch 166/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3596 - acc: 0.8507\n",
+      "Epoch 167/200\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3583 - acc: 0.8587\n",
+      "Epoch 168/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3577 - acc: 0.8555\n",
+      "Epoch 169/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3575 - acc: 0.8604\n",
+      "Epoch 170/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3569 - acc: 0.8587\n",
+      "Epoch 171/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3571 - acc: 0.8587\n",
+      "Epoch 172/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3591 - acc: 0.8604\n",
+      "Epoch 173/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3560 - acc: 0.8539\n",
+      "Epoch 174/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3571 - acc: 0.8571\n",
+      "Epoch 175/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3558 - acc: 0.8571\n",
+      "Epoch 176/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3551 - acc: 0.8571\n",
+      "Epoch 177/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3596 - acc: 0.8475\n",
+      "Epoch 178/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3596 - acc: 0.8587\n",
+      "Epoch 179/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3555 - acc: 0.8555\n",
+      "Epoch 180/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3551 - acc: 0.8604\n",
+      "Epoch 181/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3536 - acc: 0.8636\n",
+      "Epoch 182/200\n",
+      "623/623 [==============================] - 0s 32us/step - loss: 0.3542 - acc: 0.8555\n",
+      "Epoch 183/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3546 - acc: 0.8604\n",
+      "Epoch 184/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3541 - acc: 0.8571\n",
+      "Epoch 185/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3532 - acc: 0.8555\n",
+      "Epoch 186/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3540 - acc: 0.8523\n",
+      "Epoch 187/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3540 - acc: 0.8620\n",
+      "Epoch 188/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3530 - acc: 0.8571\n",
+      "Epoch 189/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3530 - acc: 0.8587\n",
+      "Epoch 190/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3531 - acc: 0.8571\n",
+      "Epoch 191/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3523 - acc: 0.8587\n",
+      "Epoch 192/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3521 - acc: 0.8604\n",
+      "Epoch 193/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3528 - acc: 0.8604\n",
+      "Epoch 194/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3525 - acc: 0.8555\n",
+      "Epoch 195/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3511 - acc: 0.8620\n",
+      "Epoch 196/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3515 - acc: 0.8555\n",
+      "Epoch 197/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3506 - acc: 0.8604\n",
+      "Epoch 198/200\n",
+      "623/623 [==============================] - 0s 45us/step - loss: 0.3514 - acc: 0.8604\n",
+      "Epoch 199/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3500 - acc: 0.8539\n",
+      "Epoch 200/200\n",
+      "623/623 [==============================] - 0s 39us/step - loss: 0.3526 - acc: 0.8571\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<keras.callbacks.History at 0x6f1f3dc50>"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compiling the NN\n",
+    "model.compile(optimizer = 'adam', loss = 'binary_crossentropy', metrics = ['accuracy'])\n",
+    "\n",
+    "# Train the NN\n",
+    "model.fit(X_train, y_train, batch_size = 32, epochs = 200)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "_cell_guid": "ae0b0267-706b-4673-a5e1-5821edc2a9e4",
+    "_execution_state": "idle",
+    "_uuid": "8f65307ece4155513982147fc7bbc497074af201"
+   },
+   "source": [
+    "We can now produce the prediction."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {
+    "_cell_guid": "4a651081-886a-4a9a-8324-a264483d0721",
+    "_execution_state": "idle",
+    "_uuid": "7a6c2f2885d7caf67993db99f6880d53e5880372"
+   },
+   "outputs": [],
+   "source": [
+    "y_pred = model.predict(X_test)\n",
+    "y_final = (y_pred > 0.5).astype(int).reshape(X_test.shape[0])\n",
+    "\n",
+    "#output = pd.DataFrame({'PassengerId': df_test['PassengerId'], 'Survived': y_final})\n",
+    "#output.to_csv('prediction.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Actual</th>\n",
+       "      <th>Predicted</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>238</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>239</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>241</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>242</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>244</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>245</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>246</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>247</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>248</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>249</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>250</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>251</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>252</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>253</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>254</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>255</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>256</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>257</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>258</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>259</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>260</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>261</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>262</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>263</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>264</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>265</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>266</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>267</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>268 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Actual  Predicted\n",
+       "0       1.0          0\n",
+       "1       0.0          0\n",
+       "2       1.0          1\n",
+       "3       0.0          0\n",
+       "4       1.0          0\n",
+       "5       0.0          0\n",
+       "6       1.0          0\n",
+       "7       0.0          0\n",
+       "8       1.0          1\n",
+       "9       0.0          0\n",
+       "10      0.0          0\n",
+       "11      1.0          1\n",
+       "12      1.0          1\n",
+       "13      1.0          0\n",
+       "14      1.0          0\n",
+       "15      1.0          1\n",
+       "16      1.0          1\n",
+       "17      0.0          0\n",
+       "18      0.0          1\n",
+       "19      0.0          0\n",
+       "20      0.0          0\n",
+       "21      1.0          1\n",
+       "22      0.0          0\n",
+       "23      0.0          0\n",
+       "24      0.0          0\n",
+       "25      1.0          1\n",
+       "26      1.0          1\n",
+       "27      0.0          0\n",
+       "28      0.0          0\n",
+       "29      0.0          0\n",
+       "..      ...        ...\n",
+       "238     0.0          0\n",
+       "239     1.0          1\n",
+       "240     0.0          0\n",
+       "241     0.0          0\n",
+       "242     1.0          0\n",
+       "243     1.0          0\n",
+       "244     0.0          0\n",
+       "245     0.0          0\n",
+       "246     1.0          0\n",
+       "247     0.0          1\n",
+       "248     0.0          0\n",
+       "249     1.0          1\n",
+       "250     0.0          0\n",
+       "251     0.0          0\n",
+       "252     1.0          1\n",
+       "253     0.0          0\n",
+       "254     1.0          0\n",
+       "255     0.0          0\n",
+       "256     0.0          0\n",
+       "257     1.0          0\n",
+       "258     0.0          0\n",
+       "259     1.0          1\n",
+       "260     0.0          0\n",
+       "261     0.0          0\n",
+       "262     0.0          0\n",
+       "263     1.0          1\n",
+       "264     0.0          0\n",
+       "265     0.0          0\n",
+       "266     0.0          0\n",
+       "267     0.0          1\n",
+       "\n",
+       "[268 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df12345 = pd.DataFrame({'Actual': y_test.flatten(), 'Predicted': y_final.flatten()})\n",
+    "df12345"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.8059701492537313\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import accuracy_score\n",
+    "Accuracy = accuracy_score(y_test,y_final)\n",
+    "print(\"Accuracy:\", Accuracy)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "              precision    recall  f1-score   support\n",
+      "\n",
+      "         0.0       0.79      0.91      0.85       160\n",
+      "         1.0       0.83      0.65      0.73       108\n",
+      "\n",
+      "   micro avg       0.81      0.81      0.81       268\n",
+      "   macro avg       0.81      0.78      0.79       268\n",
+      "weighted avg       0.81      0.81      0.80       268\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import classification_report\n",
+    "C_report = classification_report(y_test,y_final)\n",
+    "print(C_report)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[146  14]\n",
+      " [ 38  70]]\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import confusion_matrix\n",
+    "print(confusion_matrix(y_test,y_final))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cm = confusion_matrix(y_test,y_final)\n",
+    "cm_df = pd.DataFrame(cm,\n",
+    "                     index = ['survived','Not'], \n",
+    "                     columns = ['survived','Not'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 396x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.8059701492537313\n"
+     ]
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(5.5,4))\n",
+    "sns.heatmap(cm_df, annot=True, fmt=\"d\")\n",
+    "plt.title('Neural network'.format(accuracy_score(y_test, y_final)))\n",
+    "plt.ylabel('True label')\n",
+    "plt.xlabel('Predicted label')\n",
+    "plt.savefig('plot_NN.png', dpi=500, bbox_inches='tight')\n",
+    "plt.show()\n",
+    "print(\"Accuracy:\",accuracy_score(y_test, y_final))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.5816522574447647"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from sklearn.metrics import cohen_kappa_score\n",
+    "cohen_kappa_score(y_test, y_final)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}

From b323c5bfe647c44fb1c8fc640ba154904b5bda0a Mon Sep 17 00:00:00 2001
From: sankalpsaoji123 <53940684+sankalpsaoji123@users.noreply.github.com>
Date: Mon, 19 Aug 2019 13:15:25 +0100
Subject: [PATCH 2/4] Add files via upload


From 10bb73a834f9ef53c5d0ec67815cde2bfebf222a Mon Sep 17 00:00:00 2001
From: sankalpsaoji123 <53940684+sankalpsaoji123@users.noreply.github.com>
Date: Mon, 19 Aug 2019 13:15:45 +0100
Subject: [PATCH 3/4] Delete Titanic_report.pdf

---
 Titanic_report.pdf | Bin 616492 -> 0 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 Titanic_report.pdf

diff --git a/Titanic_report.pdf b/Titanic_report.pdf
deleted file mode 100644
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From 7896f74eec32d2d5173be3285cb69a06b9178f2b Mon Sep 17 00:00:00 2001
From: sankalpsaoji123 <53940684+sankalpsaoji123@users.noreply.github.com>
Date: Mon, 19 Aug 2019 13:16:01 +0100
Subject: [PATCH 4/4] Add files via upload

---
 Titanic_report.pdf | Bin 0 -> 616492 bytes
 1 file changed, 0 insertions(+), 0 deletions(-)
 create mode 100644 Titanic_report.pdf

diff --git a/Titanic_report.pdf b/Titanic_report.pdf
new file mode 100644
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