lab7_linear_regression.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(545, 13)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('https://raw.githubusercontent.com/Ataullha/CSE476-Machine-Learning-Lab/main/Housing.csv')\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>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",
       "  </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",
       "\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  "
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "scrolled": true
   },
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>parking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>5.450000e+02</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "      <td>545.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.766729e+06</td>\n",
       "      <td>5150.541284</td>\n",
       "      <td>2.965138</td>\n",
       "      <td>1.286239</td>\n",
       "      <td>1.805505</td>\n",
       "      <td>0.693578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.870440e+06</td>\n",
       "      <td>2170.141023</td>\n",
       "      <td>0.738064</td>\n",
       "      <td>0.502470</td>\n",
       "      <td>0.867492</td>\n",
       "      <td>0.861586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.750000e+06</td>\n",
       "      <td>1650.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.430000e+06</td>\n",
       "      <td>3600.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>4.340000e+06</td>\n",
       "      <td>4600.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.740000e+06</td>\n",
       "      <td>6360.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>16200.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              price          area    bedrooms   bathrooms     stories  \\\n",
       "count  5.450000e+02    545.000000  545.000000  545.000000  545.000000   \n",
       "mean   4.766729e+06   5150.541284    2.965138    1.286239    1.805505   \n",
       "std    1.870440e+06   2170.141023    0.738064    0.502470    0.867492   \n",
       "min    1.750000e+06   1650.000000    1.000000    1.000000    1.000000   \n",
       "25%    3.430000e+06   3600.000000    2.000000    1.000000    1.000000   \n",
       "50%    4.340000e+06   4600.000000    3.000000    1.000000    2.000000   \n",
       "75%    5.740000e+06   6360.000000    3.000000    2.000000    2.000000   \n",
       "max    1.330000e+07  16200.000000    6.000000    4.000000    4.000000   \n",
       "\n",
       "          parking  \n",
       "count  545.000000  \n",
       "mean     0.693578  \n",
       "std      0.861586  \n",
       "min      0.000000  \n",
       "25%      0.000000  \n",
       "50%      0.000000  \n",
       "75%      1.000000  \n",
       "max      3.000000  "
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "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": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Label Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "le = LabelEncoder()\n",
    "\n",
    "df['mainroad'] = le.fit_transform(df['mainroad'])\n",
    "df['guestroom'] = le.fit_transform(df['guestroom'])\n",
    "df['basement'] = le.fit_transform(df['basement'])\n",
    "df['hotwaterheating'] = le.fit_transform(df['hotwaterheating'])\n",
    "\n",
    "df['airconditioning'] = le.fit_transform(df['airconditioning'])\n",
    "df['prefarea'] = le.fit_transform(df['prefarea'])\n",
    "df['furnishingstatus'] = le.fit_transform(df['furnishingstatus'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<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>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>12250000</td>\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>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": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.drop(['price'], axis=1)\n",
    "y = df.price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(545, 12) (545,)\n"
     ]
    }
   ],
   "source": [
    "print(X.shape, y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=57)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th></th>\n",
       "      <th>area</th>\n",
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       "      <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>195</th>\n",
       "      <td>4410</td>\n",
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       "      <th>443</th>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>6540</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>3970</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>402</th>\n",
       "      <td>5880</td>\n",
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       "      <td>0</td>\n",
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       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     area  bedrooms  bathrooms  stories  mainroad  guestroom  basement  \\\n",
       "195  4410         4          3        2         1          0         1   \n",
       "443  4320         3          1        1         0          0         0   \n",
       "120  6540         3          1        1         1          1         1   \n",
       "528  3970         1          1        1         0          0         0   \n",
       "402  5880         2          1        1         1          0         0   \n",
       "\n",
       "     hotwaterheating  airconditioning  parking  prefarea  furnishingstatus  \n",
       "195                0                0        2         0                 1  \n",
       "443                0                0        1         0                 2  \n",
       "120                0                0        2         1                 0  \n",
       "528                0                0        0         0                 2  \n",
       "402                0                0        0         0                 2  "
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "<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",
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       "      <th>parking</th>\n",
       "      <th>prefarea</th>\n",
       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>417</th>\n",
       "      <td>3640</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "      <th>178</th>\n",
       "      <td>7085</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</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>125</th>\n",
       "      <td>15600</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>7231</td>\n",
       "      <td>3</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>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>3986</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      area  bedrooms  bathrooms  stories  mainroad  guestroom  basement  \\\n",
       "417   3640         2          1        1         1          0         0   \n",
       "178   7085         3          1        1         1          1         1   \n",
       "125  15600         3          1        1         1          0         0   \n",
       "121   7231         3          1        2         1          1         1   \n",
       "446   3986         2          2        1         0          1         1   \n",
       "\n",
       "     hotwaterheating  airconditioning  parking  prefarea  furnishingstatus  \n",
       "417                0                0        0         0                 0  \n",
       "178                0                0        2         1                 1  \n",
       "125                0                1        2         0                 1  \n",
       "121                0                1        0         1                 1  \n",
       "446                0                0        1         0                 2  "
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6646491982744086"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>price</th>\n",
       "      <th>area</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>stories</th>\n",
       "      <th>mainroad</th>\n",
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       "      <th>hotwaterheating</th>\n",
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       "      <th>parking</th>\n",
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       "      <th>furnishingstatus</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.535997</td>\n",
       "      <td>0.366494</td>\n",
       "      <td>0.517545</td>\n",
       "      <td>0.420712</td>\n",
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       "      <td>-0.304721</td>\n",
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       "    <tr>\n",
       "      <th>area</th>\n",
       "      <td>0.535997</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151858</td>\n",
       "      <td>0.193820</td>\n",
       "      <td>0.083996</td>\n",
       "      <td>0.288874</td>\n",
       "      <td>0.140297</td>\n",
       "      <td>0.047417</td>\n",
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       "      <td>0.222393</td>\n",
       "      <td>0.352980</td>\n",
       "      <td>0.234779</td>\n",
       "      <td>-0.171445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bedrooms</th>\n",
       "      <td>0.366494</td>\n",
       "      <td>0.151858</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.373930</td>\n",
       "      <td>0.408564</td>\n",
       "      <td>-0.012033</td>\n",
       "      <td>0.080549</td>\n",
       "      <td>0.097312</td>\n",
       "      <td>0.046049</td>\n",
       "      <td>0.160603</td>\n",
       "      <td>0.139270</td>\n",
       "      <td>0.079023</td>\n",
       "      <td>-0.123244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>bathrooms</th>\n",
       "      <td>0.517545</td>\n",
       "      <td>0.193820</td>\n",
       "      <td>0.373930</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.326165</td>\n",
       "      <td>0.042398</td>\n",
       "      <td>0.126469</td>\n",
       "      <td>0.102106</td>\n",
       "      <td>0.067159</td>\n",
       "      <td>0.186915</td>\n",
       "      <td>0.177496</td>\n",
       "      <td>0.063472</td>\n",
       "      <td>-0.143559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stories</th>\n",
       "      <td>0.420712</td>\n",
       "      <td>0.083996</td>\n",
       "      <td>0.408564</td>\n",
       "      <td>0.326165</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.121706</td>\n",
       "      <td>0.043538</td>\n",
       "      <td>-0.172394</td>\n",
       "      <td>0.018847</td>\n",
       "      <td>0.293602</td>\n",
       "      <td>0.045547</td>\n",
       "      <td>0.044425</td>\n",
       "      <td>-0.104672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mainroad</th>\n",
       "      <td>0.296898</td>\n",
       "      <td>0.288874</td>\n",
       "      <td>-0.012033</td>\n",
       "      <td>0.042398</td>\n",
       "      <td>0.121706</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.092337</td>\n",
       "      <td>0.044002</td>\n",
       "      <td>-0.011781</td>\n",
       "      <td>0.105423</td>\n",
       "      <td>0.204433</td>\n",
       "      <td>0.199876</td>\n",
       "      <td>-0.156726</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>guestroom</th>\n",
       "      <td>0.255517</td>\n",
       "      <td>0.140297</td>\n",
       "      <td>0.080549</td>\n",
       "      <td>0.126469</td>\n",
       "      <td>0.043538</td>\n",
       "      <td>0.092337</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.372066</td>\n",
       "      <td>-0.010308</td>\n",
       "      <td>0.138179</td>\n",
       "      <td>0.037466</td>\n",
       "      <td>0.160897</td>\n",
       "      <td>-0.118328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>basement</th>\n",
       "      <td>0.187057</td>\n",
       "      <td>0.047417</td>\n",
       "      <td>0.097312</td>\n",
       "      <td>0.102106</td>\n",
       "      <td>-0.172394</td>\n",
       "      <td>0.044002</td>\n",
       "      <td>0.372066</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>0.047341</td>\n",
       "      <td>0.051497</td>\n",
       "      <td>0.228083</td>\n",
       "      <td>-0.112831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hotwaterheating</th>\n",
       "      <td>0.093073</td>\n",
       "      <td>-0.009229</td>\n",
       "      <td>0.046049</td>\n",
       "      <td>0.067159</td>\n",
       "      <td>0.018847</td>\n",
       "      <td>-0.011781</td>\n",
       "      <td>-0.010308</td>\n",
       "      <td>0.004385</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.130023</td>\n",
       "      <td>0.067864</td>\n",
       "      <td>-0.059411</td>\n",
       "      <td>-0.031628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>airconditioning</th>\n",
       "      <td>0.452954</td>\n",
       "      <td>0.222393</td>\n",
       "      <td>0.160603</td>\n",
       "      <td>0.186915</td>\n",
       "      <td>0.293602</td>\n",
       "      <td>0.105423</td>\n",
       "      <td>0.138179</td>\n",
       "      <td>0.047341</td>\n",
       "      <td>-0.130023</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.159173</td>\n",
       "      <td>0.117382</td>\n",
       "      <td>-0.150477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parking</th>\n",
       "      <td>0.384394</td>\n",
       "      <td>0.352980</td>\n",
       "      <td>0.139270</td>\n",
       "      <td>0.177496</td>\n",
       "      <td>0.045547</td>\n",
       "      <td>0.204433</td>\n",
       "      <td>0.037466</td>\n",
       "      <td>0.051497</td>\n",
       "      <td>0.067864</td>\n",
       "      <td>0.159173</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.091627</td>\n",
       "      <td>-0.177539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>prefarea</th>\n",
       "      <td>0.329777</td>\n",
       "      <td>0.234779</td>\n",
       "      <td>0.079023</td>\n",
       "      <td>0.063472</td>\n",
       "      <td>0.044425</td>\n",
       "      <td>0.199876</td>\n",
       "      <td>0.160897</td>\n",
       "      <td>0.228083</td>\n",
       "      <td>-0.059411</td>\n",
       "      <td>0.117382</td>\n",
       "      <td>0.091627</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.107686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>furnishingstatus</th>\n",
       "      <td>-0.304721</td>\n",
       "      <td>-0.171445</td>\n",
       "      <td>-0.123244</td>\n",
       "      <td>-0.143559</td>\n",
       "      <td>-0.104672</td>\n",
       "      <td>-0.156726</td>\n",
       "      <td>-0.118328</td>\n",
       "      <td>-0.112831</td>\n",
       "      <td>-0.031628</td>\n",
       "      <td>-0.150477</td>\n",
       "      <td>-0.177539</td>\n",
       "      <td>-0.107686</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     price      area  bedrooms  bathrooms   stories  mainroad  \\\n",
       "price             1.000000  0.535997  0.366494   0.517545  0.420712  0.296898   \n",
       "area              0.535997  1.000000  0.151858   0.193820  0.083996  0.288874   \n",
       "bedrooms          0.366494  0.151858  1.000000   0.373930  0.408564 -0.012033   \n",
       "bathrooms         0.517545  0.193820  0.373930   1.000000  0.326165  0.042398   \n",
       "stories           0.420712  0.083996  0.408564   0.326165  1.000000  0.121706   \n",
       "mainroad          0.296898  0.288874 -0.012033   0.042398  0.121706  1.000000   \n",
       "guestroom         0.255517  0.140297  0.080549   0.126469  0.043538  0.092337   \n",
       "basement          0.187057  0.047417  0.097312   0.102106 -0.172394  0.044002   \n",
       "hotwaterheating   0.093073 -0.009229  0.046049   0.067159  0.018847 -0.011781   \n",
       "airconditioning   0.452954  0.222393  0.160603   0.186915  0.293602  0.105423   \n",
       "parking           0.384394  0.352980  0.139270   0.177496  0.045547  0.204433   \n",
       "prefarea          0.329777  0.234779  0.079023   0.063472  0.044425  0.199876   \n",
       "furnishingstatus -0.304721 -0.171445 -0.123244  -0.143559 -0.104672 -0.156726   \n",
       "\n",
       "                  guestroom  basement  hotwaterheating  airconditioning  \\\n",
       "price              0.255517  0.187057         0.093073         0.452954   \n",
       "area               0.140297  0.047417        -0.009229         0.222393   \n",
       "bedrooms           0.080549  0.097312         0.046049         0.160603   \n",
       "bathrooms          0.126469  0.102106         0.067159         0.186915   \n",
       "stories            0.043538 -0.172394         0.018847         0.293602   \n",
       "mainroad           0.092337  0.044002        -0.011781         0.105423   \n",
       "guestroom          1.000000  0.372066        -0.010308         0.138179   \n",
       "basement           0.372066  1.000000         0.004385         0.047341   \n",
       "hotwaterheating   -0.010308  0.004385         1.000000        -0.130023   \n",
       "airconditioning    0.138179  0.047341        -0.130023         1.000000   \n",
       "parking            0.037466  0.051497         0.067864         0.159173   \n",
       "prefarea           0.160897  0.228083        -0.059411         0.117382   \n",
       "furnishingstatus  -0.118328 -0.112831        -0.031628        -0.150477   \n",
       "\n",
       "                   parking  prefarea  furnishingstatus  \n",
       "price             0.384394  0.329777         -0.304721  \n",
       "area              0.352980  0.234779         -0.171445  \n",
       "bedrooms          0.139270  0.079023         -0.123244  \n",
       "bathrooms         0.177496  0.063472         -0.143559  \n",
       "stories           0.045547  0.044425         -0.104672  \n",
       "mainroad          0.204433  0.199876         -0.156726  \n",
       "guestroom         0.037466  0.160897         -0.118328  \n",
       "basement          0.051497  0.228083         -0.112831  \n",
       "hotwaterheating   0.067864 -0.059411         -0.031628  \n",
       "airconditioning   0.159173  0.117382         -0.150477  \n",
       "parking           1.000000  0.091627         -0.177539  \n",
       "prefarea          0.091627  1.000000         -0.107686  \n",
       "furnishingstatus -0.177539 -0.107686          1.000000  "
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cor = df.corr()\n",
    "cor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 8))\n",
    "sns.heatmap(cor, annot=True, cmap='RdBu')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.ndarray'> (12,)\n"
     ]
    }
   ],
   "source": [
    "print(type(model.coef_), model.coef_.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "[w1, w2, w3, w4, w5, w6, w7, w8, w9, w10, w11, w12] = model.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "243.6853688361755"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   area  bedrooms  bathrooms  stories  mainroad  guestroom  basement  \\\n",
       "0  7420         4          2        3         1          0         0   \n",
       "\n",
       "   hotwaterheating  airconditioning  parking  prefarea  furnishingstatus  \n",
       "0                0                1        2         1                 0  "
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.loc[[0]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7420,    4,    2,    3,    1,    0,    0,    0,    1,    2,    1,\n",
       "           0]], dtype=int64)"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.loc[[0]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "[[x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11, x12]] = X.loc[[0]].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "143879.87174374796"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = model.intercept_\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = w1*x1 + w2*x2 + w3*x3 + w4*x4 + w5*x5 + w6*x6 + w7*x7 + w8*x8 + w9*x9 + w10*x10 + w11*x11 + w12*x12 + c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8157870.538536482"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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  "language_info": {
   "codemirror_mode": {
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