Multiple_linear_regression_House_Price (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\HP\\\\Desktop\\\\letsupgrade_python'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\HP\\\\Desktop\\\\letsupgrade_python'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.chdir('C:\\\\Users\\\\HP\\\\Desktop\\\\letsupgrade_python')\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>Id</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Alley</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>NaN</td>\n",
       "      <td>196.0</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>1710</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>548</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>460</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>162.0</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>1786</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>608</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>1717</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>642</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>350.0</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>2198</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>836</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  LotFrontage  LotArea Alley  MasVnrArea  1stFlrSF  2ndFlrSF  GrLivArea  \\\n",
       "0   1         65.0     8450   NaN       196.0       856       854       1710   \n",
       "1   2         80.0     9600   NaN         0.0      1262         0       1262   \n",
       "2   3         68.0    11250   NaN       162.0       920       866       1786   \n",
       "3   4         60.0     9550   NaN         0.0       961       756       1717   \n",
       "4   5         84.0    14260   NaN       350.0      1145      1053       2198   \n",
       "\n",
       "  GarageType  GarageArea  SalePrice  \n",
       "0     Attchd         548     208500  \n",
       "1     Attchd         460     181500  \n",
       "2     Attchd         608     223500  \n",
       "3     Detchd         642     140000  \n",
       "4     Attchd         836     250000  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = pd.read_csv(\"House Price.csv\")\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 11 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Id           1460 non-null   int64  \n",
      " 1   LotFrontage  1201 non-null   float64\n",
      " 2   LotArea      1460 non-null   int64  \n",
      " 3   Alley        91 non-null     object \n",
      " 4   MasVnrArea   1452 non-null   float64\n",
      " 5   1stFlrSF     1460 non-null   int64  \n",
      " 6   2ndFlrSF     1460 non-null   int64  \n",
      " 7   GrLivArea    1460 non-null   int64  \n",
      " 8   GarageType   1379 non-null   object \n",
      " 9   GarageArea   1460 non-null   int64  \n",
      " 10  SalePrice    1460 non-null   int64  \n",
      "dtypes: float64(2), int64(7), object(2)\n",
      "memory usage: 125.6+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1460, 11)\n"
     ]
    }
   ],
   "source": [
    "print(dataset.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                0\n",
       "LotFrontage     259\n",
       "LotArea           0\n",
       "Alley          1369\n",
       "MasVnrArea        8\n",
       "1stFlrSF          0\n",
       "2ndFlrSF          0\n",
       "GrLivArea         0\n",
       "GarageType       81\n",
       "GarageArea        0\n",
       "SalePrice         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check missing value\n",
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>Id</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>196.0</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>1710</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>548</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>460</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>162.0</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>1786</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>608</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>1717</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>642</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>350.0</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>2198</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>836</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  LotFrontage  LotArea  MasVnrArea  1stFlrSF  2ndFlrSF  GrLivArea  \\\n",
       "0   1         65.0     8450       196.0       856       854       1710   \n",
       "1   2         80.0     9600         0.0      1262         0       1262   \n",
       "2   3         68.0    11250       162.0       920       866       1786   \n",
       "3   4         60.0     9550         0.0       961       756       1717   \n",
       "4   5         84.0    14260       350.0      1145      1053       2198   \n",
       "\n",
       "  GarageType  GarageArea  SalePrice  \n",
       "0     Attchd         548     208500  \n",
       "1     Attchd         460     181500  \n",
       "2     Attchd         608     223500  \n",
       "3     Detchd         642     140000  \n",
       "4     Attchd         836     250000  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#Dropping Alley column\n",
    "dataset.drop('Alley', axis = 1, inplace = True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "LotFrontage    259\n",
       "LotArea          0\n",
       "MasVnrArea       8\n",
       "1stFlrSF         0\n",
       "2ndFlrSF         0\n",
       "GrLivArea        0\n",
       "GarageType      81\n",
       "GarageArea       0\n",
       "SalePrice        0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='LotFrontage'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking outlier(outlier)\n",
    "sns.boxplot(y='LotFrontage',data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='MasVnrArea'>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAADrCAYAAACYY7plAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8vihELAAAACXBIWXMAAAsTAAALEwEAmpwYAAAY2klEQVR4nO3df3Bd5X3n8ffHErjxpIyNLIhrmdqJDazNlE3RUiCTbAlSEG2waTtsTZtY3TCjGcax3c52E9xOk3Y23jX9sVtMF3ZcQiKngMck2cWhWLXsDSGZJbgypWts4qLEDggbLF/aKVvA+Erf/eMck2txpXMldO+5V/68Zjz3nOf8+jKD5+NznnOeRxGBmZnZRGblXYCZmdU/h4WZmWVyWJiZWSaHhZmZZXJYmJlZJoeFmZllas67gGqZP39+LF68OO8yzMwayv79+09GROvY9hkbFosXL2ZgYCDvMszMGoqkH5dr92MoMzPL5LAwM7NMDgszM8vksDAzs0xVCwtJD0g6Iem5Me3rJB2WdFDSH5e0b5Q0mG67saT9KkkH0m1bJKlaNZtVW6FQYP369RQKhbxLMZuUat5ZfBXoKm2QdD2wCvi5iFgB/GnavhxYDaxIj7lXUlN62H1AD7As/XPWOc0aSW9vLwcOHGDbtm15l2I2KVULi4h4EnhtTPMdwOaIOJXucyJtXwVsj4hTEXEEGASulrQAuCAinopkLPVtwC3VqtmsmgqFAn19fUQEfX19vruwhlLrPotLgY9KelrSdyT9m7R9IfBSyX5DadvCdHlsu1nD6e3tZXR0FICRkRHfXVhDqXVYNAPzgGuA/wjsSPsgyvVDxATtZUnqkTQgaWB4eHg66jWbNnv27KFYLAJQLBbp7+/PuSKzytU6LIaAb0ZiHzAKzE/bF5Xs1wYcS9vbyrSXFRFbI6I9ItpbW9/1tbpZrjo6OmhuTgZNaG5uprOzM+eKzCpX67D4X8DHASRdCpwPnAR2AqslzZa0hKQje19EHAdel3RNegeyBni0xjWbTYvu7m5mzUr+yjU1NbFmzZqcKzKrXDVfnX0YeAq4TNKQpNuBB4APpq/Tbge607uMg8AO4BDQB6yNiJH0VHcA95N0ev8Q2FWtms2qqaWlha6uLiTR1dVFS0tL3iWZVaxqAwlGxG3jbPrUOPtvAjaVaR8ArpjG0sxy093dzdGjR31XYQ1nxo46a1aPWlpa2LJlS95lmE2ah/swM7NMDgszM8vksDAzs0wOCzMzy+SwMDOzTA4LMzPL5LAwM7NMDgszM8vksDAzs0wOCzMzy+SwMDOzTA4LMzPL5LAwM7NMDgszM8vksDAzs0zVnCnvAUkn0lnxxm77XUkhaX5J20ZJg5IOS7qxpP0qSQfSbVvS6VXNzKyGqnln8VWga2yjpEVAJ/BiSdtyYDWwIj3mXklN6eb7gB6SebmXlTunmZlVV9XCIiKeBF4rs+m/AZ8DoqRtFbA9Ik5FxBGS+bavlrQAuCAinoqIALYBt1SrZrNqKxQKrF+/nkKhkHcpZpNS0z4LSSuBlyPi78dsWgi8VLI+lLYtTJfHto93/h5JA5IGhoeHp6lqs+nT29vLgQMH2LZtW96lmE1KzcJC0hzg94EvlNtcpi0maC8rIrZGRHtEtLe2tk6tULMqKRQK9PX1ERH09fX57sIaSi3vLD4ELAH+XtJRoA14RtIHSO4YFpXs2wYcS9vbyrSbNZze3l5GR0cBGBkZ8d2FNZSahUVEHIiIiyJicUQsJgmCn4+IV4CdwGpJsyUtIenI3hcRx4HXJV2TvgW1Bni0VjWbTac9e/ZQLBYBKBaL9Pf351yRWeWq+ersw8BTwGWShiTdPt6+EXEQ2AEcAvqAtRExkm6+A7ifpNP7h8CuatVsVk0dHR00NzcD0NzcTGdnZ84VmVVOyUtGM097e3sMDAzkXYbZOwqFArfddhtvv/02s2fP5qGHHqKlpSXvsszOIml/RLSPbfcX3GY10tLSQldXF5Lo6upyUFhDac67ALNzSXd3N0ePHmXNmjV5l2I2KQ4LsxpqaWlhy5YteZdhNml+DGVmZpkcFmZmlslhYWZmmRwWZmaWyWFhZmaZHBZmZpbJYWFmZpkcFmZmlslhYWZmmRwWZmaWyWFhZmaZHBZmZpbJYWFmZpmqOVPeA5JOSHqupO1PJP1A0v+V9D8lzS3ZtlHSoKTDkm4sab9K0oF025Z0elUzM6uhat5ZfBXoGtPWD1wRET8H/AOwEUDScmA1sCI95l5JTekx9wE9JPNyLytzTjMzq7KqhUVEPAm8NqZtd0QU09XvA23p8ipge0SciogjJPNtXy1pAXBBRDwVyfyv24BbqlWzmZmVl2efxWeAXenyQuClkm1DadvCdHlse1mSeiQNSBoYHh6e5nLN3rtCocD69espFAp5l2I2KbmEhaTfB4rAg2eayuwWE7SXFRFbI6I9ItpbW1vfe6Fm06y3t5cDBw6wbdu2vEsxm5Sah4WkbuCTwG+mj5YguWNYVLJbG3AsbW8r027WcAqFAn19fUQEfX19vruwhlLTsJDUBXweWBkRb5Rs2gmsljRb0hKSjux9EXEceF3SNelbUGuAR2tZs9l06e3tZXR0FICRkRHfXVhDqearsw8DTwGXSRqSdDvwF8BPA/2SnpX0PwAi4iCwAzgE9AFrI2IkPdUdwP0knd4/5Cf9HGYNZc+ePRSLyfsdxWKR/v7+nCsyq1xztU4cEbeVaf7yBPtvAjaVaR8ArpjG0sxy0dHRweOPP06xWKS5uZnOzs68SzKrmL/gNquR7u7udx5DjY6OsmbNmpwrMqucw8LMzDI5LMxqpLSDe3R01B3c1lAcFmY1MrZDe/fu3TlVYjZ5DguzGrn44osnXDerZw4Lsxp59dVXJ1w3q2cOC7Ma6ezs5MwI+5L4xCc+kXNFZpVzWJjVSHd3N83NyadN5513nl+dtYbisDCrkZaWFm666SYkcdNNN9HS0pJ3SWYVc1iY1dDKlSuZM2cON998c96lmE2Kw8Kshnbu3Mkbb7zBt771rbxLMZsUh4VZjZQOUb5r1y4PUW4NxWFhViO9vb2cPn0agNOnT/sLbmsoDguzGunv7+fMfF8R4S+4raE4LMxqxF9wWyOr5uRHD0g6Iem5krYLJfVLeiH9nVeybaOkQUmHJd1Y0n6VpAPpti0681WTWYPxF9zWyCoOC0m/LOlzkr5w5k/GIV8Fusa03QnsjYhlwN50HUnLgdXAivSYeyU1pcfcB/SQTLW6rMw5zRqCv+C2RlZRWKTTn/46sA4QcCvwsxMdExFPAq+NaV4F9KbLvcAtJe3bI+JURBwhmUL1akkLgAsi4qlIHvZuKznGrKH4C25rZJXeWVwXEWuAf4yIPwKuBRZN4XoXR8RxgPT3orR9IfBSyX5DadvCdHlsu1nDaWlp4frrrwfg+uuv9xfc1lAqDYs30983JP0McBpYMo11lOuHiAnay59E6pE0IGlgeHh42oozmy7ucrNGVWlYPCZpLvAnwDPAUWD7FK73avpoifT3RNo+xNl3Km3AsbS9rUx7WRGxNSLaI6K9tbV1CuWZVU+hUODb3/42AE888YQ/yrOGUlFYRMR/ioh/iohvkPRVXB4RfzCF6+0EutPlbuDRkvbVkmZLWkLSkb0vfVT1uqRr0reg1pQcY9ZQSqdVHRkZ8Ud51lAq7eCeI+kPJP1lRJwCLpL0yYxjHgaeAi6TNCTpdmAz0CnpBaAzXSciDgI7gENAH7A2IkbSU90B3E/S6f1DYNdk/yPN6sGePXsoFosAFIvFd02zalbPmivc7yvAfpKObUgeDz0CPDbeARFx2zibbhhn/03ApjLtA8AVFdZpVrc6Ojp4/PHHKRaLNDc309nZmXdJZhWrtM/iQxHxxyQd20TEm5TvfDazcXR3dzNrVvJXrqmpya/OWkOpNCzelvQ+0jeRJH0IOFW1qsxmoJaWFq677joArr32Wr86aw2l0sdQXyTpS1gk6UHgI8BvVasos5lqcHDwrF+zRpEZFpJmAfOAXwWuIXn8tCEiTla5NrMZZXBwkKGh5BvToaEhBgcHWbp0ac5VmVUm8zFURIwCn42IQkT8dUQ85qAwm7wvfelLE66b1bNK+yz6Jf2upEXpyLEXSrqwqpWZzTBHjx6dcN2snlXaZ/GZ9HdtSVsAH5zecsxmrra2tnceQ51ZN2sUFYVFRLxrHChJ509/OWYz19KlS88KC/dXWCOZ1ORHSnxc0v2cPUqsmWXYt2/fhOtm9azS4T5+QdLdwI9JxnH6LnB5NQszm2k++tGPTrhuVs8mDAtJm9JxnP4zcAD4MDAcEb0R8Y+1KNBspnjrrbfOWj91yt+1WuPI6rPoAQ6TTG36WES8JWnc+STMbHzf+973zlr/7ne/m1MlZpOX9RjqAySD+60EBiV9DXifpErfojKz1NiJjzwRkjWSCcMiIkYiYlc6pepSkrkk/g/wsqSHalGg2Uxxww03TLhuVs8yO7glzZL07yLirYj4ekT8GsnkRH9T/fLMZo6enp537iYk0dPTk3NFZpWreLiPMW3/HBG9VavKbAZqaWl550O8trY2jzprDSWX4T4k/Y6kg5Kek/SwpJ9Kz9kv6YX0d17J/hslDUo6LOnGqV7XLE+FQoFXXnkFgFdeecVzcFtDqTQsPkMy1MeTJDPm7QcGpnJBSQuB9UB7RFwBNAGrgTuBvRGxDNibriNpebp9BdAF3CupaSrXNstTb28vp0+fBuD06dOeg9saSkVhERFLyvx5L+NCNfOTt6rmAMeAVcCZR1u9wC3p8ipge0SciogjJHNxX/0erm2Wi7Fzbu/evTunSswmr+JXYCVdBywuPSYiJv1Po4h4WdKfAi8CbwK7I2K3pIsj4ni6z3FJF6WHLAS+X3KKobTNrKG0tLScNTbU/Pnzc6zGbHIqCov0+4oPAc8CI2lzAJMOi7QvYhWwBPgn4BFJn5rokDJtZT8MlNRD8iEhl1xyyWRLM6uq48ePn7V+7NixnCoxm7xK7yzageURMR1fb3cARyJiGEDSN4HrgFclLUjvKhYAJ9L9h4BFJce3kTy2epeI2ApsBWhvb/eX5mZm06TSDu7nSL7mng4vAtdImqPkpfMbgOdJBijsTvfpJvkAkLR9taTZkpaQfOPh4Tqt4cybN2/CdbN6VumdxXzgkKR9wDujn0XEysleMCKelvR14BmgCPwdyd3A+4Edkm4nCZRb0/0PStoBHEr3XxsRI2VPblbHTp48OeG6WT2rNCz+cDovGhFfBL44pvkUyV1Guf03kYxRZWZmOZgwLCT9BfBQRHynRvWYmVkdyuqzeAH4M0lHJd0l6V/XoCazGcmjzlojyxp19u6IuBb4t8BrwFckPS/pC5IurUmFZjPEwoVnfx50Zpwos0ZQ6RfcP46IuyLiw8BvAL9C8gaTmVVo7FhQ7uC2RlLpHNznSbpZ0oPALuAfgF+ramVmM8zHPvaxCdfN6llWB3cncBvwSeBpYDvQExH/UoPazGaU6fmm1SwfWXcWvwc8BVweETdHxIMOCrOp8Rzc1siyOrivj4i/BOZJmg0g6RclrZc0txYFms0UHR0dZ613dnbmVInZ5FU63Mc3gBFJS4EvkwwC6Dm4zSZh5cqzBzy4+eabc6rEbPIqDYvRiCiSvAX15xHxO8CC6pVlNvM88sgjE66b1bNKw+K0pNtIBvh7LG07rzolmc1MYyc/GrtuVs8qDYt/D1wLbIqII+nor39VvbLMZp7R0dEJ183qWUUDCUbEIZJ5s8+sHwE2V6soMzOrL5XOlLcM+C/AcuCnzrS/x3m4zcysQVT6GOorwH0k80lcTzKd6teqVZSZmdWXSsPifRGxF1A6TtQfAh+vXllmZlZPKg2LtyTNAl6Q9FlJvwJcNNWLSpor6euSfpCOYnutpAsl9Ut6If2dV7L/RkmDkg5LunGq1zUzs6mpNCx+G5hD0sl9FfBpfjJf9lTcDfRFxOXAlSQj2N4J7I2IZcDedB1Jy4HVwAqgC7hXUtN7uLaZmU1SpW9D/W26+P9IXqOdMkkXAB8Dfis999vA25JWAb+Y7tYLPAF8HlgFbI+IU8ARSYPA1SRjVpmZWQ1kjTq7c6LtEbFyou3j+CAwTDKR0pXAfmADcHFEHE/Pe1zSmcdcC4Hvlxw/lLaVq7cH6AG45JJLplCamZmVk3VncS3wEvAwyRDl0zEPZDPw88C6iHha0t2kj5zGUe6aZcd6joitwFaA9vZ2jwdtZjZNsvosPkAyTPkVJP0MncDJiPhORHxnitccAoYi4ul0/esk4fGqpAUA6e+Jkv0XlRzfBhyb4rXNzGwKsoYoH4mIvojoBq4BBoEnJK2b6gUj4hXgJUmXpU03AIeAnfyk07wbeDRd3gmsljQ7HWZkGbBvqtc3M7PJy+zgTuex+GWSGfMWA1uAb77H664DHpR0PvAjkk7zWcAOSbcDLwK3AkTEQUk7SAKlCKyNiJH3eH0zM5uErA7uXpJHULuAP4qI56bjohHxLNBeZtMN4+y/Cdg0Hdc2M7PJy7qz+DTwL8ClwHrpnb5mARERF1SxNjMzqxMThkVEVPrRnpllmD9/PidPnjxr3axROAzMaqQ0KMqtm9Uzh4WZmWVyWJiZWSaHhZmZZXJYmJlZJoeFmZllcliYmVkmh4WZmWVyWJiZWSaHhZmZZXJYmJlZJoeFmZllcliYmVmm3MJCUpOkv5P0WLp+oaR+SS+kv/NK9t0oaVDSYUk35lWzmdm5KnOmvCraADwPnJkT405gb0RslnRnuv55ScuB1cAK4GeAPZIu9Wx5jeOee+5hcHAw7zLq0oYNG/IuIVdLly5l3bopz9JsNZTLnYWkNpKpWu8vaV4F9KbLvcAtJe3bI+JURBwhmQf86hqVamZm5Hdn8efA54CfLmm7OCKOA0TEcUkXpe0Lge+X7DeUtlmD8L8cEx0dHRSLxXfWm5ubufvuu3OsyKxyNb+zkPRJ4ERE7K/0kDJtMc65eyQNSBoYHh6eco1m1bB58+az1u+6666cKjGbvDweQ30EWCnpKLAd+LikvwJelbQAIP09ke4/BCwqOb4NOFbuxBGxNSLaI6K9tbW1WvWbTUl7e/s7y83NzVx11VU5VmM2OTUPi4jYGBFtEbGYpOP6f0fEp4CdQHe6WzfwaLq8E1gtabakJcAyYF+NyzabFkuWLAF8V2GNJ8+3ocbaDOyQdDvwInArQEQclLQDOAQUgbV+E8oa1QUXXMCVV17puwprOLmGRUQ8ATyRLheAG8bZbxOwqWaFmZnZWfwFt5mZZXJYmJlZJoeFmZllcliYmVkmh4WZmWVyWJiZWSaHhZmZZXJYmJlZJoeFmZllcliYmVkmh4WZmWVyWJiZWSaHhZmZZXJYmJlZJoeFmZllcliYmVmmmoeFpEWSvi3peUkHJW1I2y+U1C/phfR3XskxGyUNSjos6cZa12xmdq7L486iCPyHiPhXwDXAWknLgTuBvRGxDNibrpNuWw2sALqAeyU15VC3mdk5q+ZhERHHI+KZdPl14HlgIbAK6E136wVuSZdXAdsj4lREHAEGgatrWrSZ2Tku1z4LSYuBDwNPAxdHxHFIAgW4KN1tIfBSyWFDaZuZmdVIbmEh6f3AN4Dfjoh/nmjXMm0xzjl7JA1IGhgeHp6OMs3MjJzCQtJ5JEHxYER8M21+VdKCdPsC4ETaPgQsKjm8DThW7rwRsTUi2iOivbW1tTrFm5mdg/J4G0rAl4HnI+K/lmzaCXSny93AoyXtqyXNlrQEWAbsq1W9ZmYGzTlc8yPAp4EDkp5N234P2AzskHQ78CJwK0BEHJS0AzhE8ibV2ogYqXnVk3TPPfcwODiYdxlWZ878P7Fhw4acK7F6s3TpUtatW5d3GeOqeVhExPco3w8BcMM4x2wCNlWtqCoYHBzk2eeeZ2TOhXmXYnVk1ttJd9v+H72acyVWT5reeC3vEjLlcWdxzhiZcyFvXv5LeZdhZnXufT94PO8SMnm4DzMzy+SwMDOzTA4LMzPL5LAwM7NMDgszM8vksDAzs0wOCzMzy+SwMDOzTA4LMzPL5LAwM7NMDgszM8vksDAzs0wOCzMzy+SwMDOzTA4LMzPL1DBhIalL0mFJg5LuzLseM7NzSUOEhaQm4L8DNwHLgdskLc+3KjOzc0ejzJR3NTAYET8CkLQdWEUyL3ddevnll2l6vcD7n/la3qXkb3QEIvKuwuqRBLOa8q4ifyNFXn65mHcVE2qUsFgIvFSyPgT8wtidJPUAPQCXXHJJbSobx9y5c3nzzTdzraFenDp1itHR0bzLsDo0a9YsZs8+P+8y6sD5zJ07N+8iJtQoYaEybe/6p2pEbAW2ArS3t+f6T9n7778/z8ubmU2rhuizILmTWFSy3gYcy6kWM7NzTqOExd8CyyQtkXQ+sBrYmXNNZmbnjIZ4DBURRUmfBf4GaAIeiIiDOZdlZnbOaIiwAIiIx4HH867DzOxc1CiPoczMLEcOCzMzy+SwMDOzTA4LMzPLpJihwzBIGgZ+nHcdZmXMB07mXYTZOH42IlrHNs7YsDCrV5IGIqI97zrMJsOPoczMLJPDwszMMjkszGpva94FmE2W+yzMzCyT7yzMzCyTw8LMzDI5LMzMLJPDwszMMjkszMws0/8H8j+DXf1gqawAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#outlier found\n",
    "sns.boxplot(y='MasVnrArea',data=dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Attchd     870\n",
       "Detchd     387\n",
       "BuiltIn     88\n",
       "Basment     19\n",
       "CarPort      9\n",
       "2Types       6\n",
       "Name: GarageType, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#categorical data (using mode)\n",
    "dataset['GarageType'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['LotFrontage']=dataset['LotFrontage'].fillna(dataset['LotFrontage'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset['MasVnrArea']=dataset['MasVnrArea'].fillna(dataset['MasVnrArea'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "dataset['GarageType'] = dataset['GarageType'].fillna(dataset.GarageType.mode()[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id             0\n",
       "LotFrontage    0\n",
       "LotArea        0\n",
       "MasVnrArea     0\n",
       "1stFlrSF       0\n",
       "2ndFlrSF       0\n",
       "GrLivArea      0\n",
       "GarageType     0\n",
       "GarageArea     0\n",
       "SalePrice      0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 10 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   Id           1460 non-null   int64  \n",
      " 1   LotFrontage  1460 non-null   float64\n",
      " 2   LotArea      1460 non-null   int64  \n",
      " 3   MasVnrArea   1460 non-null   float64\n",
      " 4   1stFlrSF     1460 non-null   int64  \n",
      " 5   2ndFlrSF     1460 non-null   int64  \n",
      " 6   GrLivArea    1460 non-null   int64  \n",
      " 7   GarageType   1460 non-null   object \n",
      " 8   GarageArea   1460 non-null   int64  \n",
      " 9   SalePrice    1460 non-null   int64  \n",
      "dtypes: float64(2), int64(7), object(1)\n",
      "memory usage: 114.2+ KB\n"
     ]
    }
   ],
   "source": [
    "dataset.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Id  LotFrontage  LotArea  MasVnrArea  1stFlrSF  2ndFlrSF  GrLivArea  \\\n",
      "0   1         65.0     8450       196.0       856       854       1710   \n",
      "1   2         80.0     9600         0.0      1262         0       1262   \n",
      "2   3         68.0    11250       162.0       920       866       1786   \n",
      "3   4         60.0     9550         0.0       961       756       1717   \n",
      "4   5         84.0    14260       350.0      1145      1053       2198   \n",
      "\n",
      "   GarageArea  SalePrice  GarageType_2Types  GarageType_Attchd  \\\n",
      "0         548     208500                  0                  1   \n",
      "1         460     181500                  0                  1   \n",
      "2         608     223500                  0                  1   \n",
      "3         642     140000                  0                  0   \n",
      "4         836     250000                  0                  1   \n",
      "\n",
      "   GarageType_Basment  GarageType_BuiltIn  GarageType_CarPort  \\\n",
      "0                   0                   0                   0   \n",
      "1                   0                   0                   0   \n",
      "2                   0                   0                   0   \n",
      "3                   0                   0                   0   \n",
      "4                   0                   0                   0   \n",
      "\n",
      "   GarageType_Detchd  \n",
      "0                  0  \n",
      "1                  0  \n",
      "2                  0  \n",
      "3                  1  \n",
      "4                  0  \n"
     ]
    }
   ],
   "source": [
    "#Encoding concept (bec of catogorical data)\n",
    "dataset1 = pd.get_dummies(dataset, columns=['GarageType'])\n",
    "print(dataset1.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 15)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'LotFrontage', 'LotArea', 'MasVnrArea', '1stFlrSF', '2ndFlrSF',\n",
       "       'GrLivArea', 'GarageArea', 'SalePrice', 'GarageType_2Types',\n",
       "       'GarageType_Attchd', 'GarageType_Basment', 'GarageType_BuiltIn',\n",
       "       'GarageType_CarPort', 'GarageType_Detchd'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\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>Id</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>GarageType_2Types</th>\n",
       "      <th>GarageType_Attchd</th>\n",
       "      <th>GarageType_Basment</th>\n",
       "      <th>GarageType_BuiltIn</th>\n",
       "      <th>GarageType_CarPort</th>\n",
       "      <th>GarageType_Detchd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>65.0</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>460</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>162.0</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>1786</td>\n",
       "      <td>608</td>\n",
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       "      <td>60.0</td>\n",
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       "      <td>961</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>350.0</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>2198</td>\n",
       "      <td>836</td>\n",
       "      <td>250000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  LotFrontage  LotArea  MasVnrArea  1stFlrSF  2ndFlrSF  GrLivArea  \\\n",
       "0   1         65.0     8450       196.0       856       854       1710   \n",
       "1   2         80.0     9600         0.0      1262         0       1262   \n",
       "2   3         68.0    11250       162.0       920       866       1786   \n",
       "3   4         60.0     9550         0.0       961       756       1717   \n",
       "4   5         84.0    14260       350.0      1145      1053       2198   \n",
       "\n",
       "   GarageArea  SalePrice  GarageType_2Types  GarageType_Attchd  \\\n",
       "0         548     208500                  0                  1   \n",
       "1         460     181500                  0                  1   \n",
       "2         608     223500                  0                  1   \n",
       "3         642     140000                  0                  0   \n",
       "4         836     250000                  0                  1   \n",
       "\n",
       "   GarageType_Basment  GarageType_BuiltIn  GarageType_CarPort  \\\n",
       "0                   0                   0                   0   \n",
       "1                   0                   0                   0   \n",
       "2                   0                   0                   0   \n",
       "3                   0                   0                   0   \n",
       "4                   0                   0                   0   \n",
       "\n",
       "   GarageType_Detchd  \n",
       "0                  0  \n",
       "1                  0  \n",
       "2                  0  \n",
       "3                  1  \n",
       "4                  0  "
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageType_2Types</th>\n",
       "      <th>GarageType_Attchd</th>\n",
       "      <th>GarageType_Basment</th>\n",
       "      <th>GarageType_BuiltIn</th>\n",
       "      <th>GarageType_CarPort</th>\n",
       "      <th>GarageType_Detchd</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>196.0</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>1710</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>460</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>162.0</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>1786</td>\n",
       "      <td>608</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>1717</td>\n",
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       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>350.0</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>2198</td>\n",
       "      <td>836</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   LotFrontage  LotArea  MasVnrArea  1stFlrSF  2ndFlrSF  GrLivArea  \\\n",
       "0         65.0     8450       196.0       856       854       1710   \n",
       "1         80.0     9600         0.0      1262         0       1262   \n",
       "2         68.0    11250       162.0       920       866       1786   \n",
       "3         60.0     9550         0.0       961       756       1717   \n",
       "4         84.0    14260       350.0      1145      1053       2198   \n",
       "\n",
       "   GarageArea  GarageType_2Types  GarageType_Attchd  GarageType_Basment  \\\n",
       "0         548                  0                  1                   0   \n",
       "1         460                  0                  1                   0   \n",
       "2         608                  0                  1                   0   \n",
       "3         642                  0                  0                   0   \n",
       "4         836                  0                  1                   0   \n",
       "\n",
       "   GarageType_BuiltIn  GarageType_CarPort  GarageType_Detchd  \n",
       "0                   0                   0                  0  \n",
       "1                   0                   0                  0  \n",
       "2                   0                   0                  0  \n",
       "3                   0                   0                  1  \n",
       "4                   0                   0                  0  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# split the data into independent_variable (x) and dependent_variable (y)\n",
    "x = dataset1[['LotFrontage', 'LotArea', 'MasVnrArea', '1stFlrSF', '2ndFlrSF',\n",
    "       'GrLivArea', 'GarageArea', 'GarageType_2Types',\n",
    "       'GarageType_Attchd', 'GarageType_Basment', 'GarageType_BuiltIn',\n",
    "       'GarageType_CarPort', 'GarageType_Detchd']]\n",
    "x.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       208500\n",
       "1       181500\n",
       "2       223500\n",
       "3       140000\n",
       "4       250000\n",
       "         ...  \n",
       "1455    175000\n",
       "1456    210000\n",
       "1457    266500\n",
       "1458    142125\n",
       "1459    147500\n",
       "Name: SalePrice, Length: 1460, dtype: int64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dependent variable\n",
    "y = dataset1['SalePrice']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1168, 13)\n",
      "(292, 13)\n",
      "(1168,)\n",
      "(292,)\n"
     ]
    }
   ],
   "source": [
    "# split the data into training and test\n",
    "from sklearn.model_selection import train_test_split \n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.20, random_state = 100)\n",
    "\n",
    "print(x_train.shape)\n",
    "print(x_test.shape)\n",
    "print(y_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "reg = LinearRegression()\n",
    "reg.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "       189618.07454592, 167637.83248347, 182176.93622388, 193466.96924438,\n",
       "       124800.46191553, 206992.62404025, 369332.50837682,  91380.6316118 ,\n",
       "       145128.39784966, 165386.62680996, 224569.07158813, 192470.86564025,\n",
       "       157111.76308803, 263779.72016066, 130531.84571111, 141402.87607139,\n",
       "       163170.51855544, 141669.52036265, 248406.42562524, 165777.28018262,\n",
       "       178341.73638324, 175911.65619041, 141736.55213615,  75823.82462612,\n",
       "       293458.9899609 , 220427.97192923, 155007.72635958, 259866.70393502,\n",
       "       204611.87576416, 124577.33594774, 172487.33008659, 186288.69848321,\n",
       "       184799.0003726 , 165151.54561881, 116701.25057476,  86795.78964673,\n",
       "       213497.74458497, 191919.24329979, 227457.82697789,  93477.00995624,\n",
       "       126073.76297554, 129397.78918768, 273939.42579335,  95505.37491793,\n",
       "       334619.96410711, 254379.95068718, 204109.56451966, 331376.31501233,\n",
       "       192975.03677103, 307241.25534882, 174375.03069732, 122702.75694722,\n",
       "       225206.23386264, 225421.85804123, 204273.67792491, 185545.88187164,\n",
       "        83715.18325671, 182275.65251876, 247870.19095417, 241407.63970369,\n",
       "       191089.99443242, 205007.34851321, 206115.71595196, 158757.74000985,\n",
       "       245196.34780617, 221426.03209206, 166274.48043726,  51602.42638807,\n",
       "       195654.8544123 , 167994.2875939 , 143911.90242971, 141785.109907  ,\n",
       "       107234.08365054, 262895.77286444, 118407.76865264, 167696.73322339,\n",
       "       267318.4562269 , 191224.43586473, 211939.54981928, 103010.15067734,\n",
       "       191940.59812469, 156731.80653806, 193599.98609743, 111827.17360817,\n",
       "       132604.58059225, 183896.84305729, 184603.03948089, 166810.88083813,\n",
       "       271772.14668285, 141579.02282733,  65066.0821645 ,  88154.40805402,\n",
       "        88701.84378602, 299842.21626815, 158660.16203563, 227294.53954975,\n",
       "       278521.99611774, 160666.08090276, 391826.05680212, 243174.62660017,\n",
       "       271811.13667933, 186268.7689029 , 323733.39728294, 135723.55013395])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = reg.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-18147.20885296914\n",
      "[-1.86318733e+02  2.90264643e-01  5.77947722e+01  9.31018808e+01\n",
      "  6.58117112e+01 -8.61426742e+00  1.17270554e+02 -7.14650252e+04\n",
      "  3.81204688e+04  8.50981804e+03  4.63088111e+04 -2.70410895e+04\n",
      "  5.56701679e+03]\n"
     ]
    }
   ],
   "source": [
    "print(reg.intercept_)\n",
    "print(reg.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.725209497031192"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "from statsmodels.regression.linear_model import OLS\n",
    "import statsmodels.regression.linear_model as smf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>SalePrice</td>    <th>  R-squared:         </th> <td>   0.680</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.677</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   204.4</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 02 Jul 2021</td> <th>  Prob (F-statistic):</th> <td>1.61e-275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>14:47:38</td>     <th>  Log-Likelihood:    </th> <td> -14167.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1168</td>      <th>  AIC:               </th> <td>2.836e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1155</td>      <th>  BIC:               </th> <td>2.843e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>    12</td>      <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotFrontage</th>        <td> -186.3187</td> <td>   71.510</td> <td>   -2.605</td> <td> 0.009</td> <td> -326.623</td> <td>  -46.015</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotArea</th>            <td>    0.2903</td> <td>    0.146</td> <td>    1.995</td> <td> 0.046</td> <td>    0.005</td> <td>    0.576</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrArea</th>         <td>   57.7948</td> <td>    8.109</td> <td>    7.127</td> <td> 0.000</td> <td>   41.885</td> <td>   73.704</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1stFlrSF</th>           <td>   93.1019</td> <td>   28.689</td> <td>    3.245</td> <td> 0.001</td> <td>   36.814</td> <td>  149.390</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2ndFlrSF</th>           <td>   65.8117</td> <td>   28.535</td> <td>    2.306</td> <td> 0.021</td> <td>    9.826</td> <td>  121.798</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GrLivArea</th>          <td>   -8.6143</td> <td>   27.989</td> <td>   -0.308</td> <td> 0.758</td> <td>  -63.529</td> <td>   46.300</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageArea</th>         <td>  117.2706</td> <td>    7.725</td> <td>   15.180</td> <td> 0.000</td> <td>  102.113</td> <td>  132.428</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_2Types</th>  <td>-8.961e+04</td> <td> 2.36e+04</td> <td>   -3.796</td> <td> 0.000</td> <td>-1.36e+05</td> <td>-4.33e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_Attchd</th>  <td> 1.997e+04</td> <td> 6023.183</td> <td>    3.316</td> <td> 0.001</td> <td> 8155.654</td> <td> 3.18e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_Basment</th> <td>-9637.3908</td> <td> 1.32e+04</td> <td>   -0.731</td> <td> 0.465</td> <td>-3.55e+04</td> <td> 1.62e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_BuiltIn</th> <td> 2.816e+04</td> <td> 8327.286</td> <td>    3.382</td> <td> 0.001</td> <td> 1.18e+04</td> <td> 4.45e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_CarPort</th> <td>-4.519e+04</td> <td> 1.81e+04</td> <td>   -2.503</td> <td> 0.012</td> <td>-8.06e+04</td> <td>-9770.956</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageType_Detchd</th>  <td>-1.258e+04</td> <td> 5451.735</td> <td>   -2.308</td> <td> 0.021</td> <td>-2.33e+04</td> <td>-1883.779</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>310.434</td> <th>  Durbin-Watson:     </th> <td>   2.082</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>19505.940</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.213</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>23.016</td>  <th>  Cond. No.          </th> <td>2.64e+05</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.<br/>[2] The condition number is large, 2.64e+05. This might indicate that there are<br/>strong multicollinearity or other numerical problems."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:              SalePrice   R-squared:                       0.680\n",
       "Model:                            OLS   Adj. R-squared:                  0.677\n",
       "Method:                 Least Squares   F-statistic:                     204.4\n",
       "Date:                Fri, 02 Jul 2021   Prob (F-statistic):          1.61e-275\n",
       "Time:                        14:47:38   Log-Likelihood:                -14167.\n",
       "No. Observations:                1168   AIC:                         2.836e+04\n",
       "Df Residuals:                    1155   BIC:                         2.843e+04\n",
       "Df Model:                          12                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "======================================================================================\n",
       "                         coef    std err          t      P>|t|      [0.025      0.975]\n",
       "--------------------------------------------------------------------------------------\n",
       "LotFrontage         -186.3187     71.510     -2.605      0.009    -326.623     -46.015\n",
       "LotArea                0.2903      0.146      1.995      0.046       0.005       0.576\n",
       "MasVnrArea            57.7948      8.109      7.127      0.000      41.885      73.704\n",
       "1stFlrSF              93.1019     28.689      3.245      0.001      36.814     149.390\n",
       "2ndFlrSF              65.8117     28.535      2.306      0.021       9.826     121.798\n",
       "GrLivArea             -8.6143     27.989     -0.308      0.758     -63.529      46.300\n",
       "GarageArea           117.2706      7.725     15.180      0.000     102.113     132.428\n",
       "GarageType_2Types  -8.961e+04   2.36e+04     -3.796      0.000   -1.36e+05   -4.33e+04\n",
       "GarageType_Attchd   1.997e+04   6023.183      3.316      0.001    8155.654    3.18e+04\n",
       "GarageType_Basment -9637.3908   1.32e+04     -0.731      0.465   -3.55e+04    1.62e+04\n",
       "GarageType_BuiltIn  2.816e+04   8327.286      3.382      0.001    1.18e+04    4.45e+04\n",
       "GarageType_CarPort -4.519e+04   1.81e+04     -2.503      0.012   -8.06e+04   -9770.956\n",
       "GarageType_Detchd  -1.258e+04   5451.735     -2.308      0.021   -2.33e+04   -1883.779\n",
       "==============================================================================\n",
       "Omnibus:                      310.434   Durbin-Watson:                   2.082\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            19505.940\n",
       "Skew:                          -0.213   Prob(JB):                         0.00\n",
       "Kurtosis:                      23.016   Cond. No.                     2.64e+05\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "[2] The condition number is large, 2.64e+05. This might indicate that there are\n",
       "strong multicollinearity or other numerical problems.\n",
       "\"\"\""
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_model = smf.OLS(endog=y_train, exog=x_train).fit()\n",
    "reg_model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>Id</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>196.0</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>1710</td>\n",
       "      <td>548</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>460</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>162.0</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>1786</td>\n",
       "      <td>608</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>1717</td>\n",
       "      <td>642</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>350.0</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>2198</td>\n",
       "      <td>836</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  LotFrontage  LotArea  MasVnrArea  1stFlrSF  2ndFlrSF  GrLivArea  \\\n",
       "0   1         65.0     8450       196.0       856       854       1710   \n",
       "1   2         80.0     9600         0.0      1262         0       1262   \n",
       "2   3         68.0    11250       162.0       920       866       1786   \n",
       "3   4         60.0     9550         0.0       961       756       1717   \n",
       "4   5         84.0    14260       350.0      1145      1053       2198   \n",
       "\n",
       "   GarageArea  SalePrice  \n",
       "0         548     208500  \n",
       "1         460     181500  \n",
       "2         608     223500  \n",
       "3         642     140000  \n",
       "4         836     250000  "
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Dropping GarageType Variable\n",
    "dataset.drop('GarageType', axis = 1, inplace = True)\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'LotFrontage', 'LotArea', 'MasVnrArea', '1stFlrSF', '2ndFlrSF',\n",
       "       'GrLivArea', 'GarageArea', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       208500\n",
       "1       181500\n",
       "2       223500\n",
       "3       140000\n",
       "4       250000\n",
       "         ...  \n",
       "1455    175000\n",
       "1456    210000\n",
       "1457    266500\n",
       "1458    142125\n",
       "1459    147500\n",
       "Name: SalePrice, Length: 1460, dtype: int64"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Independent and Dependent variable\n",
    "x = dataset[['LotFrontage', 'LotArea', 'MasVnrArea', '1stFlrSF', '2ndFlrSF',\n",
    "       'GrLivArea', 'GarageArea',]]\n",
    "x.head()\n",
    "y = dataset['SalePrice']\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Test Train Split\n",
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_test, y_train, y_test = train_test_split(x,y, test_size=0.20, random_state = 100)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model creation\n",
    "from sklearn.linear_model import LinearRegression\n",
    "reg1 = LinearRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression()"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1.fit(x_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred1 = reg1.predict(x_test)\n",
    "y_pred1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7099164249210489"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Model evaluation\n",
    "from sklearn.metrics import r2_score\n",
    "r2_score(y_test, y_pred1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "#OLS method\n",
    "from statsmodels.regression.linear_model import OLS\n",
    "import statsmodels.regression.linear_model as smf1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>        <td>SalePrice</td>    <th>  R-squared (uncentered):</th>      <td>   0.942</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared (uncentered):</th> <td>   0.941</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th>          <td>   2686.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Fri, 02 Jul 2021</td> <th>  Prob (F-statistic):</th>           <td>  0.00</td>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>15:13:42</td>     <th>  Log-Likelihood:    </th>          <td> -14236.</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>  1168</td>      <th>  AIC:               </th>          <td>2.849e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>  1161</td>      <th>  BIC:               </th>          <td>2.852e+04</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     7</td>      <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>              <td> </td>    \n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "       <td></td>          <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotFrontage</th> <td> -124.0255</td> <td>   66.071</td> <td>   -1.877</td> <td> 0.061</td> <td> -253.658</td> <td>    5.607</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>LotArea</th>     <td>    0.3162</td> <td>    0.153</td> <td>    2.068</td> <td> 0.039</td> <td>    0.016</td> <td>    0.616</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>MasVnrArea</th>  <td>   67.4030</td> <td>    8.298</td> <td>    8.123</td> <td> 0.000</td> <td>   51.122</td> <td>   83.684</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>1stFlrSF</th>    <td>  127.9994</td> <td>   30.051</td> <td>    4.259</td> <td> 0.000</td> <td>   69.039</td> <td>  186.960</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>2ndFlrSF</th>    <td>   96.1686</td> <td>   29.968</td> <td>    3.209</td> <td> 0.001</td> <td>   37.371</td> <td>  154.966</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GrLivArea</th>   <td>  -35.5520</td> <td>   29.473</td> <td>   -1.206</td> <td> 0.228</td> <td>  -93.379</td> <td>   22.275</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>GarageArea</th>  <td>  106.5920</td> <td>    7.987</td> <td>   13.346</td> <td> 0.000</td> <td>   90.922</td> <td>  122.262</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td>343.626</td> <th>  Durbin-Watson:     </th> <td>   2.050</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.000</td>  <th>  Jarque-Bera (JB):  </th> <td>16679.958</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td>-0.547</td>  <th>  Prob(JB):          </th> <td>    0.00</td> \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td>21.481</td>  <th>  Cond. No.          </th> <td>    685.</td> \n",
       "</tr>\n",
       "</table><br/><br/>Notes:<br/>[1] R² is computed without centering (uncentered) since the model does not contain a constant.<br/>[2] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                                 OLS Regression Results                                \n",
       "=======================================================================================\n",
       "Dep. Variable:              SalePrice   R-squared (uncentered):                   0.942\n",
       "Model:                            OLS   Adj. R-squared (uncentered):              0.941\n",
       "Method:                 Least Squares   F-statistic:                              2686.\n",
       "Date:                Fri, 02 Jul 2021   Prob (F-statistic):                        0.00\n",
       "Time:                        15:13:42   Log-Likelihood:                         -14236.\n",
       "No. Observations:                1168   AIC:                                  2.849e+04\n",
       "Df Residuals:                    1161   BIC:                                  2.852e+04\n",
       "Df Model:                           7                                                  \n",
       "Covariance Type:            nonrobust                                                  \n",
       "===============================================================================\n",
       "                  coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------\n",
       "LotFrontage  -124.0255     66.071     -1.877      0.061    -253.658       5.607\n",
       "LotArea         0.3162      0.153      2.068      0.039       0.016       0.616\n",
       "MasVnrArea     67.4030      8.298      8.123      0.000      51.122      83.684\n",
       "1stFlrSF      127.9994     30.051      4.259      0.000      69.039     186.960\n",
       "2ndFlrSF       96.1686     29.968      3.209      0.001      37.371     154.966\n",
       "GrLivArea     -35.5520     29.473     -1.206      0.228     -93.379      22.275\n",
       "GarageArea    106.5920      7.987     13.346      0.000      90.922     122.262\n",
       "==============================================================================\n",
       "Omnibus:                      343.626   Durbin-Watson:                   2.050\n",
       "Prob(Omnibus):                  0.000   Jarque-Bera (JB):            16679.958\n",
       "Skew:                          -0.547   Prob(JB):                         0.00\n",
       "Kurtosis:                      21.481   Cond. No.                         685.\n",
       "==============================================================================\n",
       "\n",
       "Notes:\n",
       "[1] R² is computed without centering (uncentered) since the model does not contain a constant.\n",
       "[2] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg_model1 = smf1.OLS(endog=y_train, exog=x_train).fit()\n",
    "reg_model1.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Inclusion of Catogorical variable 'GarageType': r2_score: 72.5 & Adjusted R squared: 67.7\n",
    "\n",
    "\n",
    "\n",
    "#2. Enclusion of Catogorical variable 'GarageType': r2_score: 70.9 & Adjusted R squared: 94.1"
   ]
  }
 ],
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