Titanic Survival.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Importing the Dependencies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np #To make NumPy arrays\n",
    "import pandas as pd #To create a dataframe(structured table)\n",
    "import matplotlib.pyplot as plt #For plots and graphs\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split #To split our data into training and testing data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import accuracy_score #To evaluate our model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Collection & Processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the data from csv file to Pandas DataFrame\n",
    "titanic_data = pd.read_csv('D:\\ML_Projects\\Titanic Survival/train.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing the first 5 rows of the dataframe\n",
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(891, 12)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of rows and Columns\n",
    "titanic_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 891 entries, 0 to 890\n",
      "Data columns (total 12 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   PassengerId  891 non-null    int64  \n",
      " 1   Survived     891 non-null    int64  \n",
      " 2   Pclass       891 non-null    int64  \n",
      " 3   Name         891 non-null    object \n",
      " 4   Sex          891 non-null    object \n",
      " 5   Age          714 non-null    float64\n",
      " 6   SibSp        891 non-null    int64  \n",
      " 7   Parch        891 non-null    int64  \n",
      " 8   Ticket       891 non-null    object \n",
      " 9   Fare         891 non-null    float64\n",
      " 10  Cabin        204 non-null    object \n",
      " 11  Embarked     889 non-null    object \n",
      "dtypes: float64(2), int64(5), object(5)\n",
      "memory usage: 83.7+ KB\n"
     ]
    }
   ],
   "source": [
    "# Getting informations about the data\n",
    "titanic_data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId      0\n",
       "Survived         0\n",
       "Pclass           0\n",
       "Name             0\n",
       "Sex              0\n",
       "Age            177\n",
       "SibSp            0\n",
       "Parch            0\n",
       "Ticket           0\n",
       "Fare             0\n",
       "Cabin          687\n",
       "Embarked         2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the number of missing values in each column\n",
    "titanic_data.isnull().sum() #We find that for Cabin colm, most of the values are missing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling the Missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the \"Cabin\" column from the dataframe\n",
    "titanic_data = titanic_data.drop(columns='Cabin', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the missing values in \"Age\" column with mean value\n",
    "titanic_data['Age'].fillna(titanic_data['Age'].mean(), inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    S\n",
      "dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Finding the mode value of \"Embarked\" column\n",
    "print(titanic_data['Embarked'].mode())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "S\n"
     ]
    }
   ],
   "source": [
    "print(titanic_data['Embarked'].mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Replacing the missing values in \"Embarked\" column with mode value\n",
    "titanic_data['Embarked'].fillna(titanic_data['Embarked'].mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PassengerId    0\n",
       "Survived       0\n",
       "Pclass         0\n",
       "Name           0\n",
       "Sex            0\n",
       "Age            0\n",
       "SibSp          0\n",
       "Parch          0\n",
       "Ticket         0\n",
       "Fare           0\n",
       "Embarked       0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the number of missing values in each column\n",
    "titanic_data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "      <td>891.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.383838</td>\n",
       "      <td>2.308642</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.523008</td>\n",
       "      <td>0.381594</td>\n",
       "      <td>32.204208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>257.353842</td>\n",
       "      <td>0.486592</td>\n",
       "      <td>0.836071</td>\n",
       "      <td>13.002015</td>\n",
       "      <td>1.102743</td>\n",
       "      <td>0.806057</td>\n",
       "      <td>49.693429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.420000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>223.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>7.910400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>446.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>29.699118</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.454200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>668.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>891.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>512.329200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
       "count   891.000000  891.000000  891.000000  891.000000  891.000000   \n",
       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
       "std     257.353842    0.486592    0.836071   13.002015    1.102743   \n",
       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
       "25%     223.500000    0.000000    2.000000   22.000000    0.000000   \n",
       "50%     446.000000    0.000000    3.000000   29.699118    0.000000   \n",
       "75%     668.500000    1.000000    3.000000   35.000000    1.000000   \n",
       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
       "\n",
       "            Parch        Fare  \n",
       "count  891.000000  891.000000  \n",
       "mean     0.381594   32.204208  \n",
       "std      0.806057   49.693429  \n",
       "min      0.000000    0.000000  \n",
       "25%      0.000000    7.910400  \n",
       "50%      0.000000   14.454200  \n",
       "75%      0.000000   31.000000  \n",
       "max      6.000000  512.329200  "
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting some statistical measures about the data\n",
    "titanic_data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the number of people survived and not survived\n",
    "titanic_data['Survived'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d21f724788>"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a count plot for \"Survived\" column\n",
    "sns.countplot('Survived', data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the number of males and females\n",
    "titanic_data['Sex'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d21f77e4c8>"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a count plot for \"Sex\" column\n",
    "sns.countplot('Sex', data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d21f7d4c88>"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of survivors Gender wise\n",
    "sns.countplot('Sex', hue='Survived', data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d21f835688>"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a count plot for \"Pclass\" column\n",
    "sns.countplot('Pclass', data=titanic_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1d21f890a48>"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Number of survivors Pclass wise\n",
    "sns.countplot('Pclass', hue='Survived', data=titanic_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encoding the Categorical Columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "male      577\n",
       "female    314\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data['Sex'].value_counts() #To know how many diff. values are there for Sex colm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "S    646\n",
       "C    168\n",
       "Q     77\n",
       "Name: Embarked, dtype: int64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data['Embarked'].value_counts() #To know how many diff. values are there for Embarked colm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting categorical Columns\n",
    "titanic_data.replace({'Sex':{'male':0,'female':1}, 'Embarked':{'S':0,'C':1,'Q':2}}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</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>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name  Sex   Age  SibSp  Parch  \\\n",
       "0                            Braund, Mr. Owen Harris    0  22.0      1      0   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...    1  38.0      1      0   \n",
       "2                             Heikkinen, Miss. Laina    1  26.0      0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)    1  35.0      1      0   \n",
       "4                           Allen, Mr. William Henry    0  35.0      0      0   \n",
       "\n",
       "             Ticket     Fare  Embarked  \n",
       "0         A/5 21171   7.2500         0  \n",
       "1          PC 17599  71.2833         1  \n",
       "2  STON/O2. 3101282   7.9250         0  \n",
       "3            113803  53.1000         0  \n",
       "4            373450   8.0500         0  "
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Separating features & Target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = titanic_data.drop(columns = ['PassengerId','Name','Ticket','Survived'],axis=1)\n",
    "Y = titanic_data['Survived']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Pclass  Sex        Age  SibSp  Parch     Fare  Embarked\n",
      "0         3    0  22.000000      1      0   7.2500         0\n",
      "1         1    1  38.000000      1      0  71.2833         1\n",
      "2         3    1  26.000000      0      0   7.9250         0\n",
      "3         1    1  35.000000      1      0  53.1000         0\n",
      "4         3    0  35.000000      0      0   8.0500         0\n",
      "..      ...  ...        ...    ...    ...      ...       ...\n",
      "886       2    0  27.000000      0      0  13.0000         0\n",
      "887       1    1  19.000000      0      0  30.0000         0\n",
      "888       3    1  29.699118      1      2  23.4500         0\n",
      "889       1    0  26.000000      0      0  30.0000         1\n",
      "890       3    0  32.000000      0      0   7.7500         2\n",
      "\n",
      "[891 rows x 7 columns]\n"
     ]
    }
   ],
   "source": [
    "print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      0\n",
      "1      1\n",
      "2      1\n",
      "3      1\n",
      "4      0\n",
      "      ..\n",
      "886    0\n",
      "887    1\n",
      "888    0\n",
      "889    1\n",
      "890    0\n",
      "Name: Survived, Length: 891, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(Y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Splitting the data into training data & Test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, Y_train, Y_test = train_test_split(X,Y, test_size=0.2, random_state=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(891, 7) (712, 7) (179, 7)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, X_train.shape, X_test.shape) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Training"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Akanksha Priya\\anaconda3\\lib\\site-packages\\sklearn\\linear_model\\_logistic.py:940: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n",
       "                   multi_class='auto', n_jobs=None, penalty='l2',\n",
       "                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n",
       "                   warm_start=False)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the Logistic Regression model with training data\n",
    "model.fit(X_train, Y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model Evaluation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy Score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy on training data\n",
    "X_train_prediction = model.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 1 0 0 0 0 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 0 0 1 0 1\n",
      " 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1 0 0 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 0 1 0 0 1\n",
      " 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 0 1 0 0 0\n",
      " 1 1 0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 1 0 1 0 1 1 1 1 1 1 0 0 1 1 1 0 0 1 0 0\n",
      " 0 0 0 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 0 1 0 1 1 1\n",
      " 0 0 0 1 0 0 0 1 0 0 1 0 0 0 1 1 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 0\n",
      " 0 1 0 0 1 1 1 0 0 1 0 1 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 1 0 0 0\n",
      " 0 0 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 0\n",
      " 0 1 1 0 0 0 0 0 0 1 0 1 0 0 0 0 0 1 1 1 0 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 1\n",
      " 0 1 1 1 0 0 0 0 0 0 0 0 0 1 0 0 1 1 1 0 1 0 0 0 0 1 1 0 0 0 1 0 1 1 1 0 0\n",
      " 0 0 1 0 0 0 1 1 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 0 0 1 0 1 1 1 0 1 1 0 0 0\n",
      " 0 1 0 1 0 0 1 1 0 0 0 0 1 0 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 1 1 0 0\n",
      " 1 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 1 1 0 0 0 1 1 0 1 0 0 1 0 0 0 1 1 0 1 0\n",
      " 0 0 0 0 1 0 0 1 0 1 1 0 0 1 0 0 1 0 0 0 1 0 1 1 0 0 1 1 0 1 0 1 1 1 0 1 0\n",
      " 0 1 0 0 1 0 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 0 0 0\n",
      " 0 0 1 0 0 0 0 0 1 0 1 0 1 0 0 0 1 0 1 1 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 1 1\n",
      " 0 0 0 1 0 1 0 0 0 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 1 0 0 1 0 0 0 0\n",
      " 1 0 0 1 0 1 0 0 0 1 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0\n",
      " 0 0 0 1 1 0 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "print(X_train_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of training data :  0.8075842696629213\n"
     ]
    }
   ],
   "source": [
    "training_data_accuracy = accuracy_score(Y_train, X_train_prediction)\n",
    "print('Accuracy score of training data : ', training_data_accuracy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Accuracy on test data\n",
    "X_test_prediction = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 1 0 0 1 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 0 1 1\n",
      " 0 0 0 0 0 1 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 1 0\n",
      " 1 0 0 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 1 0 1 1 0 1 1 0 0 0 0\n",
      " 0 0 0 1 1 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 1 0 1 0 0\n",
      " 0 1 0 0 0 0 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 0 0 1 1 1 0 0 0 0 0]\n"
     ]
    }
   ],
   "source": [
    "print(X_test_prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy score of test data :  0.7821229050279329\n"
     ]
    }
   ],
   "source": [
    "test_data_accuracy = accuracy_score(Y_test, X_test_prediction)\n",
    "print('Accuracy score of test data : ', test_data_accuracy)"
   ]
  }
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