Regression-Housing.ipynb

{
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7-final"
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   "display_name": "Python 3.7.7 64-bit"
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 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "source": [
    "# Regression problem on housing.csv"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.preprocessing import StandardScaler, PowerTransformer, MinMaxScaler,MaxAbsScaler,QuantileTransformer,RobustScaler\n",
    "from sklearn.model_selection import train_test_split, cross_val_score\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n",
    "import xgboost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "File ditemukan\n"
    }
   ],
   "source": [
    "directory = os.path.join('D:/Bootcamp ML - Mada/regression dataset/', 'housing.csv')\n",
    "if os.path.isfile(directory):\n",
    "  print(\"File ditemukan\")\n",
    "else:\n",
    "    print(\"tidak ada\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      crim    zn  indus  chas    nox     rm   age     dis  rad  tax  ptratio  \\\n0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296     15.3   \n1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242     17.8   \n2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242     17.8   \n3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222     18.7   \n4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222     18.7   \n\n        b  lstat  medv  \n0  396.90   4.98  24.0  \n1  396.90   9.14  21.6  \n2  392.83   4.03  34.7  \n3  394.63   2.94  33.4  \n4  396.90   5.33  36.2  ",
      "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>crim</th>\n      <th>zn</th>\n      <th>indus</th>\n      <th>chas</th>\n      <th>nox</th>\n      <th>rm</th>\n      <th>age</th>\n      <th>dis</th>\n      <th>rad</th>\n      <th>tax</th>\n      <th>ptratio</th>\n      <th>b</th>\n      <th>lstat</th>\n      <th>medv</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.00632</td>\n      <td>18.0</td>\n      <td>2.31</td>\n      <td>0</td>\n      <td>0.538</td>\n      <td>6.575</td>\n      <td>65.2</td>\n      <td>4.0900</td>\n      <td>1</td>\n      <td>296</td>\n      <td>15.3</td>\n      <td>396.90</td>\n      <td>4.98</td>\n      <td>24.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.02731</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0</td>\n      <td>0.469</td>\n      <td>6.421</td>\n      <td>78.9</td>\n      <td>4.9671</td>\n      <td>2</td>\n      <td>242</td>\n      <td>17.8</td>\n      <td>396.90</td>\n      <td>9.14</td>\n      <td>21.6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.02729</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0</td>\n      <td>0.469</td>\n      <td>7.185</td>\n      <td>61.1</td>\n      <td>4.9671</td>\n      <td>2</td>\n      <td>242</td>\n      <td>17.8</td>\n      <td>392.83</td>\n      <td>4.03</td>\n      <td>34.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.03237</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0</td>\n      <td>0.458</td>\n      <td>6.998</td>\n      <td>45.8</td>\n      <td>6.0622</td>\n      <td>3</td>\n      <td>222</td>\n      <td>18.7</td>\n      <td>394.63</td>\n      <td>2.94</td>\n      <td>33.4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.06905</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0</td>\n      <td>0.458</td>\n      <td>7.147</td>\n      <td>54.2</td>\n      <td>6.0622</td>\n      <td>3</td>\n      <td>222</td>\n      <td>18.7</td>\n      <td>396.90</td>\n      <td>5.33</td>\n      <td>36.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "dataset = pd.read_csv(directory)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 506 entries, 0 to 505\nData columns (total 14 columns):\n #   Column   Non-Null Count  Dtype  \n---  ------   --------------  -----  \n 0   crim     506 non-null    float64\n 1   zn       506 non-null    float64\n 2   indus    506 non-null    float64\n 3   chas     506 non-null    int64  \n 4   nox      506 non-null    float64\n 5   rm       506 non-null    float64\n 6   age      506 non-null    float64\n 7   dis      506 non-null    float64\n 8   rad      506 non-null    int64  \n 9   tax      506 non-null    int64  \n 10  ptratio  506 non-null    float64\n 11  b        506 non-null    float64\n 12  lstat    506 non-null    float64\n 13  medv     506 non-null    float64\ndtypes: float64(11), int64(3)\nmemory usage: 55.5 KB\n"
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "             crim          zn       indus        chas         nox          rm  \\\ncount  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \nmean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \nstd      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \nmin      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n75%      3.677082   12.500000   18.100000    0.000000    0.624000    6.623500   \nmax     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n\n              age         dis         rad         tax     ptratio           b  \\\ncount  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \nmean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \nstd     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \nmin      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \nmax    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n\n            lstat        medv  \ncount  506.000000  506.000000  \nmean    12.653063   22.532806  \nstd      7.141062    9.197104  \nmin      1.730000    5.000000  \n25%      6.950000   17.025000  \n50%     11.360000   21.200000  \n75%     16.955000   25.000000  \nmax     37.970000   50.000000  ",
      "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>crim</th>\n      <th>zn</th>\n      <th>indus</th>\n      <th>chas</th>\n      <th>nox</th>\n      <th>rm</th>\n      <th>age</th>\n      <th>dis</th>\n      <th>rad</th>\n      <th>tax</th>\n      <th>ptratio</th>\n      <th>b</th>\n      <th>lstat</th>\n      <th>medv</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n      <td>506.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>3.613524</td>\n      <td>11.363636</td>\n      <td>11.136779</td>\n      <td>0.069170</td>\n      <td>0.554695</td>\n      <td>6.284634</td>\n      <td>68.574901</td>\n      <td>3.795043</td>\n      <td>9.549407</td>\n      <td>408.237154</td>\n      <td>18.455534</td>\n      <td>356.674032</td>\n      <td>12.653063</td>\n      <td>22.532806</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>8.601545</td>\n      <td>23.322453</td>\n      <td>6.860353</td>\n      <td>0.253994</td>\n      <td>0.115878</td>\n      <td>0.702617</td>\n      <td>28.148861</td>\n      <td>2.105710</td>\n      <td>8.707259</td>\n      <td>168.537116</td>\n      <td>2.164946</td>\n      <td>91.294864</td>\n      <td>7.141062</td>\n      <td>9.197104</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.006320</td>\n      <td>0.000000</td>\n      <td>0.460000</td>\n      <td>0.000000</td>\n      <td>0.385000</td>\n      <td>3.561000</td>\n      <td>2.900000</td>\n      <td>1.129600</td>\n      <td>1.000000</td>\n      <td>187.000000</td>\n      <td>12.600000</td>\n      <td>0.320000</td>\n      <td>1.730000</td>\n      <td>5.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>0.082045</td>\n      <td>0.000000</td>\n      <td>5.190000</td>\n      <td>0.000000</td>\n      <td>0.449000</td>\n      <td>5.885500</td>\n      <td>45.025000</td>\n      <td>2.100175</td>\n      <td>4.000000</td>\n      <td>279.000000</td>\n      <td>17.400000</td>\n      <td>375.377500</td>\n      <td>6.950000</td>\n      <td>17.025000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>0.256510</td>\n      <td>0.000000</td>\n      <td>9.690000</td>\n      <td>0.000000</td>\n      <td>0.538000</td>\n      <td>6.208500</td>\n      <td>77.500000</td>\n      <td>3.207450</td>\n      <td>5.000000</td>\n      <td>330.000000</td>\n      <td>19.050000</td>\n      <td>391.440000</td>\n      <td>11.360000</td>\n      <td>21.200000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>3.677082</td>\n      <td>12.500000</td>\n      <td>18.100000</td>\n      <td>0.000000</td>\n      <td>0.624000</td>\n      <td>6.623500</td>\n      <td>94.075000</td>\n      <td>5.188425</td>\n      <td>24.000000</td>\n      <td>666.000000</td>\n      <td>20.200000</td>\n      <td>396.225000</td>\n      <td>16.955000</td>\n      <td>25.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>88.976200</td>\n      <td>100.000000</td>\n      <td>27.740000</td>\n      <td>1.000000</td>\n      <td>0.871000</td>\n      <td>8.780000</td>\n      <td>100.000000</td>\n      <td>12.126500</td>\n      <td>24.000000</td>\n      <td>711.000000</td>\n      <td>22.000000</td>\n      <td>396.900000</td>\n      <td>37.970000</td>\n      <td>50.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "14.33370    2\n0.01501     2\n0.08265     1\n1.35472     1\n0.03615     1\n           ..\n0.11460     1\n0.03237     1\n5.82115     1\n0.02187     1\n0.03578     1\nName: crim, Length: 504, dtype: int64"
     },
     "metadata": {},
     "execution_count": 6
    }
   ],
   "source": [
    "dataset['crim'].value_counts()"
   ]
  },
  {
   "source": [
    "## Replace '0' values from zn feature with the mean values"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "      crim         zn  indus  chas    nox     rm   age     dis  rad  tax  \\\n0  0.00632  18.000000   2.31     0  0.538  6.575  65.2  4.0900    1  296   \n1  0.02731  11.363636   7.07     0  0.469  6.421  78.9  4.9671    2  242   \n2  0.02729  11.363636   7.07     0  0.469  7.185  61.1  4.9671    2  242   \n3  0.03237  11.363636   2.18     0  0.458  6.998  45.8  6.0622    3  222   \n4  0.06905  11.363636   2.18     0  0.458  7.147  54.2  6.0622    3  222   \n\n   ptratio       b  lstat  medv  \n0     15.3  396.90   4.98  24.0  \n1     17.8  396.90   9.14  21.6  \n2     17.8  392.83   4.03  34.7  \n3     18.7  394.63   2.94  33.4  \n4     18.7  396.90   5.33  36.2  ",
      "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>crim</th>\n      <th>zn</th>\n      <th>indus</th>\n      <th>chas</th>\n      <th>nox</th>\n      <th>rm</th>\n      <th>age</th>\n      <th>dis</th>\n      <th>rad</th>\n      <th>tax</th>\n      <th>ptratio</th>\n      <th>b</th>\n      <th>lstat</th>\n      <th>medv</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.00632</td>\n      <td>18.000000</td>\n      <td>2.31</td>\n      <td>0</td>\n      <td>0.538</td>\n      <td>6.575</td>\n      <td>65.2</td>\n      <td>4.0900</td>\n      <td>1</td>\n      <td>296</td>\n      <td>15.3</td>\n      <td>396.90</td>\n      <td>4.98</td>\n      <td>24.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.02731</td>\n      <td>11.363636</td>\n      <td>7.07</td>\n      <td>0</td>\n      <td>0.469</td>\n      <td>6.421</td>\n      <td>78.9</td>\n      <td>4.9671</td>\n      <td>2</td>\n      <td>242</td>\n      <td>17.8</td>\n      <td>396.90</td>\n      <td>9.14</td>\n      <td>21.6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.02729</td>\n      <td>11.363636</td>\n      <td>7.07</td>\n      <td>0</td>\n      <td>0.469</td>\n      <td>7.185</td>\n      <td>61.1</td>\n      <td>4.9671</td>\n      <td>2</td>\n      <td>242</td>\n      <td>17.8</td>\n      <td>392.83</td>\n      <td>4.03</td>\n      <td>34.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.03237</td>\n      <td>11.363636</td>\n      <td>2.18</td>\n      <td>0</td>\n      <td>0.458</td>\n      <td>6.998</td>\n      <td>45.8</td>\n      <td>6.0622</td>\n      <td>3</td>\n      <td>222</td>\n      <td>18.7</td>\n      <td>394.63</td>\n      <td>2.94</td>\n      <td>33.4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.06905</td>\n      <td>11.363636</td>\n      <td>2.18</td>\n      <td>0</td>\n      <td>0.458</td>\n      <td>7.147</td>\n      <td>54.2</td>\n      <td>6.0622</td>\n      <td>3</td>\n      <td>222</td>\n      <td>18.7</td>\n      <td>396.90</td>\n      <td>5.33</td>\n      <td>36.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 7
    }
   ],
   "source": [
    "mean_zn = dataset['zn'].astype('float').mean(axis=0)\n",
    "dataset['zn'].replace(0.0, mean_zn , inplace=True)\n",
    "dataset.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "             crim          zn       indus        chas         nox          rm  \\\ncount  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \nmean     3.613524   19.717930   11.136779    0.069170    0.554695    6.284634   \nstd      8.601545   19.464872    6.860353    0.253994    0.115878    0.702617   \nmin      0.006320   11.363636    0.460000    0.000000    0.385000    3.561000   \n25%      0.082045   11.363636    5.190000    0.000000    0.449000    5.885500   \n50%      0.256510   11.363636    9.690000    0.000000    0.538000    6.208500   \n75%      3.677082   12.500000   18.100000    0.000000    0.624000    6.623500   \nmax     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n\n              age         dis         rad         tax     ptratio           b  \\\ncount  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \nmean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \nstd     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \nmin      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \nmax    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n\n            lstat        medv  \ncount  506.000000  506.000000  \nmean    12.653063   22.532806  \nstd      7.141062    9.197104  \nmin      1.730000    5.000000  \n25%      6.950000   17.025000  \n50%     11.360000   21.200000  \n75%     16.955000   25.000000  \nmax     37.970000   50.000000  ",
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     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "dataset.describe()"
   ]
  },
  {
   "source": [
    "## Plotting Histogram"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[<AxesSubplot:title={'center':'crim'}>,\n        <AxesSubplot:title={'center':'zn'}>,\n        <AxesSubplot:title={'center':'indus'}>,\n        <AxesSubplot:title={'center':'chas'}>],\n       [<AxesSubplot:title={'center':'nox'}>,\n        <AxesSubplot:title={'center':'rm'}>,\n        <AxesSubplot:title={'center':'age'}>,\n        <AxesSubplot:title={'center':'dis'}>],\n       [<AxesSubplot:title={'center':'rad'}>,\n        <AxesSubplot:title={'center':'tax'}>,\n        <AxesSubplot:title={'center':'ptratio'}>,\n        <AxesSubplot:title={'center':'b'}>],\n       [<AxesSubplot:title={'center':'lstat'}>,\n        <AxesSubplot:title={'center':'medv'}>, <AxesSubplot:>,\n        <AxesSubplot:>]], dtype=object)"
     },
     "metadata": {},
     "execution_count": 9
    },
    {
     "output_type": "display_data",
     "data": {
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "dataset.hist(bins=25,figsize=(20,8))"
   ]
  },
  {
   "source": [
    "## Correlation Matrix of the features"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "     column  Correlation with medv\n0      medv               1.000000\n1        rm               0.695360\n2        zn               0.335582\n3         b               0.333461\n4       dis               0.249929\n5      chas               0.175260\n6       age              -0.376955\n7       rad              -0.381626\n8      crim              -0.388305\n9       nox              -0.427321\n10      tax              -0.468536\n11    indus              -0.483725\n12  ptratio              -0.507787\n13    lstat              -0.737663",
      "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>column</th>\n      <th>Correlation with medv</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>medv</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>rm</td>\n      <td>0.695360</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>zn</td>\n      <td>0.335582</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>b</td>\n      <td>0.333461</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>dis</td>\n      <td>0.249929</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>chas</td>\n      <td>0.175260</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>age</td>\n      <td>-0.376955</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>rad</td>\n      <td>-0.381626</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>crim</td>\n      <td>-0.388305</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>nox</td>\n      <td>-0.427321</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>tax</td>\n      <td>-0.468536</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>indus</td>\n      <td>-0.483725</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>ptratio</td>\n      <td>-0.507787</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>lstat</td>\n      <td>-0.737663</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "    correlation_matrix = dataset.corr()\n",
    "corr = correlation_matrix['medv'].sort_values(ascending=False)\n",
    "correlation_dataframe = pd.DataFrame({'column': corr.index,\n",
    "                 'Correlation with medv': corr.values})\n",
    "correlation_dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<AxesSubplot:>"
     },
     "metadata": {},
     "execution_count": 11
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x720 with 2 Axes>",
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "fig = plt.subplots(figsize=(10,10))\n",
    "sns.heatmap(correlation_matrix,vmax=1,square=True,annot=True,cmap='Blues')"
   ]
  },
  {
   "source": [
    "## Feature Engineering using various Scaller Algorithm"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "label = dataset['medv'] #--> Target Prediksi\n",
    "feature_used = dataset.drop(['medv',],axis=1) #--> Fitur yang digunakan adalah selain fitur 'quality'\n",
    "\n",
    "# Scalling Data\n",
    "scaler_1 = StandardScaler(with_mean=True,with_std=True,copy=True)\n",
    "scaler_2 = PowerTransformer(method='yeo-johnson', standardize=True)\n",
    "scaler_3 = PowerTransformer(method='box-cox',standardize=True)\n",
    "scaler_4 = QuantileTransformer(n_quantiles=100,output_distribution='uniform', random_state=304)\n",
    "scaler_5 = MinMaxScaler(feature_range=(0,1),copy=True)\n",
    "scaler_6 = MaxAbsScaler(copy=True)\n",
    "scaler_7 = RobustScaler(with_centering=True,with_scaling=True,quantile_range=(25.0,75.0))\n",
    "\n",
    "x_1 = scaler_1.fit_transform(feature_used.values)\n",
    "y_1 = scaler_1.fit_transform(label.values.reshape(-1,1)).flatten()\n",
    "\n",
    "x_2 = scaler_2.fit_transform(feature_used.values)\n",
    "y_2 = scaler_2.fit_transform(label.values.reshape(-1,1)).flatten()\n",
    "\n",
    "#x_3 = scaler_3.fit_transform(feature_used.values)\n",
    "#y_3 = scaler_3.fit_transform(label.values.reshape(-1,1)).flatten()\n",
    "\n",
    "x_4 = scaler_4.fit_transform(feature_used.values)\n",
    "y_4 = scaler_4.fit_transform(label.values.reshape(-1,1)).flatten()\n",
    "\n",
    "x_5 = scaler_5.fit_transform(feature_used.values)\n",
    "y_5 = scaler_5.fit_transform(label.values.reshape(-1,1)).flatten()\n",
    "\n",
    "x_6 = scaler_6.fit_transform(feature_used.values)\n",
    "y_6 = scaler_6.fit_transform(label.values.reshape(-1,1)).flatten()\n",
    "\n",
    "x_7 = scaler_7.fit_transform(feature_used.values)\n",
    "y_7 = scaler_7.fit_transform(label.values.reshape(-1,1)).flatten()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scores(test,prediction):\n",
    "    print(\"skor r2 : {:.3f}\".format(r2_score(test,prediction)))\n",
    "    print(\"skor mse : {:.3f}\".format(mean_squared_error(test,prediction)))\n",
    "    print(\"skor mea : {:.3f}\".format(mean_absolute_error(test,prediction)))\n",
    "    print(\"skor RMSE : {:.3f}\".format(np.sqrt(mean_squared_error(test,prediction))))"
   ]
  },
  {
   "source": [
    "## Not using box-cox scaller because its only works in data with absolute positive value"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "## Scaler Power transformer menggunakan box-cox tidak bisa digunakan karena data pada fitur terdapat nilai 0. Box-cox hanya bisa digunakan dengan data yang benar-benar positif ( nilai > 0)\n",
    "\n",
    "Solusinya adalah dengan menghapus atau mengganti nilai 0 pada fitur"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0.87805489 0.53825112 0.78632918 0.7902404  0.78068251 0.76893236\n 0.66670026 0.7505459  0.71733367 0.55049417]\nskor r2 : 0.672\nskor mse : 0.387\nskor mea : 0.422\nskor RMSE : 0.622\n"
    },
    {
     "output_type": "execute_result",
     "data": {
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     "metadata": {},
     "execution_count": 21
    },
    {
     "output_type": "display_data",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Menggunakan Standard Scaler\n",
    "feature_train, feature_test, label_train, label_test = train_test_split(x_1, y_1, test_size = 0.25, random_state=10)\n",
    "linear_reg = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n",
    "linear_reg.fit(feature_train,label_train)\n",
    "cross_val = cross_val_score(linear_reg, feature_train, label_train, cv=10)\n",
    "print(cross_val)\n",
    "predictions = linear_reg.predict(feature_test)\n",
    "scores(label_test,predictions)\n",
    "plt.scatter(label_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0.88563824 0.76616605 0.85270371 0.72191244 0.75749961 0.68807163\n 0.5429042  0.81061679 0.64169994 0.62934548]\nskor r2 : 0.767\nskor mse : 0.237\nskor mea : 0.352\nskor RMSE : 0.487\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x1c746c68508>"
     },
     "metadata": {},
     "execution_count": 15
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Menggunakan Power Transform 'yeo-jhonson'\n",
    "feature_train, feature_test, label_train, label_test = train_test_split(x_2, y_2, test_size = 0.25, random_state=10)\n",
    "linear_reg = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n",
    "linear_reg.fit(feature_train,label_train)\n",
    "cross_val = cross_val_score(linear_reg, feature_train, label_train, cv=10)\n",
    "print(cross_val)\n",
    "predictions = linear_reg.predict(feature_test)\n",
    "scores(label_test,predictions)\n",
    "plt.scatter(label_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0.87946308 0.77665517 0.82974224 0.70830983 0.83490071 0.80216023\n 0.6531498  0.835979   0.67167217 0.75440609]\nskor r2 : 0.767\nskor mse : 0.019\nskor mea : 0.104\nskor RMSE : 0.139\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x1c747447748>"
     },
     "metadata": {},
     "execution_count": 16
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Menggunakan Quantile Transform \n",
    "feature_train, feature_test, label_train, label_test = train_test_split(x_4, y_4, test_size = 0.25, random_state=10)\n",
    "linear_reg = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n",
    "linear_reg.fit(feature_train,label_train)\n",
    "cross_val = cross_val_score(linear_reg, feature_train, label_train, cv=10)\n",
    "print(cross_val)\n",
    "predictions = linear_reg.predict(feature_test)\n",
    "scores(label_test,predictions)\n",
    "plt.scatter(label_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0.87805489 0.53825112 0.78632918 0.7902404  0.78068251 0.76893236\n 0.66670026 0.7505459  0.71733367 0.55049417]\nskor r2 : 0.672\nskor mse : 0.016\nskor mea : 0.086\nskor RMSE : 0.127\n"
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Menggunakan min-max Transform \n",
    "feature_train, feature_test, label_train, label_test = train_test_split(x_5, y_5, test_size = 0.25, random_state=10)\n",
    "linear_reg = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n",
    "linear_reg.fit(feature_train,label_train)\n",
    "cross_val = cross_val_score(linear_reg, feature_train,label_train, cv=10)\n",
    "print(cross_val)\n",
    "predictions = linear_reg.predict(feature_test)\n",
    "scores(label_test,predictions)\n",
    "plt.scatter(label_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0.87805489 0.53825112 0.78632918 0.7902404  0.78068251 0.76893236\n 0.66670026 0.7505459  0.71733367 0.55049417]\nskor r2 : 0.672\nskor mse : 0.013\nskor mea : 0.078\nskor RMSE : 0.114\n"
    },
    {
     "output_type": "execute_result",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Menggunakan max absolute scaller\n",
    "feature_train, feature_test, label_train, label_test = train_test_split(x_6, y_6, test_size = 0.25, random_state=10)\n",
    "linear_reg = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n",
    "linear_reg.fit(feature_train,label_train)\n",
    "cross_val = cross_val_score(linear_reg,feature_train,label_train, cv=10)\n",
    "print(cross_val)              \n",
    "predictions = linear_reg.predict(feature_test)\n",
    "scores(label_test,predictions)\n",
    "plt.scatter(label_test,predictions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0.87805489 0.53825112 0.78632918 0.7902404  0.78068251 0.76893236\n 0.66670026 0.7505459  0.71733367 0.55049417]\nskor r2 : 0.672\nskor mse : 0.514\nskor mea : 0.486\nskor RMSE : 0.717\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x1c7474b0188>"
     },
     "metadata": {},
     "execution_count": 19
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "# Menggunakan robust scaller\n",
    "feature_train, feature_test, label_train, label_test = train_test_split(x_7, y_7, test_size = 0.25, random_state=10)\n",
    "linear_reg = LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)\n",
    "linear_reg.fit(feature_train,label_train)\n",
    "cross_val = cross_val_score(linear_reg,feature_train,label_train, cv=10)\n",
    "print(cross_val)\n",
    "predictions = linear_reg.predict(feature_test)\n",
    "scores(label_test,predictions)\n",
    "plt.scatter(label_test,predictions)"
   ]
  },
  {
   "source": [
    "# RandomForestRegressor"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0.92064164 0.85119395 0.92894017 0.84472419 0.91281663 0.85186975\n 0.84558982 0.75588182 0.8619529  0.67638659]\nskor r2 : 0.863\nskor mse : 0.162\nskor mea : 0.284\nskor RMSE : 0.402\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x1c74a6ae708>"
     },
     "metadata": {},
     "execution_count": 20
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "feature_train, feature_test, label_train, label_test = train_test_split(x_1, y_1, test_size = 0.25, random_state=10)\n",
    "rfr = RandomForestRegressor(n_estimators=100,criterion='mse',max_features='auto')\n",
    "rfr.fit(feature_train,label_train)\n",
    "cross_val = cross_val_score(rfr,feature_train,label_train, cv=10)\n",
    "print(cross_val)\n",
    "predictions = rfr.predict(feature_test)\n",
    "scores(label_test,predictions)\n",
    "plt.scatter(label_test,predictions)"
   ]
  },
  {
   "source": [
    "# XGBoost Regressor"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "[0.90424763 0.95637236 0.93168454 0.87727884 0.92792508 0.86076715\n 0.81777862 0.8497531  0.79059608 0.84376911]\nskor r2 : 0.879\nskor mse : 0.123\nskor mea : 0.247\nskor RMSE : 0.350\n"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x1c74b609c08>"
     },
     "metadata": {},
     "execution_count": 28
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "feature_train, feature_test, label_train, label_test = train_test_split(x_2, y_2, test_size = 0.25, random_state=10)\n",
    "xgb = xgboost.XGBRegressor(colsample_bytree=0.4,\n",
    "                 gamma=0,                 \n",
    "                 learning_rate=0.01,\n",
    "                 max_depth=3,\n",
    "                 min_child_weight=1.5,\n",
    "                 n_estimators=10000,                                                                    \n",
    "                 reg_alpha=0.75,\n",
    "                 reg_lambda=0.45,\n",
    "                 subsample=0.6,\n",
    "                 seed=42)\n",
    "\n",
    "xgb.fit(feature_train,label_train)\n",
    "cross_val = cross_val_score(xgb,feature_train,label_train, cv=10)\n",
    "print(cross_val)\n",
    "predictions = xgb.predict(feature_test)\n",
    "scores(label_test,predictions)\n",
    "plt.scatter(label_test,predictions)"
   ]
  },
  {
   "source": [
    "# SVM"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Menggunakan kernel poly\n[ 0.8374486   0.71925968  0.83779142  0.71458441  0.83101008  0.82372829\n  0.716815   -0.55774665  0.74902551  0.47134418]\nskor r2 : 0.749\nskor mse : 0.296\nskor mea : 0.377\nskor RMSE : 0.544\n\nMenggunakan kernel linear\n[0.87579929 0.42643032 0.77363881 0.68360404 0.78812394 0.82056092\n 0.6744046  0.75444944 0.73584066 0.46374484]\nskor r2 : 0.636\nskor mse : 0.430\nskor mea : 0.419\nskor RMSE : 0.656\n\nMenggunakan kernel rbf\n[0.92566043 0.71709293 0.85868322 0.92311935 0.84614553 0.87962061\n 0.85272492 0.88847469 0.67985736 0.48183709]\nskor r2 : 0.767\nskor mse : 0.276\nskor mea : 0.313\nskor RMSE : 0.525\n\n"
    }
   ],
   "source": [
    "feature_train, feature_test, label_train, label_test = train_test_split(x_1, y_1, test_size = 0.25, random_state=10)\n",
    "kernels =['poly','linear','rbf']\n",
    "for index,value in enumerate(kernels):\n",
    "    print(\"Menggunakan kernel \"+str(value))\n",
    "    svm = SVR(kernel=value)\n",
    "    svm.fit(feature_train,label_train)\n",
    "    cross_val = cross_val_score(svm,feature_train,label_train, cv=10)\n",
    "    print(cross_val)\n",
    "    predictions = svm.predict(feature_test)\n",
    "    scores(label_test,predictions)\n",
    "    print()"
   ]
  },
  {
   "source": [
    "# KNN Regressor"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": "Menggunakan nilai k :1\n[0.81707834 0.34006161 0.67271995 0.81706285 0.74247909 0.84818522\n 0.80184612 0.46727518 0.65541074 0.47862701]\nskor r2 : 0.703\nskor mse : 0.351\nskor mea : 0.369\nskor RMSE : 0.592\n\nMenggunakan nilai k :3\n[0.87744962 0.61327867 0.72046279 0.89735798 0.75579719 0.83497242\n 0.8610733  0.85752591 0.807469   0.39792735]\nskor r2 : 0.799\nskor mse : 0.237\nskor mea : 0.322\nskor RMSE : 0.487\n\nMenggunakan nilai k :5\n[0.86337701 0.70599861 0.76526167 0.91031063 0.77744384 0.8379338\n 0.82626589 0.84470274 0.80234432 0.37426428]\nskor r2 : 0.747\nskor mse : 0.298\nskor mea : 0.348\nskor RMSE : 0.546\n\nMenggunakan nilai k :7\n[0.89123994 0.67939034 0.80039189 0.89431418 0.76630988 0.84849727\n 0.81355603 0.86320821 0.80205542 0.40935646]\nskor r2 : 0.740\nskor mse : 0.307\nskor mea : 0.354\nskor RMSE : 0.554\n\nMenggunakan nilai k :9\n[0.90812001 0.62199406 0.81250715 0.8936771  0.78831209 0.83420468\n 0.79667137 0.85818436 0.77381669 0.40636247]\nskor r2 : 0.733\nskor mse : 0.315\nskor mea : 0.367\nskor RMSE : 0.561\n\nMenggunakan nilai k :11\n[0.90748679 0.58320172 0.82712059 0.86783047 0.80076774 0.83402513\n 0.80242544 0.85762843 0.75096817 0.38635735]\nskor r2 : 0.726\nskor mse : 0.323\nskor mea : 0.372\nskor RMSE : 0.569\n\nMenggunakan nilai k :13\n[0.90557551 0.56707314 0.81625693 0.85022655 0.79421744 0.8253763\n 0.79527543 0.87179483 0.72613212 0.36558854]\nskor r2 : 0.719\nskor mse : 0.332\nskor mea : 0.377\nskor RMSE : 0.576\n\nMenggunakan nilai k :15\n[0.90262525 0.55182399 0.79274221 0.83046978 0.77676858 0.81624737\n 0.79709173 0.8694848  0.71303968 0.35882107]\nskor r2 : 0.712\nskor mse : 0.340\nskor mea : 0.384\nskor RMSE : 0.583\n\n"
    }
   ],
   "source": [
    "feature_train, feature_test, label_train, label_test = train_test_split(x_1, y_1, test_size = 0.25, random_state=10)\n",
    "numbers =16\n",
    "for i in range(numbers):\n",
    "    if i % 2 !=0:\n",
    "        print(\"Menggunakan nilai k :\"+str(i))\n",
    "        knn = KNeighborsRegressor(n_neighbors=i,algorithm='auto',weights='distance')\n",
    "        knn.fit(feature_train,label_train)\n",
    "        cross_val = cross_val_score(knn,feature_train,label_train, cv=10)\n",
    "        print(cross_val)\n",
    "        predictions = knn.predict(feature_test)\n",
    "        scores(label_test,predictions)\n",
    "        print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}