boston_regression_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.datasets import load_boston\n",
    "from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier\n",
    "from sklearn.svm import SVR, SVC\n",
    "from sklearn.model_selection import train_test_split\n",
    "%matplotlib inline\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "boston_dataset = load_boston()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "    </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>0.0</td>\n",
       "      <td>0.538</td>\n",
       "      <td>6.575</td>\n",
       "      <td>65.2</td>\n",
       "      <td>4.0900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>296.0</td>\n",
       "      <td>15.3</td>\n",
       "      <td>396.90</td>\n",
       "      <td>4.98</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>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>6.421</td>\n",
       "      <td>78.9</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>396.90</td>\n",
       "      <td>9.14</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>0.0</td>\n",
       "      <td>0.469</td>\n",
       "      <td>7.185</td>\n",
       "      <td>61.1</td>\n",
       "      <td>4.9671</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.0</td>\n",
       "      <td>17.8</td>\n",
       "      <td>392.83</td>\n",
       "      <td>4.03</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>6.998</td>\n",
       "      <td>45.8</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>394.63</td>\n",
       "      <td>2.94</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>0.0</td>\n",
       "      <td>0.458</td>\n",
       "      <td>7.147</td>\n",
       "      <td>54.2</td>\n",
       "      <td>6.0622</td>\n",
       "      <td>3.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>18.7</td>\n",
       "      <td>396.90</td>\n",
       "      <td>5.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
       "0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n",
       "1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n",
       "2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n",
       "3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n",
       "4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n",
       "\n",
       "   PTRATIO       B  LSTAT  \n",
       "0     15.3  396.90   4.98  \n",
       "1     17.8  396.90   9.14  \n",
       "2     17.8  392.83   4.03  \n",
       "3     18.7  394.63   2.94  \n",
       "4     18.7  396.90   5.33  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['MEDV'] = boston_dataset.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 14 columns):\n",
      "CRIM       506 non-null float64\n",
      "ZN         506 non-null float64\n",
      "INDUS      506 non-null float64\n",
      "CHAS       506 non-null float64\n",
      "NOX        506 non-null float64\n",
      "RM         506 non-null float64\n",
      "AGE        506 non-null float64\n",
      "DIS        506 non-null float64\n",
      "RAD        506 non-null float64\n",
      "TAX        506 non-null float64\n",
      "PTRATIO    506 non-null float64\n",
      "B          506 non-null float64\n",
      "LSTAT      506 non-null float64\n",
      "MEDV       506 non-null float64\n",
      "dtypes: float64(14)\n",
      "memory usage: 55.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>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>3.677083</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>"
      ],
      "text/plain": [
       "             CRIM          ZN       INDUS        CHAS         NOX          RM  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean     3.613524   11.363636   11.136779    0.069170    0.554695    6.284634   \n",
       "std      8.601545   23.322453    6.860353    0.253994    0.115878    0.702617   \n",
       "min      0.006320    0.000000    0.460000    0.000000    0.385000    3.561000   \n",
       "25%      0.082045    0.000000    5.190000    0.000000    0.449000    5.885500   \n",
       "50%      0.256510    0.000000    9.690000    0.000000    0.538000    6.208500   \n",
       "75%      3.677083   12.500000   18.100000    0.000000    0.624000    6.623500   \n",
       "max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n",
       "\n",
       "              AGE         DIS         RAD         TAX     PTRATIO           B  \\\n",
       "count  506.000000  506.000000  506.000000  506.000000  506.000000  506.000000   \n",
       "mean    68.574901    3.795043    9.549407  408.237154   18.455534  356.674032   \n",
       "std     28.148861    2.105710    8.707259  168.537116    2.164946   91.294864   \n",
       "min      2.900000    1.129600    1.000000  187.000000   12.600000    0.320000   \n",
       "25%     45.025000    2.100175    4.000000  279.000000   17.400000  375.377500   \n",
       "50%     77.500000    3.207450    5.000000  330.000000   19.050000  391.440000   \n",
       "75%     94.075000    5.188425   24.000000  666.000000   20.200000  396.225000   \n",
       "max    100.000000   12.126500   24.000000  711.000000   22.000000  396.900000   \n",
       "\n",
       "            LSTAT        MEDV  \n",
       "count  506.000000  506.000000  \n",
       "mean    12.653063   22.532806  \n",
       "std      7.141062    9.197104  \n",
       "min      1.730000    5.000000  \n",
       "25%      6.950000   17.025000  \n",
       "50%     11.360000   21.200000  \n",
       "75%     16.955000   25.000000  \n",
       "max     37.970000   50.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns='MEDV')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['MEDV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfr = RandomForestRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "           max_features='auto', max_leaf_nodes=None,\n",
       "           min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "           min_samples_leaf=1, min_samples_split=2,\n",
       "           min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "           oob_score=False, random_state=None, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9724229199082925"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfr.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "svr = SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,\n",
       "  gamma='auto_deprecated', kernel='rbf', max_iter=-1, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.14280359783816798"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svr.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    506.000000\n",
       "mean      22.532806\n",
       "std        9.197104\n",
       "min        5.000000\n",
       "25%       17.025000\n",
       "50%       21.200000\n",
       "75%       25.000000\n",
       "max       50.000000\n",
       "Name: MEDV, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MEDV'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a2525ad68>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df['MEDV'].plot.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0, 10, 20, 30, 40, 50, 60])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bins = np.arange(0, 70, 10)\n",
    "bins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 20, 30, 40, 50, 60])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels = np.arange(10, 61, 10)\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>MEDV</th>\n",
       "      <th>MEDV_CAP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>24.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>21.6</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34.7</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33.4</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>36.2</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>28.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>22.9</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>27.1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>16.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>18.9</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>18.9</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>20.4</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>18.2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>19.9</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23.1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>20.2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>18.2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>13.6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19.6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>14.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>15.6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>13.9</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>16.6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>14.8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>18.4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>21.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>476</th>\n",
       "      <td>16.7</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477</th>\n",
       "      <td>12.0</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>14.6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>479</th>\n",
       "      <td>21.4</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>480</th>\n",
       "      <td>23.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>481</th>\n",
       "      <td>23.7</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>482</th>\n",
       "      <td>25.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>21.8</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>20.6</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>21.2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>486</th>\n",
       "      <td>19.1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>20.6</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>15.2</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>7.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>8.1</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>13.6</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>20.1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>21.8</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>24.5</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>23.1</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>19.7</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>18.3</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>21.2</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>17.5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>16.8</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>22.4</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>20.6</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>23.9</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>22.0</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>11.9</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>506 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     MEDV MEDV_CAP\n",
       "0    24.0       30\n",
       "1    21.6       30\n",
       "2    34.7       40\n",
       "3    33.4       40\n",
       "4    36.2       40\n",
       "5    28.7       30\n",
       "6    22.9       30\n",
       "7    27.1       30\n",
       "8    16.5       20\n",
       "9    18.9       20\n",
       "10   15.0       20\n",
       "11   18.9       20\n",
       "12   21.7       30\n",
       "13   20.4       30\n",
       "14   18.2       20\n",
       "15   19.9       20\n",
       "16   23.1       30\n",
       "17   17.5       20\n",
       "18   20.2       30\n",
       "19   18.2       20\n",
       "20   13.6       20\n",
       "21   19.6       20\n",
       "22   15.2       20\n",
       "23   14.5       20\n",
       "24   15.6       20\n",
       "25   13.9       20\n",
       "26   16.6       20\n",
       "27   14.8       20\n",
       "28   18.4       20\n",
       "29   21.0       30\n",
       "..    ...      ...\n",
       "476  16.7       20\n",
       "477  12.0       20\n",
       "478  14.6       20\n",
       "479  21.4       30\n",
       "480  23.0       30\n",
       "481  23.7       30\n",
       "482  25.0       30\n",
       "483  21.8       30\n",
       "484  20.6       30\n",
       "485  21.2       30\n",
       "486  19.1       20\n",
       "487  20.6       30\n",
       "488  15.2       20\n",
       "489   7.0       10\n",
       "490   8.1       10\n",
       "491  13.6       20\n",
       "492  20.1       30\n",
       "493  21.8       30\n",
       "494  24.5       30\n",
       "495  23.1       30\n",
       "496  19.7       20\n",
       "497  18.3       20\n",
       "498  21.2       30\n",
       "499  17.5       20\n",
       "500  16.8       20\n",
       "501  22.4       30\n",
       "502  20.6       30\n",
       "503  23.9       30\n",
       "504  22.0       30\n",
       "505  11.9       20\n",
       "\n",
       "[506 rows x 2 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MEDV_CAP'] = pd.cut(df['MEDV'], bins=bins, labels=labels)\n",
    "df[['MEDV','MEDV_CAP']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(columns=['MEDV', 'MEDV_CAP'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df['MEDV_CAP']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "rfc = RandomForestClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
       "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
       "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "            min_samples_leaf=1, min_samples_split=2,\n",
       "            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=None,\n",
       "            oob_score=False, random_state=None, verbose=0,\n",
       "            warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9882005899705014"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rfc.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc = SVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
       "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
       "  shrinking=True, tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9941002949852508"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svc.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}