04_Feature_Selection_and_Extraction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/xgboost/compat.py:93: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n",
      "/opt/anaconda3/lib/python3.8/site-packages/dask/dataframe/utils.py:366: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/dask/dataframe/utils.py:366: FutureWarning: pandas.Float64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n",
      "/opt/anaconda3/lib/python3.8/site-packages/dask/dataframe/utils.py:366: FutureWarning: pandas.UInt64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  _numeric_index_types = (pd.Int64Index, pd.Float64Index, pd.UInt64Index)\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score, roc_auc_score\n",
    "from xgboost import XGBClassifier\n",
    "from xgboost import plot_importance\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import f_classif, mutual_info_classif\n",
    "from numpy import sort\n",
    "from sklearn.feature_selection import SelectFromModel\n",
    "\n",
    "\n",
    "# Comment this if the data visualisations doesn't work on your side\n",
    "%matplotlib inline\n",
    "\n",
    "plt.style.use('bmh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30139, 15)\n"
     ]
    }
   ],
   "source": [
    "df=pd.read_csv('adult_income.csv')\n",
    "df.drop_duplicates(keep='first',inplace=True)\n",
    "df.drop(index=df[(df[' Work-class']==' ?')|(df[' Occupation']==' ?') |(df[' Native-country']==' ?')].index,inplace=True)\n",
    "df['income']=df[' Income'].map(lambda x: 0 if x ==' <=50K' else 1)\n",
    "df.drop(\" Income\",axis=1,inplace=True)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# One-hot Encoding for categorical data (native-country 0 for US,1 for others)\n",
    "df=pd.get_dummies(df, columns=[' Work-class'],drop_first=False)\n",
    "df=pd.get_dummies(df, columns=[' Marital-status'],drop_first=False)\n",
    "df=pd.get_dummies(df, columns=[' Occupation'],drop_first=False)\n",
    "df=pd.get_dummies(df, columns=[' Relationship'],drop_first=False)\n",
    "df=pd.get_dummies(df, columns=[' Race'],drop_first=False)\n",
    "df=pd.get_dummies(df, columns=[' Sex'],drop_first=False)\n",
    "df[' Native-country']=df[' Native-country'].map(lambda x: 0 if x ==' United-States' else 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Eduction and Eduction_num are essentially duplicate\n",
    "df.drop(' Education', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(30139, 49)\n",
      "(30139, 48)\n"
     ]
    }
   ],
   "source": [
    "print(df.shape)\n",
    "df['Capital'] = df[' Capital-gain'] + df[' Capital-loss']\n",
    "df.drop(' Capital-gain', axis=1, inplace=True)\n",
    "df.drop(' Capital-loss', axis=1, inplace=True)\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler().fit(df[[' fnlwgt','Capital',' Hours-per-week']])\n",
    "df[[' fnlwgt','Capital',' Hours-per-week']] = scaler.transform(df[[' fnlwgt','Capital',' Hours-per-week']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30139, 48)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataX=df.drop('income',axis=1)\n",
    "dataY=df['income']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "importance_f=f_classif(dataX, dataY) \n",
    "feature_importance=pd.DataFrame(importance_f,columns=dataX.columns,index=['f-statistics','p-value']).T.sort_values(by='f-statistics',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_importance=feature_importance.reset_index().rename(columns={'index':'feature'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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>feature</th>\n",
       "      <th>f-statistics</th>\n",
       "      <th>p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Marital-status_ Married-civ-spouse</td>\n",
       "      <td>7455.209</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Relationship_ Husband</td>\n",
       "      <td>5779.445</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Education-num</td>\n",
       "      <td>3820.265</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Marital-status_ Never-married</td>\n",
       "      <td>3435.551</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Age</td>\n",
       "      <td>1875.009</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Capital</td>\n",
       "      <td>1674.887</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Hours-per-week</td>\n",
       "      <td>1674.573</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Relationship_ Own-child</td>\n",
       "      <td>1625.722</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Sex_ Female</td>\n",
       "      <td>1484.467</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Sex_ Male</td>\n",
       "      <td>1484.467</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Occupation_ Exec-managerial</td>\n",
       "      <td>1438.858</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Relationship_ Not-in-family</td>\n",
       "      <td>1166.834</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Occupation_ Prof-specialty</td>\n",
       "      <td>1026.260</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Occupation_ Other-service</td>\n",
       "      <td>853.548</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Relationship_ Unmarried</td>\n",
       "      <td>655.419</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Work-class_ Self-emp-inc</td>\n",
       "      <td>581.814</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Marital-status_ Divorced</td>\n",
       "      <td>535.222</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Relationship_ Wife</td>\n",
       "      <td>479.078</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Work-class_ Private</td>\n",
       "      <td>419.267</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Occupation_ Adm-clerical</td>\n",
       "      <td>303.740</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Occupation_ Handlers-cleaners</td>\n",
       "      <td>267.862</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Race_ Black</td>\n",
       "      <td>237.332</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Race_ White</td>\n",
       "      <td>218.188</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Relationship_ Other-relative</td>\n",
       "      <td>216.867</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Occupation_ Machine-op-inspct</td>\n",
       "      <td>175.999</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Marital-status_ Separated</td>\n",
       "      <td>166.501</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Marital-status_ Widowed</td>\n",
       "      <td>105.840</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Work-class_ Federal-gov</td>\n",
       "      <td>99.469</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Occupation_ Farming-fishing</td>\n",
       "      <td>96.134</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Marital-status_ Married-spouse-absent</td>\n",
       "      <td>54.800</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Native-country</td>\n",
       "      <td>48.151</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Occupation_ Priv-house-serv</td>\n",
       "      <td>44.406</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Race_ Other</td>\n",
       "      <td>31.156</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Race_ Amer-Indian-Eskimo</td>\n",
       "      <td>26.179</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Work-class_ Local-gov</td>\n",
       "      <td>24.675</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Occupation_ Protective-serv</td>\n",
       "      <td>20.897</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Work-class_ Self-emp-not-inc</td>\n",
       "      <td>19.716</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Occupation_ Transport-moving</td>\n",
       "      <td>18.873</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Occupation_ Tech-support</td>\n",
       "      <td>15.823</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Occupation_ Craft-repair</td>\n",
       "      <td>13.671</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Occupation_ Sales</td>\n",
       "      <td>10.152</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Marital-status_ Married-AF-spouse</td>\n",
       "      <td>5.798</td>\n",
       "      <td>0.016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Work-class_ Without-pay</td>\n",
       "      <td>4.646</td>\n",
       "      <td>0.031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Race_ Asian-Pac-Islander</td>\n",
       "      <td>3.962</td>\n",
       "      <td>0.047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Work-class_ State-gov</td>\n",
       "      <td>2.832</td>\n",
       "      <td>0.092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>fnlwgt</td>\n",
       "      <td>2.437</td>\n",
       "      <td>0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Occupation_ Armed-Forces</td>\n",
       "      <td>0.916</td>\n",
       "      <td>0.339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   feature  f-statistics  p-value\n",
       "0       Marital-status_ Married-civ-spouse      7455.209    0.000\n",
       "1                    Relationship_ Husband      5779.445    0.000\n",
       "2                            Education-num      3820.265    0.000\n",
       "3            Marital-status_ Never-married      3435.551    0.000\n",
       "4                                      Age      1875.009    0.000\n",
       "5                                  Capital      1674.887    0.000\n",
       "6                           Hours-per-week      1674.573    0.000\n",
       "7                  Relationship_ Own-child      1625.722    0.000\n",
       "8                              Sex_ Female      1484.467    0.000\n",
       "9                                Sex_ Male      1484.467    0.000\n",
       "10             Occupation_ Exec-managerial      1438.858    0.000\n",
       "11             Relationship_ Not-in-family      1166.834    0.000\n",
       "12              Occupation_ Prof-specialty      1026.260    0.000\n",
       "13               Occupation_ Other-service       853.548    0.000\n",
       "14                 Relationship_ Unmarried       655.419    0.000\n",
       "15                Work-class_ Self-emp-inc       581.814    0.000\n",
       "16                Marital-status_ Divorced       535.222    0.000\n",
       "17                      Relationship_ Wife       479.078    0.000\n",
       "18                     Work-class_ Private       419.267    0.000\n",
       "19                Occupation_ Adm-clerical       303.740    0.000\n",
       "20           Occupation_ Handlers-cleaners       267.862    0.000\n",
       "21                             Race_ Black       237.332    0.000\n",
       "22                             Race_ White       218.188    0.000\n",
       "23            Relationship_ Other-relative       216.867    0.000\n",
       "24           Occupation_ Machine-op-inspct       175.999    0.000\n",
       "25               Marital-status_ Separated       166.501    0.000\n",
       "26                 Marital-status_ Widowed       105.840    0.000\n",
       "27                 Work-class_ Federal-gov        99.469    0.000\n",
       "28             Occupation_ Farming-fishing        96.134    0.000\n",
       "29   Marital-status_ Married-spouse-absent        54.800    0.000\n",
       "30                          Native-country        48.151    0.000\n",
       "31             Occupation_ Priv-house-serv        44.406    0.000\n",
       "32                             Race_ Other        31.156    0.000\n",
       "33                Race_ Amer-Indian-Eskimo        26.179    0.000\n",
       "34                   Work-class_ Local-gov        24.675    0.000\n",
       "35             Occupation_ Protective-serv        20.897    0.000\n",
       "36            Work-class_ Self-emp-not-inc        19.716    0.000\n",
       "37            Occupation_ Transport-moving        18.873    0.000\n",
       "38                Occupation_ Tech-support        15.823    0.000\n",
       "39                Occupation_ Craft-repair        13.671    0.000\n",
       "40                       Occupation_ Sales        10.152    0.001\n",
       "41       Marital-status_ Married-AF-spouse         5.798    0.016\n",
       "42                 Work-class_ Without-pay         4.646    0.031\n",
       "43                Race_ Asian-Pac-Islander         3.962    0.047\n",
       "44                   Work-class_ State-gov         2.832    0.092\n",
       "45                                  fnlwgt         2.437    0.119\n",
       "46                Occupation_ Armed-Forces         0.916    0.339"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,20),dpi=100)\n",
    "sns.barplot(y=feature_importance.index,x=feature_importance['f-statistics'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "# maybe choose the first 28 features\n",
    "chosen=list(feature_importance.feature[0:29].values)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA after feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.93927619 0.0357592 ]\n"
     ]
    }
   ],
   "source": [
    "dataX_pca = dataX[chosen].copy()\n",
    "variance_explained = 0.95\n",
    "pca = PCA(variance_explained)\n",
    "principalComponents = pca.fit(dataX_pca)\n",
    "print(principalComponents.explained_variance_ratio_)\n",
    "principalComponents = principalComponents.transform(dataX_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.567</td>\n",
       "      <td>-2.864</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.588</td>\n",
       "      <td>-2.623</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.455</td>\n",
       "      <td>1.150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.539</td>\n",
       "      <td>3.288</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-10.409</td>\n",
       "      <td>-2.983</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30134</th>\n",
       "      <td>-11.419</td>\n",
       "      <td>-1.949</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30135</th>\n",
       "      <td>1.566</td>\n",
       "      <td>1.155</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30136</th>\n",
       "      <td>19.531</td>\n",
       "      <td>1.377</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30137</th>\n",
       "      <td>-16.492</td>\n",
       "      <td>1.109</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30138</th>\n",
       "      <td>13.567</td>\n",
       "      <td>1.103</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30139 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0      1  income\n",
       "0       0.567 -2.864       0\n",
       "1      11.588 -2.623       0\n",
       "2      -0.455  1.150       0\n",
       "3      14.539  3.288       0\n",
       "4     -10.409 -2.983       0\n",
       "...       ...    ...     ...\n",
       "30134 -11.419 -1.949       0\n",
       "30135   1.566  1.155       1\n",
       "30136  19.531  1.377       0\n",
       "30137 -16.492  1.109       0\n",
       "30138  13.567  1.103       1\n",
       "\n",
       "[30139 rows x 3 columns]"
      ]
     },
     "execution_count": 166,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf = pd.DataFrame(data = principalComponents)\n",
    "# # Concatenate dataframe with 'shares_bin2' column\n",
    "df_pca = pd.merge(principalDf, df[['income']].reset_index(drop='index'),right_index=True,left_index=True)\n",
    "df_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_pcs=pca.components_.shape[0]\n",
    "most_important = [np.abs(pca.components_[i]).argmax() for i in range(n_pcs)]\n",
    "initial_feature_names  = chosen\n",
    "most_important_names = [initial_feature_names[most_important[i]] for i in range(n_pcs)]\n",
    "dic = {'PC{}'.format(i): most_important_names[i] for i in range(n_pcs)}\n",
    "pd.DataFrame(dic.items())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PCA with no feature selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.9309868  0.03548639]\n"
     ]
    }
   ],
   "source": [
    "dataX_pca_nofs = dataX.copy()\n",
    "variance_explained = 0.95\n",
    "pca = PCA(variance_explained)\n",
    "principalComponents = pca.fit(dataX_pca_nofs)\n",
    "print(principalComponents.explained_variance_ratio_)\n",
    "principalComponents = principalComponents.transform(dataX_pca_nofs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>income</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.567</td>\n",
       "      <td>-2.864</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.588</td>\n",
       "      <td>-2.623</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.455</td>\n",
       "      <td>1.150</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.539</td>\n",
       "      <td>3.288</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-10.409</td>\n",
       "      <td>-2.983</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30134</th>\n",
       "      <td>-11.419</td>\n",
       "      <td>-1.949</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30135</th>\n",
       "      <td>1.566</td>\n",
       "      <td>1.155</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30136</th>\n",
       "      <td>19.531</td>\n",
       "      <td>1.377</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30137</th>\n",
       "      <td>-16.492</td>\n",
       "      <td>1.109</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30138</th>\n",
       "      <td>13.567</td>\n",
       "      <td>1.103</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30139 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0      1  income\n",
       "0       0.567 -2.864       0\n",
       "1      11.588 -2.623       0\n",
       "2      -0.455  1.150       0\n",
       "3      14.539  3.288       0\n",
       "4     -10.409 -2.983       0\n",
       "...       ...    ...     ...\n",
       "30134 -11.419 -1.949       0\n",
       "30135   1.566  1.155       1\n",
       "30136  19.531  1.377       0\n",
       "30137 -16.492  1.109       0\n",
       "30138  13.567  1.103       1\n",
       "\n",
       "[30139 rows x 3 columns]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf = pd.DataFrame(data = principalComponents)\n",
    "# # Concatenate dataframe with 'shares_bin2' column\n",
    "df_pca_nofs = pd.merge(principalDf, df[['income']].reset_index(drop='index'),right_index=True,left_index=True)\n",
    "df_pca_nofs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PC0</td>\n",
       "      <td>Age</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PC1</td>\n",
       "      <td>Education-num</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0               1\n",
       "0  PC0             Age\n",
       "1  PC1   Education-num"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_pcs=pca.components_.shape[0]\n",
    "most_important = [np.abs(pca.components_[i]).argmax() for i in range(n_pcs)]\n",
    "initial_feature_names  = chosen\n",
    "most_important_names = [initial_feature_names[most_important[i]] for i in range(n_pcs)]\n",
    "dic = {'PC{}'.format(i): most_important_names[i] for i in range(n_pcs)}\n",
    "pd.DataFrame(dic.items())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "import sklearn.metrics as metrics\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn import decomposition, ensemble\n",
    "from sklearn import model_selection, preprocessing, linear_model, naive_bayes, metrics, svm\n",
    "import xgboost\n",
    "import lightgbm as lgb\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model(X_train, X_test, y_train, y_test):\n",
    "    models = {'knn': KNeighborsClassifier(),\n",
    "            'lr':linear_model.LogisticRegression(),\n",
    "            'rf':ensemble.RandomForestClassifier(),\n",
    "            'xgboost':xgboost.XGBClassifier(),\n",
    "            'lighGBM':lgb.LGBMClassifier()\n",
    "            }\n",
    "\n",
    "    y_predicts = pd.DataFrame()\n",
    "    for name, model in models.items():\n",
    "        clf = model.fit(X_train, y_train)\n",
    "        y_train_pred = clf.predict(X_train)\n",
    "        acc = metrics.accuracy_score(y_train, y_train_pred)\n",
    "        print('Model: {} | train accuracy: {:.4f}'.format(name, acc))\n",
    "        y_test_pred = clf.predict(X_test) #_topic\n",
    "        acc_test = metrics.accuracy_score(y_test, y_test_pred)\n",
    "        print('Test: Model: {} | Test accuracy: {:.4f}'.format(name, acc_test))\n",
    "        y_predicts.loc[name,'train accuracy']=acc\n",
    "        y_predicts.loc[name,'test accuracy']=acc_test\n",
    "    return y_predicts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train, x_test, y_train, y_test = train_test_split(dataX, dataY,\n",
    "                                                    stratify=dataY, \n",
    "                                                  random_state=42, \n",
    "                                                  test_size=0.2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: knn | train accuracy: 0.8721\n",
      "Test: Model: knn | Test accuracy: 0.8210\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: lr | train accuracy: 0.8477\n",
      "Test: Model: lr | Test accuracy: 0.8432\n",
      "Model: rf | train accuracy: 1.0000\n",
      "Test: Model: rf | Test accuracy: 0.8510\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py:173: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: xgboost | train accuracy: 0.9073\n",
      "Test: Model: xgboost | Test accuracy: 0.8643\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py:173: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: lighGBM | train accuracy: 0.8879\n",
      "Test: Model: lighGBM | Test accuracy: 0.8676\n"
     ]
    }
   ],
   "source": [
    "result_no=model(x_train, x_test, y_train, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_fs, x_test_fs, y_train_fs, y_test_fs = train_test_split(dataX[chosen], dataY,\n",
    "                                                    stratify=dataY, \n",
    "                                                  random_state=42, \n",
    "                                                  test_size=0.2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: knn | train accuracy: 0.8707\n",
      "Test: Model: knn | Test accuracy: 0.8227\n",
      "Model: lr | train accuracy: 0.8459\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/sklearn/linear_model/_logistic.py:814: 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",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test: Model: lr | Test accuracy: 0.8439\n",
      "Model: rf | train accuracy: 0.9674\n",
      "Test: Model: rf | Test accuracy: 0.8441\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py:173: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: xgboost | train accuracy: 0.8888\n",
      "Test: Model: xgboost | Test accuracy: 0.8623\n",
      "Model: lighGBM | train accuracy: 0.8803\n",
      "Test: Model: lighGBM | Test accuracy: 0.8656\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py:173: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "result_fs=model(x_train_fs, x_test_fs, y_train_fs, y_test_fs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_pca, x_test_pca, y_train_pca, y_test_pca = train_test_split(df_pca.drop('income',axis=1), df_pca['income'],\n",
    "                                                    stratify=df_pca['income'], \n",
    "                                                  random_state=42, \n",
    "                                                  test_size=0.2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: knn | train accuracy: 0.8564\n",
      "Test: Model: knn | Test accuracy: 0.7887\n",
      "Model: lr | train accuracy: 0.7815\n",
      "Test: Model: lr | Test accuracy: 0.7782\n",
      "Model: rf | train accuracy: 0.9674\n",
      "Test: Model: rf | Test accuracy: 0.7860\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py:173: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: xgboost | train accuracy: 0.8567\n",
      "Test: Model: xgboost | Test accuracy: 0.8089\n",
      "Model: lighGBM | train accuracy: 0.8251\n",
      "Test: Model: lighGBM | Test accuracy: 0.8127\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py:173: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "result_fs_pca=model(x_train_pca, x_test_pca, y_train_pca, y_test_pca)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train_pca_nofs, x_test_pca_nofs, y_train_pca_nofs, y_test_pca_nofs = train_test_split(df_pca_nofs.drop('income',axis=1), df_pca_nofs['income'],\n",
    "                                                    stratify=df_pca_nofs['income'], \n",
    "                                                  random_state=42, \n",
    "                                                  test_size=0.2, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: knn | train accuracy: 0.8564\n",
      "Test: Model: knn | Test accuracy: 0.7887\n",
      "Model: lr | train accuracy: 0.7815\n",
      "Test: Model: lr | Test accuracy: 0.7782\n",
      "Model: rf | train accuracy: 0.9675\n",
      "Test: Model: rf | Test accuracy: 0.7882\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py:173: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: xgboost | train accuracy: 0.8567\n",
      "Test: Model: xgboost | Test accuracy: 0.8089\n",
      "Model: lighGBM | train accuracy: 0.8251\n",
      "Test: Model: lighGBM | Test accuracy: 0.8127\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.8/site-packages/xgboost/data.py:173: FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version. Use pandas.Index with the appropriate dtype instead.\n",
      "  from pandas import MultiIndex, Int64Index\n"
     ]
    }
   ],
   "source": [
    "result_pca=model(x_train_pca_nofs, x_test_pca_nofs, y_train_pca_nofs, y_test_pca_nofs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "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>train accuracy</th>\n",
       "      <th>test accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>0.872</td>\n",
       "      <td>0.821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>0.848</td>\n",
       "      <td>0.843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>1.000</td>\n",
       "      <td>0.851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>0.907</td>\n",
       "      <td>0.864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lighGBM</th>\n",
       "      <td>0.888</td>\n",
       "      <td>0.868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         train accuracy  test accuracy\n",
       "knn               0.872          0.821\n",
       "lr                0.848          0.843\n",
       "rf                1.000          0.851\n",
       "xgboost           0.907          0.864\n",
       "lighGBM           0.888          0.868"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_no"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>train accuracy</th>\n",
       "      <th>test accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>0.871</td>\n",
       "      <td>0.823</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>0.846</td>\n",
       "      <td>0.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>0.967</td>\n",
       "      <td>0.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>0.889</td>\n",
       "      <td>0.862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lighGBM</th>\n",
       "      <td>0.880</td>\n",
       "      <td>0.866</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         train accuracy  test accuracy\n",
       "knn               0.871          0.823\n",
       "lr                0.846          0.844\n",
       "rf                0.967          0.844\n",
       "xgboost           0.889          0.862\n",
       "lighGBM           0.880          0.866"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_fs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "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>train accuracy</th>\n",
       "      <th>test accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>0.856</td>\n",
       "      <td>0.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>0.782</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>0.967</td>\n",
       "      <td>0.788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>0.857</td>\n",
       "      <td>0.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lighGBM</th>\n",
       "      <td>0.825</td>\n",
       "      <td>0.813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         train accuracy  test accuracy\n",
       "knn               0.856          0.789\n",
       "lr                0.782          0.778\n",
       "rf                0.967          0.788\n",
       "xgboost           0.857          0.809\n",
       "lighGBM           0.825          0.813"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "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>train accuracy</th>\n",
       "      <th>test accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>knn</th>\n",
       "      <td>0.856</td>\n",
       "      <td>0.789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lr</th>\n",
       "      <td>0.782</td>\n",
       "      <td>0.778</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rf</th>\n",
       "      <td>0.967</td>\n",
       "      <td>0.786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>xgboost</th>\n",
       "      <td>0.857</td>\n",
       "      <td>0.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lighGBM</th>\n",
       "      <td>0.825</td>\n",
       "      <td>0.813</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         train accuracy  test accuracy\n",
       "knn               0.856          0.789\n",
       "lr                0.782          0.778\n",
       "rf                0.967          0.786\n",
       "xgboost           0.857          0.809\n",
       "lighGBM           0.825          0.813"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result_fs_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['knn', 'lr', 'rf', 'xgboost', 'lighGBM']"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(result_no.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8),dpi=100)\n",
    "plt.plot(result_no['train accuracy'],marker='o',label='baseline')\n",
    "plt.plot(result_fs['train accuracy'],marker='o',label='28 important features')\n",
    "plt.plot(result_pca['train accuracy'],marker='o',label='pca')\n",
    "plt.plot(result_fs_pca['train accuracy'],marker='o',label='28 important features+pca')\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('train accuracy')\n",
    "plt.xlabel('models')\n",
    "plt.title('Train accuracy comparison')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8),dpi=100)\n",
    "plt.plot(result_no['test accuracy'],marker='o',label='baseline')\n",
    "plt.plot(result_fs['test accuracy'],marker='o',label='28 important features')\n",
    "plt.plot(result_pca['test accuracy'],marker='o',label='pca')\n",
    "plt.plot(result_fs_pca['test accuracy'],marker='o',label='28 important features+pca')\n",
    "plt.legend(loc='best')\n",
    "plt.ylabel('Test accuracy')\n",
    "plt.xlabel('models')\n",
    "plt.title('Test accuracy comparison')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "40d3a090f54c6569ab1632332b64b2c03c39dcf918b08424e98f38b5ae0af88f"
  },
  "kernelspec": {
   "display_name": "Python 3.8.3 ('base')",
   "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": "3.8.3"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}