{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3b7bcb5f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Correlation and Variance Threshold:\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.datasets import fetch_california_housing\n",
    "from sklearn.feature_selection import VarianceThreshold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9c107a0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  MedHouseVal  \n",
       "0    -122.23        4.526  \n",
       "1    -122.22        3.585  \n",
       "2    -122.24        3.521  \n",
       "3    -122.25        3.413  \n",
       "4    -122.25        3.422  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california_housing = fetch_california_housing(as_frame = True)\n",
    "california_housing.frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "114b8b30",
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  \n",
       "0    -122.23  \n",
       "1    -122.22  \n",
       "2    -122.24  \n",
       "3    -122.25  \n",
       "4    -122.25  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california_housing.data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8df57099",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.526\n",
       "1    3.585\n",
       "2    3.521\n",
       "3    3.413\n",
       "4    3.422\n",
       "Name: MedHouseVal, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california_housing.target.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "16dfc417",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Correlation Chart:\n",
    "corrCH = california_housing.frame.corr()\n",
    "plt.figure(figsize = (10, 8))\n",
    "sns.heatmap(corrCH, annot = True, cmap = \"PiYG\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "303c5a8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Variance Threshold:\n",
    "california_housing.frame[\"NewColumn\"] = 321"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e989ec2f",
   "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>MedInc</th>\n",
       "      <th>HouseAge</th>\n",
       "      <th>AveRooms</th>\n",
       "      <th>AveBedrms</th>\n",
       "      <th>Population</th>\n",
       "      <th>AveOccup</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>MedHouseVal</th>\n",
       "      <th>NewColumn</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3252</td>\n",
       "      <td>41.0</td>\n",
       "      <td>6.984127</td>\n",
       "      <td>1.023810</td>\n",
       "      <td>322.0</td>\n",
       "      <td>2.555556</td>\n",
       "      <td>37.88</td>\n",
       "      <td>-122.23</td>\n",
       "      <td>4.526</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.3014</td>\n",
       "      <td>21.0</td>\n",
       "      <td>6.238137</td>\n",
       "      <td>0.971880</td>\n",
       "      <td>2401.0</td>\n",
       "      <td>2.109842</td>\n",
       "      <td>37.86</td>\n",
       "      <td>-122.22</td>\n",
       "      <td>3.585</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.2574</td>\n",
       "      <td>52.0</td>\n",
       "      <td>8.288136</td>\n",
       "      <td>1.073446</td>\n",
       "      <td>496.0</td>\n",
       "      <td>2.802260</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.24</td>\n",
       "      <td>3.521</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.6431</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.817352</td>\n",
       "      <td>1.073059</td>\n",
       "      <td>558.0</td>\n",
       "      <td>2.547945</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.413</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.8462</td>\n",
       "      <td>52.0</td>\n",
       "      <td>6.281853</td>\n",
       "      <td>1.081081</td>\n",
       "      <td>565.0</td>\n",
       "      <td>2.181467</td>\n",
       "      <td>37.85</td>\n",
       "      <td>-122.25</td>\n",
       "      <td>3.422</td>\n",
       "      <td>321</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MedInc  HouseAge  AveRooms  AveBedrms  Population  AveOccup  Latitude  \\\n",
       "0  8.3252      41.0  6.984127   1.023810       322.0  2.555556     37.88   \n",
       "1  8.3014      21.0  6.238137   0.971880      2401.0  2.109842     37.86   \n",
       "2  7.2574      52.0  8.288136   1.073446       496.0  2.802260     37.85   \n",
       "3  5.6431      52.0  5.817352   1.073059       558.0  2.547945     37.85   \n",
       "4  3.8462      52.0  6.281853   1.081081       565.0  2.181467     37.85   \n",
       "\n",
       "   Longitude  MedHouseVal  NewColumn  \n",
       "0    -122.23        4.526        321  \n",
       "1    -122.22        3.585        321  \n",
       "2    -122.24        3.521        321  \n",
       "3    -122.25        3.413        321  \n",
       "4    -122.25        3.422        321  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "california_housing.frame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "3f223cd1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
       "       False])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VarThresh = VarianceThreshold(threshold = 0)\n",
    "VarThresh.fit(california_housing.frame)\n",
    "VarThresh.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "64290f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True, False,  True,  True,  True,  True,  True,\n",
       "       False])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "VarThresh = VarianceThreshold(threshold = 0.5)\n",
    "VarThresh.fit(california_housing.frame)\n",
    "VarThresh.get_support()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "bb2b4ad7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Chi Square and ANOVA F-value:\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.feature_selection import SelectKBest\n",
    "from sklearn.feature_selection import SelectPercentile\n",
    "from sklearn.feature_selection import chi2\n",
    "from sklearn.feature_selection import f_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "55df9a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "IRIS = load_iris()\n",
    "x = IRIS.data\n",
    "y = IRIS.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fcc8a808",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "a71771bc",
   "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>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sepal length (cm)</td>\n",
       "      <td>10.287129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sepal width (cm)</td>\n",
       "      <td>5.022670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>petal length (cm)</td>\n",
       "      <td>133.068548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>petal width (cm)</td>\n",
       "      <td>74.279070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature       score\n",
       "0  sepal length (cm)   10.287129\n",
       "1   sepal width (cm)    5.022670\n",
       "2  petal length (cm)  133.068548\n",
       "3   petal width (cm)   74.279070"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_selector = SelectKBest(chi2, k = 2)\n",
    "KBest = chi2_selector.fit_transform(x, y)\n",
    "chi2_scores = pd.DataFrame(list(zip(IRIS.feature_names, chi2_selector.scores_)),\n",
    "                           columns = [\"feature\", \"score\"])\n",
    "chi2_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "67703b87",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Original Features : 4\n",
      "Number of Reduced Features : 2\n"
     ]
    }
   ],
   "source": [
    "print (\"Number of Original Features :\", x.shape[1])\n",
    "print (\"Number of Reduced Features :\", KBest.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "106efdfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['petal length (cm)', 'petal width (cm)'], dtype='<U17')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(IRIS.feature_names)[chi2_selector.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "db2167db",
   "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>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sepal length (cm)</td>\n",
       "      <td>81.197151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sepal width (cm)</td>\n",
       "      <td>33.715004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>petal length (cm)</td>\n",
       "      <td>1160.011597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>petal width (cm)</td>\n",
       "      <td>385.483002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature        score\n",
       "0  sepal length (cm)    81.197151\n",
       "1   sepal width (cm)    33.715004\n",
       "2  petal length (cm)  1160.011597\n",
       "3   petal width (cm)   385.483002"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_selector = SelectKBest(f_classif, k = 3)\n",
    "KBest = chi2_selector.fit_transform(x, y)\n",
    "chi2_scores = pd.DataFrame(list(zip(IRIS.feature_names, chi2_selector.scores_)), columns = [\"feature\", \"score\"])\n",
    "chi2_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a997334f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Original Features : 4\n",
      "Number of Reduced Features : 3\n"
     ]
    }
   ],
   "source": [
    "print (\"Number of Original Features :\", x.shape[1])\n",
    "print (\"Number of Reduced Features :\", KBest.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3aba64ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['sepal length (cm)', 'petal length (cm)', 'petal width (cm)'],\n",
       "      dtype='<U17')"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(IRIS.feature_names)[chi2_selector.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c259de1f",
   "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>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sepal length (cm)</td>\n",
       "      <td>10.287129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sepal width (cm)</td>\n",
       "      <td>5.022670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>petal length (cm)</td>\n",
       "      <td>133.068548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>petal width (cm)</td>\n",
       "      <td>74.279070</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature       score\n",
       "0  sepal length (cm)   10.287129\n",
       "1   sepal width (cm)    5.022670\n",
       "2  petal length (cm)  133.068548\n",
       "3   petal width (cm)   74.279070"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_selector = SelectPercentile(chi2)\n",
    "KBest = chi2_selector.fit_transform(x, y)\n",
    "chi2_scores = pd.DataFrame(list(zip(IRIS.feature_names, chi2_selector.scores_)), columns = [\"feature\", \"score\"])\n",
    "chi2_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "10ecb36e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Original Features : 4\n",
      "Number of Reduced Features : 1\n"
     ]
    }
   ],
   "source": [
    "print (\"Number of Original Features :\", x.shape[1])\n",
    "print (\"Number of Reduced Features :\", KBest.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "1947368b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['petal length (cm)'], dtype='<U17')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(IRIS.feature_names)[chi2_selector.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "93830906",
   "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>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>sepal length (cm)</td>\n",
       "      <td>81.197151</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>sepal width (cm)</td>\n",
       "      <td>33.715004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>petal length (cm)</td>\n",
       "      <td>1160.011597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>petal width (cm)</td>\n",
       "      <td>385.483002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             feature        score\n",
       "0  sepal length (cm)    81.197151\n",
       "1   sepal width (cm)    33.715004\n",
       "2  petal length (cm)  1160.011597\n",
       "3   petal width (cm)   385.483002"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chi2_selector = SelectPercentile(f_classif)\n",
    "KBest = chi2_selector.fit_transform(x, y)\n",
    "chi2_scores = pd.DataFrame(list(zip(IRIS.feature_names, chi2_selector.scores_)), columns = [\"feature\", \"score\"])\n",
    "chi2_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "40202512",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of Original Features : 4\n",
      "Number of Reduced Features : 1\n"
     ]
    }
   ],
   "source": [
    "print (\"Number of Original Features :\", x.shape[1])\n",
    "print (\"Number of Reduced Features :\", KBest.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "291d71e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['petal length (cm)'], dtype='<U17')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(IRIS.feature_names)[chi2_selector.get_support()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "00acf43e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Information Gain:\n",
    "from sklearn.feature_selection import mutual_info_classif"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "f4373a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>bmi</th>\n",
       "      <th>children</th>\n",
       "      <th>smoker</th>\n",
       "      <th>region</th>\n",
       "      <th>expenses</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19</td>\n",
       "      <td>female</td>\n",
       "      <td>27.9</td>\n",
       "      <td>0</td>\n",
       "      <td>yes</td>\n",
       "      <td>southwest</td>\n",
       "      <td>16884.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>18</td>\n",
       "      <td>male</td>\n",
       "      <td>33.8</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>1725.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>28</td>\n",
       "      <td>male</td>\n",
       "      <td>33.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>southeast</td>\n",
       "      <td>4449.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33</td>\n",
       "      <td>male</td>\n",
       "      <td>22.7</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>21984.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32</td>\n",
       "      <td>male</td>\n",
       "      <td>28.9</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>northwest</td>\n",
       "      <td>3866.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   age     sex   bmi  children smoker     region  expenses\n",
       "0   19  female  27.9         0    yes  southwest  16884.92\n",
       "1   18    male  33.8         1     no  southeast   1725.55\n",
       "2   28    male  33.0         3     no  southeast   4449.46\n",
       "3   33    male  22.7         0     no  northwest  21984.47\n",
       "4   32    male  28.9         0     no  northwest   3866.86"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Data = pd.read_csv(\"insurance.csv\")\n",
    "Data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "9f8cbbee",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\IRANICA\\anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:993: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.02776601, 0.09673063, 0.07626668])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = Data[[\"age\", \"bmi\", \"expenses\"]]\n",
    "target = Data[[\"region\"]]\n",
    "feature_scores = mutual_info_classif(features, target, random_state = 75)\n",
    "feature_scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "12b50809",
   "metadata": {},
   "outputs": [],
   "source": [
    "#So bmi has highest score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a3003ec",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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