Boston_house_price_prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2304bbef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the libraries \n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c828acdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0.00632  18.00   2.310  0  0.5380  6.5750  65.20  4.0900   1  296.0  15.30 396.90   4.98  24.00\n",
      "0   0.02731   0.00   7.070  0  0.4690  6.4210  78...                                              \n",
      "1   0.02729   0.00   7.070  0  0.4690  7.1850  61...                                              \n",
      "2   0.03237   0.00   2.180  0  0.4580  6.9980  45...                                              \n",
      "3   0.06905   0.00   2.180  0  0.4580  7.1470  54...                                              \n",
      "4   0.02985   0.00   2.180  0  0.4580  6.4300  58...                                              \n"
     ]
    }
   ],
   "source": [
    "# Importing the Boston Housing dataset\n",
    "df = pd.read_csv(\"C:\\\\boston_house_price\\\\housing.csv\")\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "722f05a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n",
      "0  0.00632  18.0   2.31     0  0.538  6.575  65.2  4.0900    1  296.0   \n",
      "1  0.02731   0.0   7.07     0  0.469  6.421  78.9  4.9671    2  242.0   \n",
      "2  0.02729   0.0   7.07     0  0.469  7.185  61.1  4.9671    2  242.0   \n",
      "3  0.03237   0.0   2.18     0  0.458  6.998  45.8  6.0622    3  222.0   \n",
      "4  0.06905   0.0   2.18     0  0.458  7.147  54.2  6.0622    3  222.0   \n",
      "\n",
      "   PTRATIO       B  LSTAT  MEDV  \n",
      "0     15.3  396.90   4.98  24.0  \n",
      "1     17.8  396.90   9.14  21.6  \n",
      "2     17.8  392.83   4.03  34.7  \n",
      "3     18.7  394.63   2.94  33.4  \n",
      "4     18.7  396.90   5.33  36.2  \n"
     ]
    }
   ],
   "source": [
    "column_names = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT', 'MEDV']\n",
    "df = pd.read_csv(\"C:\\\\boston_house_price\\\\housing.csv\", header=None, delimiter=r\"\\s+\", names=column_names)\n",
    "print(df.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "397d80f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['PRICE'] = df['MEDV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6daa78a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(506, 15)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0cc75201",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX',\n",
       "       'PTRATIO', 'B', 'LSTAT', 'MEDV', 'PRICE'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8d2ad279",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       float64\n",
       "ZN         float64\n",
       "INDUS      float64\n",
       "CHAS         int64\n",
       "NOX        float64\n",
       "RM         float64\n",
       "AGE        float64\n",
       "DIS        float64\n",
       "RAD          int64\n",
       "TAX        float64\n",
       "PTRATIO    float64\n",
       "B          float64\n",
       "LSTAT      float64\n",
       "MEDV       float64\n",
       "PRICE      float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d3d558a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       504\n",
       "ZN          26\n",
       "INDUS       76\n",
       "CHAS         2\n",
       "NOX         81\n",
       "RM         446\n",
       "AGE        356\n",
       "DIS        412\n",
       "RAD          9\n",
       "TAX         66\n",
       "PTRATIO     46\n",
       "B          357\n",
       "LSTAT      455\n",
       "MEDV       229\n",
       "PRICE      229\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6c5e4ec9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CRIM       0\n",
       "ZN         0\n",
       "INDUS      0\n",
       "CHAS       0\n",
       "NOX        0\n",
       "RM         0\n",
       "AGE        0\n",
       "DIS        0\n",
       "RAD        0\n",
       "TAX        0\n",
       "PTRATIO    0\n",
       "B          0\n",
       "LSTAT      0\n",
       "MEDV       0\n",
       "PRICE      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7aa7dd92",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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",
       "      <th>PRICE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [CRIM, ZN, INDUS, CHAS, NOX, RM, AGE, DIS, RAD, TAX, PTRATIO, B, LSTAT, MEDV, PRICE]\n",
       "Index: []"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df56f7a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 506 entries, 0 to 505\n",
      "Data columns (total 15 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    float64\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\n",
      " 14  PRICE    506 non-null    float64\n",
      "dtypes: float64(13), int64(2)\n",
      "memory usage: 59.4 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "12844622",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "</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",
       "      <th>PRICE</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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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",
       "      <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       PRICE  \n",
       "count  506.000000  506.000000  506.000000  \n",
       "mean    12.653063   22.532806   22.532806  \n",
       "std      7.141062    9.197104    9.197104  \n",
       "min      1.730000    5.000000    5.000000  \n",
       "25%      6.950000   17.025000   17.025000  \n",
       "50%     11.360000   21.200000   21.200000  \n",
       "75%     16.955000   25.000000   25.000000  \n",
       "max     37.970000   50.000000   50.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Viewing the data statistics\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "cd91bf8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(15, 15)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = df.corr()\n",
    "corr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "818fea73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x1000 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the heatmap of correlation between features\n",
    "plt.figure(figsize=(15,10))\n",
    "sns.heatmap(df.corr(),annot=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "675475d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Spliting target variable and independent variables\n",
    "X = df.drop(['MEDV'], axis = 1)\n",
    "y = df['MEDV']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "526dcd86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Splitting to training and testing data\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state = 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8157fc7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LinearRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LinearRegression</label><div class=\"sk-toggleable__content\"><pre>LinearRegression()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import library for Linear Regression\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# Create a Linear regressor\n",
    "lm = LinearRegression()\n",
    "\n",
    "# Train the model using the training sets \n",
    "lm.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2a902b58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.7408297026122455e-13"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Value of y intercept\n",
    "lm.intercept_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d31ed84c",
   "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>Attribute</th>\n",
       "      <th>Coefficients</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>CRIM</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ZN</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>INDUS</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHAS</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NOX</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>RM</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>AGE</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>DIS</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>RAD</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>TAX</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>PTRATIO</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>B</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>LSTAT</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>PRICE</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Attribute Coefficients\n",
       "0       CRIM          0.0\n",
       "1         ZN         -0.0\n",
       "2      INDUS          0.0\n",
       "3       CHAS          0.0\n",
       "4        NOX          0.0\n",
       "5         RM          0.0\n",
       "6        AGE         -0.0\n",
       "7        DIS          0.0\n",
       "8        RAD         -0.0\n",
       "9        TAX          0.0\n",
       "10   PTRATIO          0.0\n",
       "11         B          0.0\n",
       "12     LSTAT         -0.0\n",
       "13     PRICE          1.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converting the coefficient values to a dataframe\n",
    "coeffcients = pd.DataFrame([X_train.columns,lm.coef_]).T\n",
    "coeffcients = coeffcients.rename(columns={0: 'Attribute', 1: 'Coefficients'})\n",
    "coeffcients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "37fd05d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model prediction on train data\n",
    "y_pred = lm.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "ea92b7bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 1.0\n",
      "Adjusted R^2: 1.0\n",
      "MAE: 1.3500813775159248e-14\n",
      "MSE: 4.0130958711450947e-28\n",
      "RMSE: 2.003271292447704e-14\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "print('R^2:',metrics.r2_score(y_train, y_pred))\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_train, y_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_train, y_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7a945473",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the differences between actual prices and predicted values\n",
    "plt.scatter(y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fc968532",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking residuals\n",
    "plt.scatter(y_pred,y_train-y_pred)\n",
    "plt.title(\"Predicted vs residuals\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7ad7f68c",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adlin\\AppData\\Local\\Temp\\ipykernel_21584\\3326403628.py:2: UserWarning: \n",
      "\n",
      "`distplot` is a deprecated function and will be removed in seaborn v0.14.0.\n",
      "\n",
      "Please adapt your code to use either `displot` (a figure-level function with\n",
      "similar flexibility) or `histplot` (an axes-level function for histograms).\n",
      "\n",
      "For a guide to updating your code to use the new functions, please see\n",
      "https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751\n",
      "\n",
      "  sns.distplot(y_train-y_pred)\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if pd.api.types.is_categorical_dtype(vector):\n",
      "C:\\ProgramData\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking Normality of errors\n",
    "sns.distplot(y_train-y_pred)\n",
    "plt.title(\"Histogram of Residuals\")\n",
    "plt.xlabel(\"Residuals\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "d2a59a3c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting Test data with the model\n",
    "y_test_pred = lm.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1c0271c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 1.0\n",
      "Adjusted R^2: 1.0\n",
      "MAE: 1.5256801669980442e-14\n",
      "MSE: 4.730518595394079e-28\n",
      "RMSE: 2.1749755390335036e-14\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "acc_linreg = metrics.r2_score(y_test, y_test_pred)\n",
    "print('R^2:', acc_linreg)\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_test_pred))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_test, y_test_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_test, y_test_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ad86778c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestRegressor()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestRegressor</label><div class=\"sk-toggleable__content\"><pre>RandomForestRegressor()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestRegressor()"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import Random Forest Regressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "\n",
    "# Create a Random Forest Regressor\n",
    "reg = RandomForestRegressor()\n",
    "\n",
    "# Train the model using the training sets \n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "96c65cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model prediction on train data\n",
    "y_pred = reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ca07fb21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9999059671291639\n",
      "Adjusted R^2: 0.9999020837657665\n",
      "MAE: 0.03935028248587603\n",
      "MSE: 0.007077932203390115\n",
      "RMSE: 0.08413044754065031\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "print('R^2:',metrics.r2_score(y_train, y_pred))\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_train, y_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_train, y_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "10f0719c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the differences between actual prices and predicted values\n",
    "plt.scatter(y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "34ce80e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking residuals\n",
    "plt.scatter(y_pred,y_train-y_pred)\n",
    "plt.title(\"Predicted vs residuals\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "7e908ea6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting Test data with the model\n",
    "y_test_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "dea2eee2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.998434501557468\n",
      "Adjusted R^2: 0.9982745236144356\n",
      "MAE: 0.16180263157894786\n",
      "MSE: 0.16346946052631878\n",
      "RMSE: 0.4043135670816882\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "acc_rf = metrics.r2_score(y_test, y_test_pred)\n",
    "print('R^2:', acc_rf)\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_test_pred))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_test, y_test_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_test, y_test_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bf4b126f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:335: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:338: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  is_categorical_dtype(dtype) or is_pa_ext_categorical_dtype(dtype)\n",
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:384: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype):\n",
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:359: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n",
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:520: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(data):\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBRegressor</label><div class=\"sk-toggleable__content\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=None, feature_types=None,\n",
       "             gamma=None, grow_policy=None, importance_type=None,\n",
       "             interaction_constraints=None, learning_rate=None, max_bin=None,\n",
       "             max_cat_threshold=None, max_cat_to_onehot=None,\n",
       "             max_delta_step=None, max_depth=None, max_leaves=None,\n",
       "             min_child_weight=None, missing=nan, monotone_constraints=None,\n",
       "             multi_strategy=None, n_estimators=None, n_jobs=None,\n",
       "             num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import XGBoost Regressor\n",
    "from xgboost import XGBRegressor\n",
    "\n",
    "#Create a XGBoost Regressor\n",
    "reg = XGBRegressor()\n",
    "\n",
    "# Train the model using the training sets \n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "233a7991",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:335: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:338: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  is_categorical_dtype(dtype) or is_pa_ext_categorical_dtype(dtype)\n",
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:384: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype):\n",
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:359: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n"
     ]
    }
   ],
   "source": [
    "# Model prediction on train data\n",
    "y_pred = reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "276fb5c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9999999846196728\n",
      "Adjusted R^2: 0.999999983984497\n",
      "MAE: 0.0007401549883480268\n",
      "MSE: 1.1576899942924505e-06\n",
      "RMSE: 0.001075960033780275\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "print('R^2:',metrics.r2_score(y_train, y_pred))\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_train, y_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_train, y_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f379223e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the differences between actual prices and predicted values\n",
    "plt.scatter(y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "045dd152",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking residuals\n",
    "plt.scatter(y_pred,y_train-y_pred)\n",
    "plt.title(\"Predicted vs residuals\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3dacf927",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:335: FutureWarning: is_sparse is deprecated and will be removed in a future version. Check `isinstance(dtype, pd.SparseDtype)` instead.\n",
      "  if is_sparse(dtype):\n",
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:338: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  is_categorical_dtype(dtype) or is_pa_ext_categorical_dtype(dtype)\n",
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:384: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  if is_categorical_dtype(dtype):\n",
      "C:\\Users\\adlin\\AppData\\Roaming\\Python\\Python311\\site-packages\\xgboost\\data.py:359: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
      "  return is_int or is_bool or is_float or is_categorical_dtype(dtype)\n"
     ]
    }
   ],
   "source": [
    "#Predicting Test data with the model\n",
    "y_test_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f360f60a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.9976151146235994\n",
      "Adjusted R^2: 0.9973714037092227\n",
      "MAE: 0.21120242319608984\n",
      "MSE: 0.24902990338768838\n",
      "RMSE: 0.4990289604699194\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "acc_xgb = metrics.r2_score(y_test, y_test_pred)\n",
    "print('R^2:', acc_xgb)\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_test_pred))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_test, y_test_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_test, y_test_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "a9619f15",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating scaled set to be used in model to improve our results\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "sc = StandardScaler()\n",
    "X_train = sc.fit_transform(X_train)\n",
    "X_test = sc.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "e2b3905d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import SVM Regressor\n",
    "from sklearn import svm\n",
    "\n",
    "# Create a SVM Regressor\n",
    "reg = svm.SVR()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "8350c371",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>SVR()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">SVR</label><div class=\"sk-toggleable__content\"><pre>SVR()</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "SVR()"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Train the model using the training sets \n",
    "reg.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "95d41461",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Model prediction on train data\n",
    "y_pred = reg.predict(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "69822c80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.7703450938212612\n",
      "Adjusted R^2: 0.7608608204097498\n",
      "MAE: 1.930182742261485\n",
      "MSE: 17.2863153241632\n",
      "RMSE: 4.157681484212469\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "print('R^2:',metrics.r2_score(y_train, y_pred))\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_train, y_pred))*(len(y_train)-1)/(len(y_train)-X_train.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_train, y_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_train, y_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_train, y_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "1462b7cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the differences between actual prices and predicted values\n",
    "plt.scatter(y_train, y_pred)\n",
    "plt.xlabel(\"Prices\")\n",
    "plt.ylabel(\"Predicted prices\")\n",
    "plt.title(\"Prices vs Predicted prices\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "f409bb33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Checking residuals\n",
    "plt.scatter(y_pred,y_train-y_pred)\n",
    "plt.title(\"Predicted vs residuals\")\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Residuals\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "136b06c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Predicting Test data with the model\n",
    "y_test_pred = reg.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "0ab1c371",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2: 0.7156661455233423\n",
      "Adjusted R^2: 0.6866101311972606\n",
      "MAE: 2.7340918363356947\n",
      "MSE: 29.69016163663131\n",
      "RMSE: 5.448867922479981\n"
     ]
    }
   ],
   "source": [
    "# Model Evaluation\n",
    "acc_svm = metrics.r2_score(y_test, y_test_pred)\n",
    "print('R^2:', acc_svm)\n",
    "print('Adjusted R^2:',1 - (1-metrics.r2_score(y_test, y_test_pred))*(len(y_test)-1)/(len(y_test)-X_test.shape[1]-1))\n",
    "print('MAE:',metrics.mean_absolute_error(y_test, y_test_pred))\n",
    "print('MSE:',metrics.mean_squared_error(y_test, y_test_pred))\n",
    "print('RMSE:',np.sqrt(metrics.mean_squared_error(y_test, y_test_pred)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c25754aa",
   "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>Model</th>\n",
       "      <th>R-squared Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Linear Regression</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Random Forest</td>\n",
       "      <td>99.843450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>XGBoost</td>\n",
       "      <td>99.761511</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Support Vector Machines</td>\n",
       "      <td>71.566615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Model  R-squared Score\n",
       "0        Linear Regression       100.000000\n",
       "1            Random Forest        99.843450\n",
       "2                  XGBoost        99.761511\n",
       "3  Support Vector Machines        71.566615"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "models = pd.DataFrame({\n",
    "    'Model': ['Linear Regression', 'Random Forest', 'XGBoost', 'Support Vector Machines'],\n",
    "    'R-squared Score': [acc_linreg*100, acc_rf*100, acc_xgb*100, acc_svm*100]})\n",
    "models.sort_values(by='R-squared Score', ascending=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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 },
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