HousePricePrediction.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c35870f1",
   "metadata": {},
   "source": [
    "# Importing the Required libraries :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "79c9be8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7d12389",
   "metadata": {},
   "source": [
    "# Importing the dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d2cd0c9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('Housing.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "28b8a1d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12250000</td>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10850000</td>\n",
       "      <td>7500</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10150000</td>\n",
       "      <td>8580</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>semi-furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10150000</td>\n",
       "      <td>16200</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>0</td>\n",
       "      <td>no</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9870000</td>\n",
       "      <td>8100</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>2</td>\n",
       "      <td>yes</td>\n",
       "      <td>furnished</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9800000</td>\n",
       "      <td>5750</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>yes</td>\n",
       "      <td>yes</td>\n",
       "      <td>no</td>\n",
       "      <td>no</td>\n",
       "      <td>yes</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "      <td>unfurnished</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price   area  bedrooms  bathrooms  stories mainroad guestroom basement  \\\n",
       "0  13300000   7420         4          2        3      yes        no       no   \n",
       "1  12250000   8960         4          4        4      yes        no       no   \n",
       "2  12250000   9960         3          2        2      yes        no      yes   \n",
       "3  12215000   7500         4          2        2      yes        no      yes   \n",
       "4  11410000   7420         4          1        2      yes       yes      yes   \n",
       "5  10850000   7500         3          3        1      yes        no      yes   \n",
       "6  10150000   8580         4          3        4      yes        no       no   \n",
       "7  10150000  16200         5          3        2      yes        no       no   \n",
       "8   9870000   8100         4          1        2      yes       yes      yes   \n",
       "9   9800000   5750         3          2        4      yes       yes       no   \n",
       "\n",
       "  hotwaterheating airconditioning  parking prefarea furnishingstatus  \n",
       "0              no             yes        2      yes        furnished  \n",
       "1              no             yes        3       no        furnished  \n",
       "2              no              no        2      yes   semi-furnished  \n",
       "3              no             yes        3      yes        furnished  \n",
       "4              no             yes        2       no        furnished  \n",
       "5              no             yes        2      yes   semi-furnished  \n",
       "6              no             yes        2      yes   semi-furnished  \n",
       "7              no              no        0       no      unfurnished  \n",
       "8              no             yes        2      yes        furnished  \n",
       "9              no             yes        1      yes      unfurnished  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# FIRST 10 ROWS OF THE DATASET\n",
    "\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6b624072",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['price', 'area', 'bedrooms', 'bathrooms', 'stories', 'mainroad',\n",
       "       'guestroom', 'basement', 'hotwaterheating', 'airconditioning',\n",
       "       'parking', 'prefarea', 'furnishingstatus'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# COLUMNS :\n",
    "\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cbf5593b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545, 13)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# SIZE OF THE DATASET \n",
    "\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "497d76b5",
   "metadata": {},
   "source": [
    "**As we can see our dataset has 545 rows and 13 columns.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fbc83246",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 545 entries, 0 to 544\n",
      "Data columns (total 13 columns):\n",
      " #   Column            Non-Null Count  Dtype \n",
      "---  ------            --------------  ----- \n",
      " 0   price             545 non-null    int64 \n",
      " 1   area              545 non-null    int64 \n",
      " 2   bedrooms          545 non-null    int64 \n",
      " 3   bathrooms         545 non-null    int64 \n",
      " 4   stories           545 non-null    int64 \n",
      " 5   mainroad          545 non-null    object\n",
      " 6   guestroom         545 non-null    object\n",
      " 7   basement          545 non-null    object\n",
      " 8   hotwaterheating   545 non-null    object\n",
      " 9   airconditioning   545 non-null    object\n",
      " 10  parking           545 non-null    int64 \n",
      " 11  prefarea          545 non-null    object\n",
      " 12  furnishingstatus  545 non-null    object\n",
      "dtypes: int64(6), object(7)\n",
      "memory usage: 55.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# DATA TYPES OF THE COLUMNS OF THE DATASET\n",
    "\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fa95d77",
   "metadata": {},
   "source": [
    "# Data Preprocessing :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "64fc1a37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "price               0\n",
       "area                0\n",
       "bedrooms            0\n",
       "bathrooms           0\n",
       "stories             0\n",
       "mainroad            0\n",
       "guestroom           0\n",
       "basement            0\n",
       "hotwaterheating     0\n",
       "airconditioning     0\n",
       "parking             0\n",
       "prefarea            0\n",
       "furnishingstatus    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CHECKING FOR NULL VALUES\n",
    "\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "09f559c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1mInference:\u001b[0m The dataset doesn't have any duplicates\n"
     ]
    }
   ],
   "source": [
    "# REMOVAL OF DUPLICATE VALUE \n",
    "\n",
    "counter = 0\n",
    "rs,cs = df.shape\n",
    "\n",
    "df.drop_duplicates(inplace=True)\n",
    "\n",
    "if df.shape==(rs,cs):\n",
    "    print('\\n\\033[1mInference:\\033[0m The dataset doesn\\'t have any duplicates')\n",
    "else:\n",
    "    print(f'\\n\\033[1mInference:\\033[0m Number of duplicates dropped/fixed ---> {rs-df.shape[0]}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4342bbc4",
   "metadata": {},
   "source": [
    "**No null values present our dataset.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "eb6d52c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CONVERTING ALL OUR CATEGORICAL DATA COLUMNS TO NUMERIC FORM\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "categ = [\"mainroad\",\"guestroom\",\"basement\",\"hotwaterheating\",\"airconditioning\",\"prefarea\",\"furnishingstatus\"]\n",
    "\n",
    "# Encode Categorical Columns\n",
    "le = LabelEncoder()\n",
    "df[categ] = df[categ].apply(le.fit_transform)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c8d8c2bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>13300000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12250000</td>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12215000</td>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11410000</td>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price  area  bedrooms  bathrooms  stories  mainroad  guestroom  \\\n",
       "0  13300000  7420         4          2        3         1          0   \n",
       "1  12250000  8960         4          4        4         1          0   \n",
       "2  12250000  9960         3          2        2         1          0   \n",
       "3  12215000  7500         4          2        2         1          0   \n",
       "4  11410000  7420         4          1        2         1          1   \n",
       "\n",
       "   basement  hotwaterheating  airconditioning  parking  prefarea  \\\n",
       "0         0                0                1        2         1   \n",
       "1         0                0                1        3         0   \n",
       "2         1                0                0        2         1   \n",
       "3         1                0                1        3         1   \n",
       "4         1                0                1        2         0   \n",
       "\n",
       "   furnishingstatus  \n",
       "0                 0  \n",
       "1                 0  \n",
       "2                 1  \n",
       "3                 0  \n",
       "4                 0  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# AFTER CONVERTING OUR DATASET LOOKS LIKE THIS ...\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9cf675e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1200x700 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# CORRELATION BETWEEN THE COLUMNS\n",
    "\n",
    "corr = df.corr()\n",
    "plt.figure(figsize=(12,7))\n",
    "sns.heatmap(corr,cmap='coolwarm',annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be6dfaef",
   "metadata": {},
   "source": [
    "# Spliting The Dataset :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0e060771",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['price'],axis=1)\n",
    "y = df['price']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "06a729e4",
   "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>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
       "      <th>guestroom</th>\n",
       "      <th>basement</th>\n",
       "      <th>hotwaterheating</th>\n",
       "      <th>airconditioning</th>\n",
       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8960</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9960</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7500</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7420</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>3000</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>541</th>\n",
       "      <td>2400</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>542</th>\n",
       "      <td>3620</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>543</th>\n",
       "      <td>2910</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>3850</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>545 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     area  bedrooms  bathrooms  stories  mainroad  guestroom  basement  \\\n",
       "0    7420         4          2        3         1          0         0   \n",
       "1    8960         4          4        4         1          0         0   \n",
       "2    9960         3          2        2         1          0         1   \n",
       "3    7500         4          2        2         1          0         1   \n",
       "4    7420         4          1        2         1          1         1   \n",
       "..    ...       ...        ...      ...       ...        ...       ...   \n",
       "540  3000         2          1        1         1          0         1   \n",
       "541  2400         3          1        1         0          0         0   \n",
       "542  3620         2          1        1         1          0         0   \n",
       "543  2910         3          1        1         0          0         0   \n",
       "544  3850         3          1        2         1          0         0   \n",
       "\n",
       "     hotwaterheating  airconditioning  parking  prefarea  furnishingstatus  \n",
       "0                  0                1        2         1                 0  \n",
       "1                  0                1        3         0                 0  \n",
       "2                  0                0        2         1                 1  \n",
       "3                  0                1        3         1                 0  \n",
       "4                  0                1        2         0                 0  \n",
       "..               ...              ...      ...       ...               ...  \n",
       "540                0                0        2         0                 2  \n",
       "541                0                0        0         0                 1  \n",
       "542                0                0        0         0                 2  \n",
       "543                0                0        0         0                 0  \n",
       "544                0                0        0         0                 2  \n",
       "\n",
       "[545 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3c761825",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      13300000\n",
       "1      12250000\n",
       "2      12250000\n",
       "3      12215000\n",
       "4      11410000\n",
       "         ...   \n",
       "540     1820000\n",
       "541     1767150\n",
       "542     1750000\n",
       "543     1750000\n",
       "544     1750000\n",
       "Name: price, Length: 545, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d547d3bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "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=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "1451327d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(381, 164)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# LENGTH OF X_train AND X_test\n",
    "\n",
    "len(X_train),len(X_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef2ddbfe",
   "metadata": {},
   "source": [
    "# Linear Regression :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "904efb96",
   "metadata": {},
   "outputs": [],
   "source": [
    "# IMPORTING THE MODULE\n",
    "\n",
    "from sklearn.linear_model import LinearRegression\n",
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "91e3225c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</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 fitted 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 fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LinearRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LinearRegression.html\">?<span>Documentation for LinearRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LinearRegression()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# FITTING THE DATA INTO THE MODEL\n",
    "\n",
    "model.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "25925073",
   "metadata": {},
   "outputs": [],
   "source": [
    "# PREDICTING THE OUTCOMES\n",
    "\n",
    "y_predict = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "97df504d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5407508.87024418, 7097185.46706855, 3055462.44314053,\n",
       "       4476945.19636315, 3315983.65663579, 3618373.03255259,\n",
       "       5758111.46044028, 6466502.43909126, 2830273.16469119,\n",
       "       2588804.65810567, 9649589.31414054, 2830606.51113843,\n",
       "       3048137.62898116, 3392779.60203048, 3823232.9673009 ,\n",
       "       5358170.87034031, 2955016.41578148, 4836054.53230682,\n",
       "       4603068.47740645, 3551464.60674927, 5625018.82657786,\n",
       "       5796938.54363456, 2758483.74755246, 4873266.20950521,\n",
       "       5600804.93370716, 7772078.63540938, 3381536.16270183,\n",
       "       5370732.06725796, 8352665.9587942 , 3406110.06934798,\n",
       "       6335677.41367624, 3427228.10570008, 6740746.88053742,\n",
       "       4205633.93578768, 3624702.80095917, 5797171.46441145,\n",
       "       5080025.13346592, 4386055.52335342, 3070137.54474224,\n",
       "       4635050.40917587, 4743419.55702887, 3433682.48420934,\n",
       "       7076940.4807988 , 4096598.07073101, 3741261.35302813,\n",
       "       4308416.36745432, 6678982.6364043 , 4092649.04459023,\n",
       "       3872211.05471678, 3687383.17722361, 7462374.91109114,\n",
       "       2898324.62920572, 4501494.58592862, 4427073.78264695,\n",
       "       3822457.27350851, 2641947.74706375, 7510739.56714418,\n",
       "       2940944.89650582, 4246613.52617664, 2796696.1591366 ,\n",
       "       5048338.58815569, 3582935.78133612, 5255053.95042848,\n",
       "       4255235.59619518, 4807607.51782257, 4711232.00096704,\n",
       "       7260531.24221025, 3577762.50878024, 6285890.98464552,\n",
       "       6318431.41494408, 4758297.02517639, 5066345.6283754 ,\n",
       "       4660923.85280285, 8066891.08353479, 3498363.85433616,\n",
       "       5429714.24082061, 4058399.55113095, 4441034.13232771,\n",
       "       4816316.06017677, 4040152.44618202, 7752648.28119992,\n",
       "       4172277.59889826, 6705184.89500425, 5431743.8402273 ,\n",
       "       2763801.26664556, 7145185.10824616, 2626368.70533511,\n",
       "       3708769.99925747, 8020894.34927403, 8360086.48106035,\n",
       "       3255839.86876679, 6099608.09004818, 3610311.65133947,\n",
       "       3666255.62947034, 7909304.88416764, 4708264.41070267,\n",
       "       5200345.04442612, 6430338.84553524, 4954759.03354751,\n",
       "       5980566.29859787, 3959705.24306595, 6578815.16383057,\n",
       "       3699022.87139188, 6086458.26574741, 5225872.35919378,\n",
       "       4461872.63697472, 7065795.84374116, 6430992.76558985,\n",
       "       6577488.25056946, 7181092.29436555, 7132469.27644316,\n",
       "       4969951.60846033, 3988509.66451833, 3282579.03980776,\n",
       "       4284449.11400227, 3307885.27264494, 2794018.0999277 ,\n",
       "       2314211.92533476, 3639260.72788702, 4017775.19574567,\n",
       "       3783639.23978665, 4997528.13318575, 3920039.36114686,\n",
       "       4086116.99024392, 6051867.67437964, 2314854.63439944,\n",
       "       5648580.65216443, 3388208.4469447 , 8106887.37935499,\n",
       "       6945211.4619021 , 3290813.6187236 , 4943358.78455695,\n",
       "       7021438.17367802, 6633943.09586481, 3257272.80697058,\n",
       "       2346620.41017362, 3128074.82457597, 5602713.80008826,\n",
       "       3052041.70375378, 3931847.49328003, 2574372.82187877,\n",
       "       4011784.5084254 , 5232639.95388041, 4105734.9218601 ,\n",
       "       4188942.50390193, 2421273.7970433 , 6767186.36585123,\n",
       "       5302303.58194732, 6807894.55657294, 7246680.49265498,\n",
       "       5468336.15502   , 4606361.04221866, 3769887.60312207,\n",
       "       3860336.84228787, 3308041.40432698, 7268912.41470939,\n",
       "       8146912.63449662, 4013563.22451008, 7210577.89436119,\n",
       "       3796902.64702948, 4130262.35836178, 7058248.99034354,\n",
       "       5417344.25448755, 5180833.66432885])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3e1f2371",
   "metadata": {},
   "source": [
    "# Evaluation :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3194da17",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import r2_score,mean_absolute_error\n",
    "score = r2_score(y_test,y_predict)\n",
    "mae = mean_absolute_error(y_test,y_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a5d593f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6435419628959107"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1cb8b2c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "925543.548315656"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "74635022",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
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   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
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