PCA Principle Component.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORT TẤT CẢ THƯ VIỆN PHÂN LOẠI\n",
    "# data analysis and wrangling\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import random as rnd\n",
    "\n",
    "# visualization\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# machine learning\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.svm import SVC, LinearSVC\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.linear_model import Perceptron\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "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",
       "      <td>0</td>\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",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\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",
       "      <td>4</td>\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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(r'C:\\HỌC TẬP\\PYTHON LEARNING\\DATA\\titanic_train.csv')\n",
    "data = pd.DataFrame(data)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Sex_female</th>\n",
       "      <th>Sex_male</th>\n",
       "      <th>Embarked_C</th>\n",
       "      <th>Embarked_Q</th>\n",
       "      <th>Embarked_S</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass   Age  SibSp  Parch     Fare  Sex_female  \\\n",
       "0            1         0       3  22.0      1      0   7.2500           0   \n",
       "1            2         1       1  38.0      1      0  71.2833           1   \n",
       "2            3         1       3  26.0      0      0   7.9250           1   \n",
       "3            4         1       1  35.0      1      0  53.1000           1   \n",
       "4            5         0       3  35.0      0      0   8.0500           0   \n",
       "\n",
       "   Sex_male  Embarked_C  Embarked_Q  Embarked_S  \n",
       "0         1           0           0           1  \n",
       "1         0           1           0           0  \n",
       "2         0           0           0           1  \n",
       "3         0           0           0           1  \n",
       "4         1           0           0           1  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop(['Name','Ticket','Cabin'],axis = 1)\n",
    "data = pd.get_dummies(data)\n",
    "data = data.dropna()\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Principal Components Analysis (PCA)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = data.drop('Survived',axis = 1)\n",
    "y = data['Survived']\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "# Tiêu chuẩn hóa các Component\n",
    "x = StandardScaler().fit_transform(x)\n",
    "# Phân tách và kiểm định các Component\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(x)\n",
    "principalDf = pd.DataFrame(data = principalComponents, columns = ['PC1', 'PC2'])\n",
    "finalDf = pd.concat([principalDf, data[['Survived']]], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 component PCA', fontsize = 20)\n",
    "targets = [1,0]\n",
    "colors = ['r', 'b']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = finalDf['Survived'] == target\n",
    "    ax.scatter(finalDf.loc[indicesToKeep, 'PC1']\n",
    "               , finalDf.loc[indicesToKeep, 'PC2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  }
 ],
 "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}