House price prediction_Anil.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment Part-I\n",
    "A US-based housing company named Surprise Housing has decided to enter the Australian market. The company uses data analytics to purchase houses at a price below their actual values and flip them on at a higher price. For the same purpose, the company has collected a data set from the sale of houses in Australia. The data is provided in the CSV file below.\n",
    "The company is looking at prospective properties to buy to enter the market. You are required to build a regression model using regularisation in order to predict the actual value of the prospective properties and decide whether to invest in them or not.\n",
    "The company wants to know the following things about the prospective properties:\n",
    "\n",
    "Which variables are significant in predicting the price of a house, and\n",
    "\n",
    "How well those variables describe the price of a house.\n",
    "Also, determine the optimal value of lambda for ridge and lasso regression.\n",
    "\n",
    "#### Business Goal \n",
    "\n",
    "You are required to model the price of houses with the available independent variables. This model will then be used by the management to understand how exactly the prices vary with the variables. They can accordingly manipulate the strategy of the firm and concentrate on areas that will yield high returns. Further, the model will be a good way for management to understand the pricing dynamics of a new market.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Flow of the Assignment\n",
    "\n",
    "1. Data exploration and understanding\n",
    "2. Data cleaning\n",
    "3. Data preparation\n",
    "4. Split the data into train and test set\n",
    "5. Ridge regression\n",
    "6. Lasso regression\n",
    "7. Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Data exploration and understanding\n",
    "Will look at the dataset and understand the size, attribute names etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets import the necessary libraries required for predictions\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import linear_model, metrics\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "\n",
    "# Importing RFE and LinearRegression\n",
    "from sklearn.feature_selection import RFE\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "import os\n",
    "\n",
    "# hide warnings\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reading the dataset\n",
    "house = pd.read_csv(\"train.csv\",encoding = 'latin')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0   1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1   2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2   3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3   4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4   5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "\n",
       "  LandContour Utilities  ... PoolArea PoolQC Fence MiscFeature MiscVal MoSold  \\\n",
       "0         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "1         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      5   \n",
       "2         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      9   \n",
       "3         Lvl    AllPub  ...        0    NaN   NaN         NaN       0      2   \n",
       "4         Lvl    AllPub  ...        0    NaN   NaN         NaN       0     12   \n",
       "\n",
       "  YrSold  SaleType  SaleCondition  SalePrice  \n",
       "0   2008        WD         Normal     208500  \n",
       "1   2007        WD         Normal     181500  \n",
       "2   2008        WD         Normal     223500  \n",
       "3   2006        WD        Abnorml     140000  \n",
       "4   2008        WD         Normal     250000  \n",
       "\n",
       "[5 rows x 81 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#identifying top 5 rows\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1460, 81)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#identify the shape of dataset\n",
    "house.shape"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Dataset has 1460 rows and 81 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     1452 non-null   object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "#dataset info\n",
    "house.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>...</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>...</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
       "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
       "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
       "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  ...  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000  ...   \n",
       "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726  ...   \n",
       "std       1.112799    30.202904     20.645407   181.066207   456.098091  ...   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000  ...   \n",
       "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000  ...   \n",
       "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000  ...   \n",
       "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000  ...   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000  ...   \n",
       "\n",
       "        WoodDeckSF  OpenPorchSF  EnclosedPorch    3SsnPorch  ScreenPorch  \\\n",
       "count  1460.000000  1460.000000    1460.000000  1460.000000  1460.000000   \n",
       "mean     94.244521    46.660274      21.954110     3.409589    15.060959   \n",
       "std     125.338794    66.256028      61.119149    29.317331    55.757415   \n",
       "min       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000       0.000000     0.000000     0.000000   \n",
       "50%       0.000000    25.000000       0.000000     0.000000     0.000000   \n",
       "75%     168.000000    68.000000       0.000000     0.000000     0.000000   \n",
       "max     857.000000   547.000000     552.000000   508.000000   480.000000   \n",
       "\n",
       "          PoolArea       MiscVal       MoSold       YrSold      SalePrice  \n",
       "count  1460.000000   1460.000000  1460.000000  1460.000000    1460.000000  \n",
       "mean      2.758904     43.489041     6.321918  2007.815753  180921.195890  \n",
       "std      40.177307    496.123024     2.703626     1.328095   79442.502883  \n",
       "min       0.000000      0.000000     1.000000  2006.000000   34900.000000  \n",
       "25%       0.000000      0.000000     5.000000  2007.000000  129975.000000  \n",
       "50%       0.000000      0.000000     6.000000  2008.000000  163000.000000  \n",
       "75%       0.000000      0.000000     8.000000  2009.000000  214000.000000  \n",
       "max     738.000000  15500.000000    12.000000  2010.000000  755000.000000  \n",
       "\n",
       "[8 rows x 38 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#identifying other attributes of the dataset using describe function\n",
    "house.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>...</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>...</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>...</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  YearBuilt  \\\n",
       "0   1          60         65.0     8450            7            5       2003   \n",
       "1   2          20         80.0     9600            6            8       1976   \n",
       "2   3          60         68.0    11250            7            5       2001   \n",
       "3   4          70         60.0     9550            7            5       1915   \n",
       "4   5          60         84.0    14260            8            5       2000   \n",
       "\n",
       "   YearRemodAdd  MasVnrArea  BsmtFinSF1  ...  WoodDeckSF  OpenPorchSF  \\\n",
       "0          2003       196.0         706  ...           0           61   \n",
       "1          1976         0.0         978  ...         298            0   \n",
       "2          2002       162.0         486  ...           0           42   \n",
       "3          1970         0.0         216  ...           0           35   \n",
       "4          2000       350.0         655  ...         192           84   \n",
       "\n",
       "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  \\\n",
       "0              0          0            0         0        0       2    2008   \n",
       "1              0          0            0         0        0       5    2007   \n",
       "2              0          0            0         0        0       9    2008   \n",
       "3            272          0            0         0        0       2    2006   \n",
       "4              0          0            0         0        0      12    2008   \n",
       "\n",
       "   SalePrice  \n",
       "0     208500  \n",
       "1     181500  \n",
       "2     223500  \n",
       "3     140000  \n",
       "4     250000  \n",
       "\n",
       "[5 rows x 38 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all numeric (float and int) variables in the dataset\n",
    "house_numeric = house.select_dtypes(include=['float64', 'int64'])\n",
    "house_numeric.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 38 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   LotFrontage    1201 non-null   float64\n",
      " 3   LotArea        1460 non-null   int64  \n",
      " 4   OverallQual    1460 non-null   int64  \n",
      " 5   OverallCond    1460 non-null   int64  \n",
      " 6   YearBuilt      1460 non-null   int64  \n",
      " 7   YearRemodAdd   1460 non-null   int64  \n",
      " 8   MasVnrArea     1452 non-null   float64\n",
      " 9   BsmtFinSF1     1460 non-null   int64  \n",
      " 10  BsmtFinSF2     1460 non-null   int64  \n",
      " 11  BsmtUnfSF      1460 non-null   int64  \n",
      " 12  TotalBsmtSF    1460 non-null   int64  \n",
      " 13  1stFlrSF       1460 non-null   int64  \n",
      " 14  2ndFlrSF       1460 non-null   int64  \n",
      " 15  LowQualFinSF   1460 non-null   int64  \n",
      " 16  GrLivArea      1460 non-null   int64  \n",
      " 17  BsmtFullBath   1460 non-null   int64  \n",
      " 18  BsmtHalfBath   1460 non-null   int64  \n",
      " 19  FullBath       1460 non-null   int64  \n",
      " 20  HalfBath       1460 non-null   int64  \n",
      " 21  BedroomAbvGr   1460 non-null   int64  \n",
      " 22  KitchenAbvGr   1460 non-null   int64  \n",
      " 23  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 24  Fireplaces     1460 non-null   int64  \n",
      " 25  GarageYrBlt    1379 non-null   float64\n",
      " 26  GarageCars     1460 non-null   int64  \n",
      " 27  GarageArea     1460 non-null   int64  \n",
      " 28  WoodDeckSF     1460 non-null   int64  \n",
      " 29  OpenPorchSF    1460 non-null   int64  \n",
      " 30  EnclosedPorch  1460 non-null   int64  \n",
      " 31  3SsnPorch      1460 non-null   int64  \n",
      " 32  ScreenPorch    1460 non-null   int64  \n",
      " 33  PoolArea       1460 non-null   int64  \n",
      " 34  MiscVal        1460 non-null   int64  \n",
      " 35  MoSold         1460 non-null   int64  \n",
      " 36  YrSold         1460 non-null   int64  \n",
      " 37  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35)\n",
      "memory usage: 433.6 KB\n"
     ]
    }
   ],
   "source": [
    "#house numeric info\n",
    "house_numeric.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Data cleaning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### identifying the null values of categorical variables and removing/imputing those values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Alley           1369\n",
       "MasVnrType         8\n",
       "BsmtQual          37\n",
       "BsmtCond          37\n",
       "BsmtExposure      38\n",
       "BsmtFinType1      37\n",
       "BsmtFinType2      38\n",
       "Electrical         1\n",
       "FireplaceQu      690\n",
       "GarageType        81\n",
       "GarageFinish      81\n",
       "GarageQual        81\n",
       "GarageCond        81\n",
       "PoolQC          1453\n",
       "Fence           1179\n",
       "MiscFeature     1406\n",
       "dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.select_dtypes(include='object').isnull().sum()[house.select_dtypes(include='object').isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Will replace the NA with None\n",
    "\n",
    "for col in ('Alley', 'MasVnrType', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'FireplaceQu', \n",
    "            'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PoolQC', 'Fence', 'MiscFeature', 'Electrical'):\n",
    "    \n",
    "    house[col]=house[col].fillna('None')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSZoning         0\n",
       "Street           0\n",
       "Alley            0\n",
       "LotShape         0\n",
       "LandContour      0\n",
       "Utilities        0\n",
       "LotConfig        0\n",
       "LandSlope        0\n",
       "Neighborhood     0\n",
       "Condition1       0\n",
       "Condition2       0\n",
       "BldgType         0\n",
       "HouseStyle       0\n",
       "RoofStyle        0\n",
       "RoofMatl         0\n",
       "Exterior1st      0\n",
       "Exterior2nd      0\n",
       "MasVnrType       0\n",
       "ExterQual        0\n",
       "ExterCond        0\n",
       "Foundation       0\n",
       "BsmtQual         0\n",
       "BsmtCond         0\n",
       "BsmtExposure     0\n",
       "BsmtFinType1     0\n",
       "BsmtFinType2     0\n",
       "Heating          0\n",
       "HeatingQC        0\n",
       "CentralAir       0\n",
       "Electrical       0\n",
       "KitchenQual      0\n",
       "Functional       0\n",
       "FireplaceQu      0\n",
       "GarageType       0\n",
       "GarageFinish     0\n",
       "GarageQual       0\n",
       "GarageCond       0\n",
       "PavedDrive       0\n",
       "PoolQC           0\n",
       "Fence            0\n",
       "MiscFeature      0\n",
       "SaleType         0\n",
       "SaleCondition    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.select_dtypes(include='object').isnull().sum()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "There are no null values in categorical attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>...</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Reg</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>IR1</td>\n",
       "      <td>FR2</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea LotShape LotConfig  \\\n",
       "0   1          60       RL         65.0     8450      Reg    Inside   \n",
       "1   2          20       RL         80.0     9600      Reg       FR2   \n",
       "2   3          60       RL         68.0    11250      IR1    Inside   \n",
       "3   4          70       RL         60.0     9550      IR1    Corner   \n",
       "4   5          60       RL         84.0    14260      IR1       FR2   \n",
       "\n",
       "  Neighborhood BldgType HouseStyle  ...  EnclosedPorch  3SsnPorch  \\\n",
       "0      CollgCr     1Fam     2Story  ...              0          0   \n",
       "1      Veenker     1Fam     1Story  ...              0          0   \n",
       "2      CollgCr     1Fam     2Story  ...              0          0   \n",
       "3      Crawfor     1Fam     2Story  ...            272          0   \n",
       "4      NoRidge     1Fam     2Story  ...              0          0   \n",
       "\n",
       "   ScreenPorch  PoolArea Fence MiscVal MoSold YrSold  SaleCondition SalePrice  \n",
       "0            0         0  None       0      2   2008         Normal    208500  \n",
       "1            0         0  None       0      5   2007         Normal    181500  \n",
       "2            0         0  None       0      9   2008         Normal    223500  \n",
       "3            0         0  None       0      2   2006        Abnorml    140000  \n",
       "4            0         0  None       0     12   2008         Normal    250000  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#To avoid skewdness in the categorical variables will drop the variables/attributes\n",
    "#Will remove those attributes having more than 85% of the values\n",
    "\n",
    "def largeCategoricalValueCounts():\n",
    "    column = []\n",
    "    categorical_columns = house.select_dtypes(include=['object'])\n",
    "    for col in (categorical_columns):\n",
    "        if(house[col].value_counts().max() >= 1241):\n",
    "            column.append(col)\n",
    "    return column\n",
    "\n",
    "removedColumns = largeCategoricalValueCounts()\n",
    "\n",
    "# Remove the columns with skewed data\n",
    "\n",
    "house.drop(removedColumns, axis = 1, inplace = True)\n",
    "\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### identifying the null values of numerical variables and removing/imputing those values\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id                 0\n",
       "MSSubClass         0\n",
       "MSZoning           0\n",
       "LotFrontage      259\n",
       "LotArea            0\n",
       "LotShape           0\n",
       "LotConfig          0\n",
       "Neighborhood       0\n",
       "BldgType           0\n",
       "HouseStyle         0\n",
       "OverallQual        0\n",
       "OverallCond        0\n",
       "YearBuilt          0\n",
       "YearRemodAdd       0\n",
       "RoofStyle          0\n",
       "Exterior1st        0\n",
       "Exterior2nd        0\n",
       "MasVnrType         0\n",
       "MasVnrArea         8\n",
       "ExterQual          0\n",
       "Foundation         0\n",
       "BsmtQual           0\n",
       "BsmtExposure       0\n",
       "BsmtFinType1       0\n",
       "BsmtFinSF1         0\n",
       "BsmtFinSF2         0\n",
       "BsmtUnfSF          0\n",
       "TotalBsmtSF        0\n",
       "HeatingQC          0\n",
       "1stFlrSF           0\n",
       "2ndFlrSF           0\n",
       "LowQualFinSF       0\n",
       "GrLivArea          0\n",
       "BsmtFullBath       0\n",
       "BsmtHalfBath       0\n",
       "FullBath           0\n",
       "HalfBath           0\n",
       "BedroomAbvGr       0\n",
       "KitchenAbvGr       0\n",
       "KitchenQual        0\n",
       "TotRmsAbvGrd       0\n",
       "Fireplaces         0\n",
       "FireplaceQu        0\n",
       "GarageType         0\n",
       "GarageYrBlt       81\n",
       "GarageFinish       0\n",
       "GarageCars         0\n",
       "GarageArea         0\n",
       "WoodDeckSF         0\n",
       "OpenPorchSF        0\n",
       "EnclosedPorch      0\n",
       "3SsnPorch          0\n",
       "ScreenPorch        0\n",
       "PoolArea           0\n",
       "Fence              0\n",
       "MiscVal            0\n",
       "MoSold             0\n",
       "YrSold             0\n",
       "SaleCondition      0\n",
       "SalePrice          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LotFrontage    259\n",
       "MasVnrArea       8\n",
       "GarageYrBlt     81\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.select_dtypes(include=['int64','float']).isnull().sum()[house.select_dtypes(include=['int64','float']).isnull().sum()>0]"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "There are three numerical columns with null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# null values are imputed to attributes LotFrontage and MasVnrArea\n",
    "\n",
    "house['LotFrontage'] = house['LotFrontage'].replace(np.nan, house['LotFrontage'].median())\n",
    "house['MasVnrArea'] = house['MasVnrArea'].replace(np.nan, house['MasVnrArea'].median())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#With respect to GarageYrBlt, will impute with 0.  \n",
    "#imputed the 0 value\n",
    "house['GarageYrBlt']=house['GarageYrBlt'].fillna(0)\n",
    "house['GarageYrBlt'] = house['GarageYrBlt'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>...</th>\n",
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       "      <th>ScreenPorch</th>\n",
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       "      <th>Old_NewGarage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
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       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>208500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
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       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Reg</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
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       "    <tr>\n",
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       "      <td>60</td>\n",
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       "      <td>11250</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>IR1</td>\n",
       "      <td>FR2</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea LotShape LotConfig  \\\n",
       "0   1          60       RL         65.0     8450      Reg    Inside   \n",
       "1   2          20       RL         80.0     9600      Reg       FR2   \n",
       "2   3          60       RL         68.0    11250      IR1    Inside   \n",
       "3   4          70       RL         60.0     9550      IR1    Corner   \n",
       "4   5          60       RL         84.0    14260      IR1       FR2   \n",
       "\n",
       "  Neighborhood BldgType HouseStyle  ...  3SsnPorch  ScreenPorch  PoolArea  \\\n",
       "0      CollgCr     1Fam     2Story  ...          0            0         0   \n",
       "1      Veenker     1Fam     1Story  ...          0            0         0   \n",
       "2      CollgCr     1Fam     2Story  ...          0            0         0   \n",
       "3      Crawfor     1Fam     2Story  ...          0            0         0   \n",
       "4      NoRidge     1Fam     2Story  ...          0            0         0   \n",
       "\n",
       "   Fence MiscVal MoSold YrSold SaleCondition  SalePrice Old_NewGarage  \n",
       "0   None       0      2   2008        Normal     208500             1  \n",
       "1   None       0      5   2007        Normal     181500             0  \n",
       "2   None       0      9   2008        Normal     223500             1  \n",
       "3   None       0      2   2006       Abnorml     140000             0  \n",
       "4   None       0     12   2008        Normal     250000             1  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Identifying the garage build new and old. Previously imputed zero will be automatically considered as old\n",
    "def GarageConstructionPeriod(row):\n",
    "    if row == 0:\n",
    "        return 0\n",
    "#if garage is build before 2000 will be considered as old (0) else new (1)\n",
    "    elif row >= 1900 and row < 2000:        \n",
    "        return 0\n",
    "    else:   \n",
    "        return 1\n",
    "    \n",
    "#separate Old_NewGarage is created\n",
    "house['Old_NewGarage'] = house['GarageYrBlt'].apply(GarageConstructionPeriod)\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Id               0\n",
       "MSSubClass       0\n",
       "LotFrontage      0\n",
       "LotArea          0\n",
       "OverallQual      0\n",
       "OverallCond      0\n",
       "YearBuilt        0\n",
       "YearRemodAdd     0\n",
       "MasVnrArea       0\n",
       "BsmtFinSF1       0\n",
       "BsmtFinSF2       0\n",
       "BsmtUnfSF        0\n",
       "TotalBsmtSF      0\n",
       "1stFlrSF         0\n",
       "2ndFlrSF         0\n",
       "LowQualFinSF     0\n",
       "GrLivArea        0\n",
       "BsmtFullBath     0\n",
       "BsmtHalfBath     0\n",
       "FullBath         0\n",
       "HalfBath         0\n",
       "BedroomAbvGr     0\n",
       "KitchenAbvGr     0\n",
       "TotRmsAbvGrd     0\n",
       "Fireplaces       0\n",
       "GarageYrBlt      0\n",
       "GarageCars       0\n",
       "GarageArea       0\n",
       "WoodDeckSF       0\n",
       "OpenPorchSF      0\n",
       "EnclosedPorch    0\n",
       "3SsnPorch        0\n",
       "ScreenPorch      0\n",
       "PoolArea         0\n",
       "MiscVal          0\n",
       "MoSold           0\n",
       "YrSold           0\n",
       "SalePrice        0\n",
       "Old_NewGarage    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.select_dtypes(include=['int64','float']).isnull().sum()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "There are no null values in numerical categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>Old_NewGarage</th>\n",
       "      <th>Remodelled</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>2</td>\n",
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       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Reg</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>IR1</td>\n",
       "      <td>FR2</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 62 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea LotShape LotConfig  \\\n",
       "0   1          60       RL         65.0     8450      Reg    Inside   \n",
       "1   2          20       RL         80.0     9600      Reg       FR2   \n",
       "2   3          60       RL         68.0    11250      IR1    Inside   \n",
       "3   4          70       RL         60.0     9550      IR1    Corner   \n",
       "4   5          60       RL         84.0    14260      IR1       FR2   \n",
       "\n",
       "  Neighborhood BldgType HouseStyle  ...  ScreenPorch  PoolArea  Fence  \\\n",
       "0      CollgCr     1Fam     2Story  ...            0         0   None   \n",
       "1      Veenker     1Fam     1Story  ...            0         0   None   \n",
       "2      CollgCr     1Fam     2Story  ...            0         0   None   \n",
       "3      Crawfor     1Fam     2Story  ...            0         0   None   \n",
       "4      NoRidge     1Fam     2Story  ...            0         0   None   \n",
       "\n",
       "   MiscVal MoSold YrSold SaleCondition SalePrice  Old_NewGarage Remodelled  \n",
       "0        0      2   2008        Normal    208500              1          0  \n",
       "1        0      5   2007        Normal    181500              0          0  \n",
       "2        0      9   2008        Normal    223500              1          1  \n",
       "3        0      2   2006       Abnorml    140000              0          1  \n",
       "4        0     12   2008        Normal    250000              1          0  \n",
       "\n",
       "[5 rows x 62 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Looking at attributes we could create a new column wheter remodelled or not based on the year built and year modified\n",
    "\n",
    "def whetherRemodelled(row):\n",
    "    if(row['YearBuilt'] == row['YearRemodAdd']):\n",
    "        return 0\n",
    "    elif(row['YearBuilt'] < row['YearRemodAdd']):\n",
    "        return 1\n",
    "    else:\n",
    "        return 2\n",
    "    \n",
    "house['Remodelled'] = house.apply(whetherRemodelled, axis=1)\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>...</th>\n",
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       "      <th>Remodelled</th>\n",
       "      <th>Built_RemodelAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
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       "      <td>9600</td>\n",
       "      <td>Reg</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
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       "      <td>181500</td>\n",
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       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>IR1</td>\n",
       "      <td>FR2</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 63 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  MSSubClass MSZoning  LotFrontage  LotArea LotShape LotConfig  \\\n",
       "0   1          60       RL         65.0     8450      Reg    Inside   \n",
       "1   2          20       RL         80.0     9600      Reg       FR2   \n",
       "2   3          60       RL         68.0    11250      IR1    Inside   \n",
       "3   4          70       RL         60.0     9550      IR1    Corner   \n",
       "4   5          60       RL         84.0    14260      IR1       FR2   \n",
       "\n",
       "  Neighborhood BldgType HouseStyle  ...  PoolArea  Fence  MiscVal  MoSold  \\\n",
       "0      CollgCr     1Fam     2Story  ...         0   None        0       2   \n",
       "1      Veenker     1Fam     1Story  ...         0   None        0       5   \n",
       "2      CollgCr     1Fam     2Story  ...         0   None        0       9   \n",
       "3      Crawfor     1Fam     2Story  ...         0   None        0       2   \n",
       "4      NoRidge     1Fam     2Story  ...         0   None        0      12   \n",
       "\n",
       "  YrSold SaleCondition SalePrice Old_NewGarage  Remodelled Built_RemodelAge  \n",
       "0   2008        Normal    208500             1           0                5  \n",
       "1   2007        Normal    181500             0           0               31  \n",
       "2   2008        Normal    223500             1           1                6  \n",
       "3   2006       Abnorml    140000             0           1               36  \n",
       "4   2008        Normal    250000             1           0                8  \n",
       "\n",
       "[5 rows x 63 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# identifying built age at the time of selling\n",
    "\n",
    "def builtOremodelledAge(row):\n",
    "    if(row['YearBuilt'] == row['YearRemodAdd']):\n",
    "        return row['YrSold'] - row['YearBuilt']\n",
    "    else:\n",
    "        return row['YrSold'] - row['YearRemodAdd']\n",
    "       \n",
    "house['Built_RemodelAge'] = house.apply( builtOremodelledAge, axis=1)\n",
    "house.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#now we can drop these not required features\n",
    "house.drop(['YearBuilt', 'YearRemodAdd', 'YrSold', 'GarageYrBlt'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "#column id is not needed, so we can drop this too\n",
    "house.drop(['Id'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
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       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>...</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>Old_NewGarage</th>\n",
       "      <th>Remodelled</th>\n",
       "      <th>Built_RemodelAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Reg</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 58 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea LotShape LotConfig Neighborhood  \\\n",
       "0          60       RL         65.0     8450      Reg    Inside      CollgCr   \n",
       "1          20       RL         80.0     9600      Reg       FR2      Veenker   \n",
       "\n",
       "  BldgType HouseStyle  OverallQual  ...  ScreenPorch PoolArea Fence MiscVal  \\\n",
       "0     1Fam     2Story            7  ...            0        0  None       0   \n",
       "1     1Fam     1Story            6  ...            0        0  None       0   \n",
       "\n",
       "  MoSold  SaleCondition SalePrice Old_NewGarage Remodelled Built_RemodelAge  \n",
       "0      2         Normal    208500             1          0                5  \n",
       "1      5         Normal    181500             0          0               31  \n",
       "\n",
       "[2 rows x 58 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>...</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>Fence</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>Old_NewGarage</th>\n",
       "      <th>Remodelled</th>\n",
       "      <th>Built_RemodelAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>None</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Reg</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>None</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>None</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>None</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>IR1</td>\n",
       "      <td>FR2</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>None</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea LotShape LotConfig Neighborhood  \\\n",
       "0          60       RL         65.0     8450      Reg    Inside      CollgCr   \n",
       "1          20       RL         80.0     9600      Reg       FR2      Veenker   \n",
       "2          60       RL         68.0    11250      IR1    Inside      CollgCr   \n",
       "3          70       RL         60.0     9550      IR1    Corner      Crawfor   \n",
       "4          60       RL         84.0    14260      IR1       FR2      NoRidge   \n",
       "\n",
       "  BldgType HouseStyle  OverallQual  ...  GarageCars GarageArea WoodDeckSF  \\\n",
       "0     1Fam     2Story            7  ...           2        548          0   \n",
       "1     1Fam     1Story            6  ...           2        460        298   \n",
       "2     1Fam     2Story            7  ...           2        608          0   \n",
       "3     1Fam     2Story            7  ...           3        642          0   \n",
       "4     1Fam     2Story            8  ...           3        836        192   \n",
       "\n",
       "  OpenPorchSF Fence  SaleCondition SalePrice Old_NewGarage Remodelled  \\\n",
       "0          61  None         Normal    208500             1          0   \n",
       "1           0  None         Normal    181500             0          0   \n",
       "2          42  None         Normal    223500             1          1   \n",
       "3          35  None        Abnorml    140000             0          1   \n",
       "4          84  None         Normal    250000             1          0   \n",
       "\n",
       "  Built_RemodelAge  \n",
       "0                5  \n",
       "1               31  \n",
       "2                6  \n",
       "3               36  \n",
       "4                8  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#As we did for categorical variables, here we drop variables with value counts higher than 85% (1241/1450)*100=85%\n",
    "#to avoid extremeties \n",
    "def highNumericalvaluecounts():\n",
    "    column = []\n",
    "    numerical_columns = house.select_dtypes(include=['int64', 'float'])\n",
    "    for col in (numerical_columns):\n",
    "        if(house[col].value_counts().max() >= 1241):\n",
    "            column.append(col)\n",
    "    return column\n",
    "\n",
    "columnsToBeRemoved = highNumericalvaluecounts()\n",
    "house.drop(columnsToBeRemoved, axis = 1, inplace = True)\n",
    "\n",
    "house.drop(['MoSold'], axis = 1, inplace = True)\n",
    "\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Handling the outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>...</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>Old_NewGarage</th>\n",
       "      <th>Remodelled</th>\n",
       "      <th>Built_RemodelAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>56.897260</td>\n",
       "      <td>69.863699</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>103.117123</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>567.240411</td>\n",
       "      <td>1057.429452</td>\n",
       "      <td>1162.626712</td>\n",
       "      <td>...</td>\n",
       "      <td>6.517808</td>\n",
       "      <td>0.613014</td>\n",
       "      <td>1.767123</td>\n",
       "      <td>472.980137</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>180921.195890</td>\n",
       "      <td>0.275342</td>\n",
       "      <td>0.476712</td>\n",
       "      <td>22.950000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42.300571</td>\n",
       "      <td>22.027677</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>180.731373</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>441.866955</td>\n",
       "      <td>438.705324</td>\n",
       "      <td>386.587738</td>\n",
       "      <td>...</td>\n",
       "      <td>1.625393</td>\n",
       "      <td>0.644666</td>\n",
       "      <td>0.747315</td>\n",
       "      <td>213.804841</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>79442.502883</td>\n",
       "      <td>0.446840</td>\n",
       "      <td>0.499629</td>\n",
       "      <td>20.640653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>795.750000</td>\n",
       "      <td>882.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>334.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>477.500000</td>\n",
       "      <td>991.500000</td>\n",
       "      <td>1087.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>164.250000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>1298.250000</td>\n",
       "      <td>1391.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>41.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>120.000000</td>\n",
       "      <td>92.000000</td>\n",
       "      <td>14381.700000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>1065.500000</td>\n",
       "      <td>1232.000000</td>\n",
       "      <td>1602.200000</td>\n",
       "      <td>1680.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>757.100000</td>\n",
       "      <td>262.000000</td>\n",
       "      <td>130.000000</td>\n",
       "      <td>278000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>56.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95%</th>\n",
       "      <td>160.000000</td>\n",
       "      <td>104.000000</td>\n",
       "      <td>17401.150000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>456.000000</td>\n",
       "      <td>1274.000000</td>\n",
       "      <td>1468.000000</td>\n",
       "      <td>1753.000000</td>\n",
       "      <td>1831.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>850.100000</td>\n",
       "      <td>335.000000</td>\n",
       "      <td>175.050000</td>\n",
       "      <td>326100.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>58.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>137.410000</td>\n",
       "      <td>37567.640000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>791.280000</td>\n",
       "      <td>1572.410000</td>\n",
       "      <td>1797.050000</td>\n",
       "      <td>2155.050000</td>\n",
       "      <td>2219.460000</td>\n",
       "      <td>...</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1002.790000</td>\n",
       "      <td>505.460000</td>\n",
       "      <td>285.820000</td>\n",
       "      <td>442567.010000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>2336.000000</td>\n",
       "      <td>6110.000000</td>\n",
       "      <td>4692.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1418.000000</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>60.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MSSubClass  LotFrontage        LotArea  OverallQual  OverallCond  \\\n",
       "count  1460.000000  1460.000000    1460.000000  1460.000000  1460.000000   \n",
       "mean     56.897260    69.863699   10516.828082     6.099315     5.575342   \n",
       "std      42.300571    22.027677    9981.264932     1.382997     1.112799   \n",
       "min      20.000000    21.000000    1300.000000     1.000000     1.000000   \n",
       "25%      20.000000    60.000000    7553.500000     5.000000     5.000000   \n",
       "50%      50.000000    69.000000    9478.500000     6.000000     5.000000   \n",
       "75%      70.000000    79.000000   11601.500000     7.000000     6.000000   \n",
       "90%     120.000000    92.000000   14381.700000     8.000000     7.000000   \n",
       "95%     160.000000   104.000000   17401.150000     8.000000     8.000000   \n",
       "99%     190.000000   137.410000   37567.640000    10.000000     9.000000   \n",
       "max     190.000000   313.000000  215245.000000    10.000000     9.000000   \n",
       "\n",
       "        MasVnrArea   BsmtFinSF1    BsmtUnfSF  TotalBsmtSF     1stFlrSF  ...  \\\n",
       "count  1460.000000  1460.000000  1460.000000  1460.000000  1460.000000  ...   \n",
       "mean    103.117123   443.639726   567.240411  1057.429452  1162.626712  ...   \n",
       "std     180.731373   456.098091   441.866955   438.705324   386.587738  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000   334.000000  ...   \n",
       "25%       0.000000     0.000000   223.000000   795.750000   882.000000  ...   \n",
       "50%       0.000000   383.500000   477.500000   991.500000  1087.000000  ...   \n",
       "75%     164.250000   712.250000   808.000000  1298.250000  1391.250000  ...   \n",
       "90%     335.000000  1065.500000  1232.000000  1602.200000  1680.000000  ...   \n",
       "95%     456.000000  1274.000000  1468.000000  1753.000000  1831.250000  ...   \n",
       "99%     791.280000  1572.410000  1797.050000  2155.050000  2219.460000  ...   \n",
       "max    1600.000000  5644.000000  2336.000000  6110.000000  4692.000000  ...   \n",
       "\n",
       "       TotRmsAbvGrd   Fireplaces   GarageCars   GarageArea   WoodDeckSF  \\\n",
       "count   1460.000000  1460.000000  1460.000000  1460.000000  1460.000000   \n",
       "mean       6.517808     0.613014     1.767123   472.980137    94.244521   \n",
       "std        1.625393     0.644666     0.747315   213.804841   125.338794   \n",
       "min        2.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%        5.000000     0.000000     1.000000   334.500000     0.000000   \n",
       "50%        6.000000     1.000000     2.000000   480.000000     0.000000   \n",
       "75%        7.000000     1.000000     2.000000   576.000000   168.000000   \n",
       "90%        9.000000     1.000000     3.000000   757.100000   262.000000   \n",
       "95%       10.000000     2.000000     3.000000   850.100000   335.000000   \n",
       "99%       11.000000     2.000000     3.000000  1002.790000   505.460000   \n",
       "max       14.000000     3.000000     4.000000  1418.000000   857.000000   \n",
       "\n",
       "       OpenPorchSF      SalePrice  Old_NewGarage   Remodelled  \\\n",
       "count  1460.000000    1460.000000    1460.000000  1460.000000   \n",
       "mean     46.660274  180921.195890       0.275342     0.476712   \n",
       "std      66.256028   79442.502883       0.446840     0.499629   \n",
       "min       0.000000   34900.000000       0.000000     0.000000   \n",
       "25%       0.000000  129975.000000       0.000000     0.000000   \n",
       "50%      25.000000  163000.000000       0.000000     0.000000   \n",
       "75%      68.000000  214000.000000       1.000000     1.000000   \n",
       "90%     130.000000  278000.000000       1.000000     1.000000   \n",
       "95%     175.050000  326100.000000       1.000000     1.000000   \n",
       "99%     285.820000  442567.010000       1.000000     1.000000   \n",
       "max     547.000000  755000.000000       1.000000     1.000000   \n",
       "\n",
       "       Built_RemodelAge  \n",
       "count       1460.000000  \n",
       "mean          22.950000  \n",
       "std           20.640653  \n",
       "min           -1.000000  \n",
       "25%            4.000000  \n",
       "50%           14.000000  \n",
       "75%           41.000000  \n",
       "90%           56.000000  \n",
       "95%           58.000000  \n",
       "99%           60.000000  \n",
       "max           60.000000  \n",
       "\n",
       "[11 rows x 26 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check outliers at 25%, 50%, 75%, 90%, 95%, 98% and above in the housing dataset\n",
    "house.describe(percentiles=[.25,.5,.75,.90,.95,.99])\n"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Extreme outliers i,e. beyond 98% is visualized below using box plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.boxplot(y = 'LotArea', palette='gist_heat', data = house)\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ZYZGORawAFgD/ADwh6bPjnVKkreiLgZLWkrxIyLx588otzayijh49Ou62WS0rtWfRCiyOkT702fk00BsR/QCSvgN8DHhT0py0VzEHOJ4e3wcUzuXcTHLb6pdExGZgM0Bra6vfNLeaNjn/czKrjlIHuF8heZt7MrwOXCvpfCUPnd8IvEoyQeHq9JjVJC8AkravkjRT0gKSdzz2TVItZlUza9asM7Yvvtjrh1n9KLVn0QgclLQPeG/2s4hYXu4FI+JFSd8CfggMAX9D0hv4FWC7pNtJAuWW9PgDkrYDB9Pj10fE6aI/blbDBgbOXI24v78/p0rMyldqWNw3mReNiK8AXxnVfJKkl1Hs+I0kc1SZmVkOxg0LSX8K/FVEeHpMM7NzWNaYxWHgTyT9RNIfSPqXVajJbEryrLNWz7Jmnf16RFwHfAJ4C9gi6VVJX5Z0eVUqNJsiRuaFGtHS0jLGkWa1p9Q3uH8aEX8QER8B/j3wWyRPMJlZiTzAbfWs1DW4GyT9pqTHgKeB14DPVLQysylm9JrbN9xwQz6FmE1A1gD3EuBW4GbgRWAbsDYi/qkKtZlNKR6jsHqW1bP4XWAv8OGI+M2IeMxBYTYxo9fc9hrcVk+yBrg/GRF/DsySNBNA0g2SviDpomoUaDZVtLW1jbttVstKne7j28BpSQuBb5BMAug1uM3KsHz5mRMerFy5Mp9CzCag1LAYjoghkqegNkXEbwNzKleW2dTzzW9+84ztbdu25VSJWflKDYtBSbeSTPD3VNrWUJmSzKam3bt3n7H9zDPP5FSJWflKDYs1wHXAxojoTWd//cvKlWU29Zw+fXrcbbNaVtJEghFxkGTd7JHtXuBrlSrKzMxqS6kr5S0C/huwGHjfSPtZrsNtZmZ1otTbUFuAh0jWk/gkyXKqf1GposzMrLaUGhbvj4g9gNJ5ou4DPlW5sszMrJaUGhY/lzQNOCzpDkm/BVwy0YtKukjStyT9bTqL7XWSLpa0W9Lh9HNWwfEbJPVIOiTJbzKZmVVZqWFxN3A+ySD31cDn+MV62RPxdaAzIj4M/BrJDLb3AHsiYhGwJ91G0mJgFXAlsBR4UNL0s7i2mZmVqdSnof46/fr/SB6jnTBJFwIfB9rT3z4FnJK0ArghPWwr8H3gS8AKYFtEnAR6JfUA15DMWWVmZlWQNevsjvH2R8Ty8faP4YNAP8lCSr8GvATcBVwaEcfS3z0maeQ211zghYLz+9K2YvWuBdYCzJs3bwKlmZlZMVk9i+uAI8DjJFOUT8YcyzOAXwfujIgXJX2d9JbTGIpdM4odGBGbgc0Ara2tRY8xM7PyZY1ZXEYyTflVJOMMS4CBiHguIiY6v3If0BcRL6bb3yIJjzclzQFIP48XHF+4/mQzcHSC1zYzswnImqL8dER0RsRq4FqgB/i+pDsnesGIeAM4IumKtOlG4CCwg18Mmq8Gnky/7wBWSZqZTjOyCNg30eubmVn5Mge403UslpGsmDcfuB/4zlle907gMUnnAX9HMmg+Ddgu6XbgdeAWgIg4IGk7SaAMAesjwpPqmJlVUdYA91aSW1BPA78fEa9MxkUj4kdAa5FdN45x/EZg42Rc28zMypfVs/gc8E/A5cAXCtYQFhARcWEFazMzsxoxblhERKkv7ZlZhqamJvr7+8/YNqsXDgOzKikMimLbZrXMYWFmZpkcFmZmlslhYWZmmRwWZmaWyWFhZmaZHBZmZpbJYWFmZpkcFmZmlslhYWZmmRwWZmaWyWFhZmaZHBZmZpYpt7CQNF3S30h6Kt2+WNJuSYfTz1kFx26Q1CPpkKS2vGo2MztX5dmzuAt4tWD7HmBPRCwC9qTbSFoMrAKuBJYCD0qaXuVazczOaZnLqlaCpGaSpVo3Av8xbV4B3JB+3wp8H/hS2r4tIk4CvZJ6gGuAvVUs2c7Cpk2b6OnpybuMmnTHHXfkXUKuFi5cyN133513GVaCvHoWm4AvAsMFbZdGxDGA9POStH0ucKTguL60zczMqqTqPQtJNwPHI+IlSTeUckqRthjjt9cCawHmzZs30RJtkvkvx8Rdd91Fd3f3e9sf/ehH2bRpU34FmZUhj57F9cByST8BtgGfkvSXwJuS5gCkn8fT4/uAloLzm4GjxX44IjZHRGtEtHrJSqs1995777jbZrWs6mERERsiojki5pMMXD8bEZ8FdgCr08NWA0+m33cAqyTNlLQAWATsq3LZZmetsbGRCy64AEh6FbNnz865IrPS1dJ7Fl8Dlkg6DCxJt4mIA8B24CDQCayPiNO5VWl2FhYsWMAFF1zgXoXVnVyehhoREd8neeqJiDgB3DjGcRtJnpwyq2sNDQ1cfvnl7lVY3amlnoWZmdUoh4WZmWVyWJiZWSaHhZmZZXJYmJlZJoeFmZllcliYmVkmh4WZmWVyWJiZWSaHhZmZZXJYmJlZJoeFmZllcliYmVkmh4WZmWVyWJiZWSaHhZmZZap6WEhqkfS/JL0q6YCku9L2iyXtlnQ4/ZxVcM4GST2SDklqq3bNZmbnujx6FkPAf4qIfwFcC6yXtBi4B9gTEYuAPek26b5VwJXAUuBBSdNzqNvM7JxV9bCIiGMR8cP0+9vAq8BcYAWwNT1sK7Ay/b4C2BYRJyOiF+gBrqlq0WZm57hcxywkzQc+ArwIXBoRxyAJFOCS9LC5wJGC0/rSNjMzq5LcwkLSrwDfBu6OiH8c79AibTHGb66V1C2pu7+/fzLKNDMzcgoLSQ0kQfFYRHwnbX5T0px0/xzgeNreB7QUnN4MHC32uxGxOSJaI6K1qampMsWbmZ2D8ngaSsA3gFcj4r8X7NoBrE6/rwaeLGhfJWmmpAXAImBfteo1MzOYkcM1rwc+B7ws6Udp2+8CXwO2S7odeB24BSAiDkjaDhwkeZJqfUScrnrVZdq0aRM9PT15l2E15vDhwwDccccdOVditWbhwoXcfffdeZcxpqqHRUT8H4qPQwDcOMY5G4GNFSuqAnp6ejjQ3U3TqVN5l2I1JBoaADj+gx/kXInVkv7zzsu7hEx59CzOGU2nTvGZN47lXYaZ1bhvXzYn7xIyeboPMzPL5LAwM7NMDgszM8vksDAzs0wOCzMzy+SwMDOzTA4LMzPL5LAwM7NMDgszM8vksDAzs0wOCzMzy+SwMDOzTA4LMzPL5LAwM7NMDgszM8tUN2EhaamkQ5J6JN2Tdz1mZueSuggLSdOBB4B/DSwGbpW0ON+qzMzOHfWyUt41QE9E/B2ApG3ACpJ1uWtSX18fA+97H3827wN5l5K7oWnTiLyLsJokYMbwcN5l5G5w2jRO9fXlXca46iUs5gJHCrb7gN8YfZCktcBagHnz5lWnsjFcdNFFvPvuu7nWUCvi5ElOnz6ddxlWg6ZPn855M2fmXUbuziP5/4xaVi9hoSJtv/THakRsBjYDtLa25vrHbEdHR56XNzObVHUxZkHSk2gp2G4GjuZUi5nZOadewuKvgUWSFkg6D1gF7Mi5JjOzc0Zd3IaKiCFJdwBdwHTgkYg4kHNZZmbnjLoIC4CI2AXsyrsOM7NzUb3chjIzsxw5LMzMLJPDwszMMjkszMwskyKm5kQMkvqBn+Zdh1kRjcBA3kWYjeEDEdE0unHKhoVZrZLUHRGteddhVg7fhjIzs0wOCzMzy+SwMKu+zXkXYFYuj1mYmVkm9yzMzCyTw8LMzDI5LMzMLJPDwszMMjkszMws0/8HVeGFuOkUHzsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(y = 'MasVnrArea', palette='gist_heat', data = house)\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.boxplot(y = 'TotalBsmtSF', palette='gist_heat', data = house)\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.boxplot(y = 'WoodDeckSF', palette='gist_heat', data = house)\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.boxplot(y = 'OpenPorchSF', palette='gist_heat', data = house)\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Remove the outliers which are 98% and above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "nn_quartile_LotArea = house['LotArea'].quantile(0.98)\n",
    "house = house[house[\"LotArea\"] < nn_quartile_LotArea]\n",
    "\n",
    "nn_quartile_MasVnrArea = house['MasVnrArea'].quantile(0.98)\n",
    "house = house[house[\"MasVnrArea\"] < nn_quartile_MasVnrArea]\n",
    "\n",
    "nn_quartile_TotalBsmtSF = house['TotalBsmtSF'].quantile(0.99)\n",
    "house = house[house[\"TotalBsmtSF\"] < nn_quartile_TotalBsmtSF]\n",
    "\n",
    "nn_quartile_WoodDeckSF = house['WoodDeckSF'].quantile(0.99)\n",
    "house =house[house[\"WoodDeckSF\"] < nn_quartile_WoodDeckSF]\n",
    "\n",
    "nn_quartile_OpenPorchSF = house['OpenPorchSF'].quantile(0.99)\n",
    "house = house[house[\"OpenPorchSF\"] < nn_quartile_OpenPorchSF]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "93.01\n"
     ]
    }
   ],
   "source": [
    "num_data = round(100*(len(house)/1460),2)\n",
    "print(num_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'LotShape',\n",
       "       'LotConfig', 'Neighborhood', 'BldgType', 'HouseStyle', 'OverallQual',\n",
       "       'OverallCond', 'RoofStyle', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'Foundation', 'BsmtQual', 'BsmtExposure',\n",
       "       'BsmtFinType1', 'BsmtFinSF1', 'BsmtUnfSF', 'TotalBsmtSF', 'HeatingQC',\n",
       "       '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenQual', 'TotRmsAbvGrd', 'Fireplaces',\n",
       "       'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageCars', 'GarageArea',\n",
       "       'WoodDeckSF', 'OpenPorchSF', 'Fence', 'SaleCondition', 'SalePrice',\n",
       "       'Old_NewGarage', 'Remodelled', 'Built_RemodelAge'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>...</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>Old_NewGarage</th>\n",
       "      <th>Remodelled</th>\n",
       "      <th>Built_RemodelAge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.401931</td>\n",
       "      <td>-0.393699</td>\n",
       "      <td>0.060222</td>\n",
       "      <td>-0.059830</td>\n",
       "      <td>0.024548</td>\n",
       "      <td>-0.071873</td>\n",
       "      <td>-0.129118</td>\n",
       "      <td>-0.245604</td>\n",
       "      <td>-0.253736</td>\n",
       "      <td>...</td>\n",
       "      <td>0.051450</td>\n",
       "      <td>-0.031299</td>\n",
       "      <td>-0.040892</td>\n",
       "      <td>-0.105249</td>\n",
       "      <td>-0.030496</td>\n",
       "      <td>0.004843</td>\n",
       "      <td>-0.069081</td>\n",
       "      <td>0.040748</td>\n",
       "      <td>-0.047843</td>\n",
       "      <td>-0.048133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>-0.401931</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.570154</td>\n",
       "      <td>0.190775</td>\n",
       "      <td>-0.051785</td>\n",
       "      <td>0.162426</td>\n",
       "      <td>0.107511</td>\n",
       "      <td>0.141324</td>\n",
       "      <td>0.288790</td>\n",
       "      <td>0.344437</td>\n",
       "      <td>...</td>\n",
       "      <td>0.305140</td>\n",
       "      <td>0.203051</td>\n",
       "      <td>0.272192</td>\n",
       "      <td>0.304673</td>\n",
       "      <td>0.079295</td>\n",
       "      <td>0.134818</td>\n",
       "      <td>0.344203</td>\n",
       "      <td>0.078414</td>\n",
       "      <td>-0.046754</td>\n",
       "      <td>-0.065145</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>-0.393699</td>\n",
       "      <td>0.570154</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.171768</td>\n",
       "      <td>-0.005919</td>\n",
       "      <td>0.096369</td>\n",
       "      <td>0.142376</td>\n",
       "      <td>0.092427</td>\n",
       "      <td>0.287169</td>\n",
       "      <td>0.391483</td>\n",
       "      <td>...</td>\n",
       "      <td>0.370356</td>\n",
       "      <td>0.308544</td>\n",
       "      <td>0.258948</td>\n",
       "      <td>0.296699</td>\n",
       "      <td>0.136089</td>\n",
       "      <td>0.133542</td>\n",
       "      <td>0.377013</td>\n",
       "      <td>-0.017580</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>-0.039391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>0.060222</td>\n",
       "      <td>0.190775</td>\n",
       "      <td>0.171768</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.088590</td>\n",
       "      <td>0.366129</td>\n",
       "      <td>0.193002</td>\n",
       "      <td>0.285256</td>\n",
       "      <td>0.507715</td>\n",
       "      <td>0.429465</td>\n",
       "      <td>...</td>\n",
       "      <td>0.386576</td>\n",
       "      <td>0.377691</td>\n",
       "      <td>0.586480</td>\n",
       "      <td>0.539509</td>\n",
       "      <td>0.219328</td>\n",
       "      <td>0.358891</td>\n",
       "      <td>0.800134</td>\n",
       "      <td>0.563296</td>\n",
       "      <td>-0.100826</td>\n",
       "      <td>-0.548074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>-0.059830</td>\n",
       "      <td>-0.051785</td>\n",
       "      <td>-0.005919</td>\n",
       "      <td>-0.088590</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.125851</td>\n",
       "      <td>-0.017521</td>\n",
       "      <td>-0.143153</td>\n",
       "      <td>-0.162739</td>\n",
       "      <td>-0.136004</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.047843</td>\n",
       "      <td>-0.011391</td>\n",
       "      <td>-0.177895</td>\n",
       "      <td>-0.136103</td>\n",
       "      <td>0.003890</td>\n",
       "      <td>-0.055243</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>-0.290144</td>\n",
       "      <td>0.317140</td>\n",
       "      <td>-0.073388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>0.024548</td>\n",
       "      <td>0.162426</td>\n",
       "      <td>0.096369</td>\n",
       "      <td>0.366129</td>\n",
       "      <td>-0.125851</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.213629</td>\n",
       "      <td>0.086560</td>\n",
       "      <td>0.307658</td>\n",
       "      <td>0.299629</td>\n",
       "      <td>...</td>\n",
       "      <td>0.241253</td>\n",
       "      <td>0.224874</td>\n",
       "      <td>0.336142</td>\n",
       "      <td>0.338542</td>\n",
       "      <td>0.123847</td>\n",
       "      <td>0.164114</td>\n",
       "      <td>0.407368</td>\n",
       "      <td>0.165754</td>\n",
       "      <td>-0.162473</td>\n",
       "      <td>-0.142217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>-0.071873</td>\n",
       "      <td>0.107511</td>\n",
       "      <td>0.142376</td>\n",
       "      <td>0.193002</td>\n",
       "      <td>-0.017521</td>\n",
       "      <td>0.213629</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.558481</td>\n",
       "      <td>0.427601</td>\n",
       "      <td>0.348825</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.038561</td>\n",
       "      <td>0.204278</td>\n",
       "      <td>0.189729</td>\n",
       "      <td>0.230009</td>\n",
       "      <td>0.195079</td>\n",
       "      <td>0.064733</td>\n",
       "      <td>0.354492</td>\n",
       "      <td>0.011541</td>\n",
       "      <td>-0.126288</td>\n",
       "      <td>-0.121970</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>-0.129118</td>\n",
       "      <td>0.141324</td>\n",
       "      <td>0.092427</td>\n",
       "      <td>0.285256</td>\n",
       "      <td>-0.143153</td>\n",
       "      <td>0.086560</td>\n",
       "      <td>-0.558481</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.437582</td>\n",
       "      <td>0.311913</td>\n",
       "      <td>...</td>\n",
       "      <td>0.238135</td>\n",
       "      <td>0.039859</td>\n",
       "      <td>0.204051</td>\n",
       "      <td>0.175354</td>\n",
       "      <td>-0.040374</td>\n",
       "      <td>0.137854</td>\n",
       "      <td>0.203009</td>\n",
       "      <td>0.335792</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>-0.174480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>-0.245604</td>\n",
       "      <td>0.288790</td>\n",
       "      <td>0.287169</td>\n",
       "      <td>0.507715</td>\n",
       "      <td>-0.162739</td>\n",
       "      <td>0.307658</td>\n",
       "      <td>0.427601</td>\n",
       "      <td>0.437582</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.770977</td>\n",
       "      <td>...</td>\n",
       "      <td>0.214726</td>\n",
       "      <td>0.277055</td>\n",
       "      <td>0.421145</td>\n",
       "      <td>0.444024</td>\n",
       "      <td>0.202218</td>\n",
       "      <td>0.224610</td>\n",
       "      <td>0.606054</td>\n",
       "      <td>0.329473</td>\n",
       "      <td>-0.110048</td>\n",
       "      <td>-0.303012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>-0.253736</td>\n",
       "      <td>0.344437</td>\n",
       "      <td>0.391483</td>\n",
       "      <td>0.429465</td>\n",
       "      <td>-0.136004</td>\n",
       "      <td>0.299629</td>\n",
       "      <td>0.348825</td>\n",
       "      <td>0.311913</td>\n",
       "      <td>0.770977</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.361194</td>\n",
       "      <td>0.360585</td>\n",
       "      <td>0.419848</td>\n",
       "      <td>0.447568</td>\n",
       "      <td>0.188469</td>\n",
       "      <td>0.182961</td>\n",
       "      <td>0.584060</td>\n",
       "      <td>0.217796</td>\n",
       "      <td>-0.042623</td>\n",
       "      <td>-0.242892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>0.317736</td>\n",
       "      <td>0.058318</td>\n",
       "      <td>0.117686</td>\n",
       "      <td>0.274435</td>\n",
       "      <td>0.031213</td>\n",
       "      <td>0.106767</td>\n",
       "      <td>-0.194927</td>\n",
       "      <td>-0.004303</td>\n",
       "      <td>-0.252999</td>\n",
       "      <td>-0.277935</td>\n",
       "      <td>...</td>\n",
       "      <td>0.613164</td>\n",
       "      <td>0.188738</td>\n",
       "      <td>0.169987</td>\n",
       "      <td>0.122111</td>\n",
       "      <td>0.049387</td>\n",
       "      <td>0.221724</td>\n",
       "      <td>0.302405</td>\n",
       "      <td>0.068471</td>\n",
       "      <td>0.101400</td>\n",
       "      <td>-0.126112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>0.106203</td>\n",
       "      <td>0.305912</td>\n",
       "      <td>0.394339</td>\n",
       "      <td>0.556815</td>\n",
       "      <td>-0.067520</td>\n",
       "      <td>0.306692</td>\n",
       "      <td>0.070643</td>\n",
       "      <td>0.227733</td>\n",
       "      <td>0.330743</td>\n",
       "      <td>0.477915</td>\n",
       "      <td>...</td>\n",
       "      <td>0.831437</td>\n",
       "      <td>0.430994</td>\n",
       "      <td>0.449132</td>\n",
       "      <td>0.428382</td>\n",
       "      <td>0.180318</td>\n",
       "      <td>0.337239</td>\n",
       "      <td>0.696071</td>\n",
       "      <td>0.213452</td>\n",
       "      <td>0.073066</td>\n",
       "      <td>-0.284055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>0.006396</td>\n",
       "      <td>0.031903</td>\n",
       "      <td>0.065287</td>\n",
       "      <td>0.098288</td>\n",
       "      <td>-0.035720</td>\n",
       "      <td>0.092870</td>\n",
       "      <td>0.651551</td>\n",
       "      <td>-0.428141</td>\n",
       "      <td>0.282508</td>\n",
       "      <td>0.205899</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.085609</td>\n",
       "      <td>0.097903</td>\n",
       "      <td>0.110291</td>\n",
       "      <td>0.147879</td>\n",
       "      <td>0.164522</td>\n",
       "      <td>0.057039</td>\n",
       "      <td>0.217104</td>\n",
       "      <td>0.024967</td>\n",
       "      <td>-0.068157</td>\n",
       "      <td>-0.122513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullBath</th>\n",
       "      <td>0.147537</td>\n",
       "      <td>0.169123</td>\n",
       "      <td>0.187991</td>\n",
       "      <td>0.539660</td>\n",
       "      <td>-0.202994</td>\n",
       "      <td>0.239007</td>\n",
       "      <td>0.029620</td>\n",
       "      <td>0.266432</td>\n",
       "      <td>0.305160</td>\n",
       "      <td>0.365099</td>\n",
       "      <td>...</td>\n",
       "      <td>0.530166</td>\n",
       "      <td>0.226585</td>\n",
       "      <td>0.470290</td>\n",
       "      <td>0.411200</td>\n",
       "      <td>0.186001</td>\n",
       "      <td>0.311626</td>\n",
       "      <td>0.573275</td>\n",
       "      <td>0.437739</td>\n",
       "      <td>-0.091317</td>\n",
       "      <td>-0.440756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath</th>\n",
       "      <td>0.187404</td>\n",
       "      <td>0.040485</td>\n",
       "      <td>0.092749</td>\n",
       "      <td>0.246581</td>\n",
       "      <td>-0.050472</td>\n",
       "      <td>0.162853</td>\n",
       "      <td>-0.031305</td>\n",
       "      <td>-0.062549</td>\n",
       "      <td>-0.113918</td>\n",
       "      <td>-0.187185</td>\n",
       "      <td>...</td>\n",
       "      <td>0.333775</td>\n",
       "      <td>0.197636</td>\n",
       "      <td>0.193556</td>\n",
       "      <td>0.132143</td>\n",
       "      <td>0.071854</td>\n",
       "      <td>0.213432</td>\n",
       "      <td>0.267858</td>\n",
       "      <td>0.076901</td>\n",
       "      <td>-0.095517</td>\n",
       "      <td>-0.168496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>-0.032854</td>\n",
       "      <td>0.256733</td>\n",
       "      <td>0.285788</td>\n",
       "      <td>0.109499</td>\n",
       "      <td>0.014373</td>\n",
       "      <td>0.119338</td>\n",
       "      <td>-0.118604</td>\n",
       "      <td>0.161291</td>\n",
       "      <td>0.050113</td>\n",
       "      <td>0.131634</td>\n",
       "      <td>...</td>\n",
       "      <td>0.696947</td>\n",
       "      <td>0.107451</td>\n",
       "      <td>0.085815</td>\n",
       "      <td>0.075006</td>\n",
       "      <td>0.025231</td>\n",
       "      <td>0.102451</td>\n",
       "      <td>0.184950</td>\n",
       "      <td>-0.076318</td>\n",
       "      <td>0.008141</td>\n",
       "      <td>0.033651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>0.051450</td>\n",
       "      <td>0.305140</td>\n",
       "      <td>0.370356</td>\n",
       "      <td>0.386576</td>\n",
       "      <td>-0.047843</td>\n",
       "      <td>0.241253</td>\n",
       "      <td>-0.038561</td>\n",
       "      <td>0.238135</td>\n",
       "      <td>0.214726</td>\n",
       "      <td>0.361194</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.295986</td>\n",
       "      <td>0.340693</td>\n",
       "      <td>0.307983</td>\n",
       "      <td>0.113176</td>\n",
       "      <td>0.236318</td>\n",
       "      <td>0.513014</td>\n",
       "      <td>0.147266</td>\n",
       "      <td>0.064305</td>\n",
       "      <td>-0.171862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>-0.031299</td>\n",
       "      <td>0.203051</td>\n",
       "      <td>0.308544</td>\n",
       "      <td>0.377691</td>\n",
       "      <td>-0.011391</td>\n",
       "      <td>0.224874</td>\n",
       "      <td>0.204278</td>\n",
       "      <td>0.039859</td>\n",
       "      <td>0.277055</td>\n",
       "      <td>0.360585</td>\n",
       "      <td>...</td>\n",
       "      <td>0.295986</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.276506</td>\n",
       "      <td>0.236324</td>\n",
       "      <td>0.182376</td>\n",
       "      <td>0.171194</td>\n",
       "      <td>0.447660</td>\n",
       "      <td>0.066082</td>\n",
       "      <td>0.046228</td>\n",
       "      <td>-0.099869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>-0.040892</td>\n",
       "      <td>0.272192</td>\n",
       "      <td>0.258948</td>\n",
       "      <td>0.586480</td>\n",
       "      <td>-0.177895</td>\n",
       "      <td>0.336142</td>\n",
       "      <td>0.189729</td>\n",
       "      <td>0.204051</td>\n",
       "      <td>0.421145</td>\n",
       "      <td>0.419848</td>\n",
       "      <td>...</td>\n",
       "      <td>0.340693</td>\n",
       "      <td>0.276506</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.888469</td>\n",
       "      <td>0.209245</td>\n",
       "      <td>0.255260</td>\n",
       "      <td>0.643519</td>\n",
       "      <td>0.470487</td>\n",
       "      <td>-0.160422</td>\n",
       "      <td>-0.416031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>-0.105249</td>\n",
       "      <td>0.304673</td>\n",
       "      <td>0.296699</td>\n",
       "      <td>0.539509</td>\n",
       "      <td>-0.136103</td>\n",
       "      <td>0.338542</td>\n",
       "      <td>0.230009</td>\n",
       "      <td>0.175354</td>\n",
       "      <td>0.444024</td>\n",
       "      <td>0.447568</td>\n",
       "      <td>...</td>\n",
       "      <td>0.307983</td>\n",
       "      <td>0.236324</td>\n",
       "      <td>0.888469</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.207016</td>\n",
       "      <td>0.267820</td>\n",
       "      <td>0.631235</td>\n",
       "      <td>0.432165</td>\n",
       "      <td>-0.155576</td>\n",
       "      <td>-0.367153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>-0.030496</td>\n",
       "      <td>0.079295</td>\n",
       "      <td>0.136089</td>\n",
       "      <td>0.219328</td>\n",
       "      <td>0.003890</td>\n",
       "      <td>0.123847</td>\n",
       "      <td>0.195079</td>\n",
       "      <td>-0.040374</td>\n",
       "      <td>0.202218</td>\n",
       "      <td>0.188469</td>\n",
       "      <td>...</td>\n",
       "      <td>0.113176</td>\n",
       "      <td>0.182376</td>\n",
       "      <td>0.209245</td>\n",
       "      <td>0.207016</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.052953</td>\n",
       "      <td>0.299413</td>\n",
       "      <td>0.136778</td>\n",
       "      <td>-0.036002</td>\n",
       "      <td>-0.231531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>0.004843</td>\n",
       "      <td>0.134818</td>\n",
       "      <td>0.133542</td>\n",
       "      <td>0.358891</td>\n",
       "      <td>-0.055243</td>\n",
       "      <td>0.164114</td>\n",
       "      <td>0.064733</td>\n",
       "      <td>0.137854</td>\n",
       "      <td>0.224610</td>\n",
       "      <td>0.182961</td>\n",
       "      <td>...</td>\n",
       "      <td>0.236318</td>\n",
       "      <td>0.171194</td>\n",
       "      <td>0.255260</td>\n",
       "      <td>0.267820</td>\n",
       "      <td>0.052953</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.399439</td>\n",
       "      <td>0.268201</td>\n",
       "      <td>-0.025425</td>\n",
       "      <td>-0.291852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SalePrice</th>\n",
       "      <td>-0.069081</td>\n",
       "      <td>0.344203</td>\n",
       "      <td>0.377013</td>\n",
       "      <td>0.800134</td>\n",
       "      <td>-0.068653</td>\n",
       "      <td>0.407368</td>\n",
       "      <td>0.354492</td>\n",
       "      <td>0.203009</td>\n",
       "      <td>0.606054</td>\n",
       "      <td>0.584060</td>\n",
       "      <td>...</td>\n",
       "      <td>0.513014</td>\n",
       "      <td>0.447660</td>\n",
       "      <td>0.643519</td>\n",
       "      <td>0.631235</td>\n",
       "      <td>0.299413</td>\n",
       "      <td>0.399439</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.510298</td>\n",
       "      <td>-0.051476</td>\n",
       "      <td>-0.545670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Old_NewGarage</th>\n",
       "      <td>0.040748</td>\n",
       "      <td>0.078414</td>\n",
       "      <td>-0.017580</td>\n",
       "      <td>0.563296</td>\n",
       "      <td>-0.290144</td>\n",
       "      <td>0.165754</td>\n",
       "      <td>0.011541</td>\n",
       "      <td>0.335792</td>\n",
       "      <td>0.329473</td>\n",
       "      <td>0.217796</td>\n",
       "      <td>...</td>\n",
       "      <td>0.147266</td>\n",
       "      <td>0.066082</td>\n",
       "      <td>0.470487</td>\n",
       "      <td>0.432165</td>\n",
       "      <td>0.136778</td>\n",
       "      <td>0.268201</td>\n",
       "      <td>0.510298</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.152086</td>\n",
       "      <td>-0.590025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Remodelled</th>\n",
       "      <td>-0.047843</td>\n",
       "      <td>-0.046754</td>\n",
       "      <td>0.002721</td>\n",
       "      <td>-0.100826</td>\n",
       "      <td>0.317140</td>\n",
       "      <td>-0.162473</td>\n",
       "      <td>-0.126288</td>\n",
       "      <td>0.031564</td>\n",
       "      <td>-0.110048</td>\n",
       "      <td>-0.042623</td>\n",
       "      <td>...</td>\n",
       "      <td>0.064305</td>\n",
       "      <td>0.046228</td>\n",
       "      <td>-0.160422</td>\n",
       "      <td>-0.155576</td>\n",
       "      <td>-0.036002</td>\n",
       "      <td>-0.025425</td>\n",
       "      <td>-0.051476</td>\n",
       "      <td>-0.152086</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.067664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Built_RemodelAge</th>\n",
       "      <td>-0.048133</td>\n",
       "      <td>-0.065145</td>\n",
       "      <td>-0.039391</td>\n",
       "      <td>-0.548074</td>\n",
       "      <td>-0.073388</td>\n",
       "      <td>-0.142217</td>\n",
       "      <td>-0.121970</td>\n",
       "      <td>-0.174480</td>\n",
       "      <td>-0.303012</td>\n",
       "      <td>-0.242892</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.171862</td>\n",
       "      <td>-0.099869</td>\n",
       "      <td>-0.416031</td>\n",
       "      <td>-0.367153</td>\n",
       "      <td>-0.231531</td>\n",
       "      <td>-0.291852</td>\n",
       "      <td>-0.545670</td>\n",
       "      <td>-0.590025</td>\n",
       "      <td>-0.067664</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26 rows × 26 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  MSSubClass  LotFrontage   LotArea  OverallQual  OverallCond  \\\n",
       "MSSubClass          1.000000    -0.401931 -0.393699     0.060222    -0.059830   \n",
       "LotFrontage        -0.401931     1.000000  0.570154     0.190775    -0.051785   \n",
       "LotArea            -0.393699     0.570154  1.000000     0.171768    -0.005919   \n",
       "OverallQual         0.060222     0.190775  0.171768     1.000000    -0.088590   \n",
       "OverallCond        -0.059830    -0.051785 -0.005919    -0.088590     1.000000   \n",
       "MasVnrArea          0.024548     0.162426  0.096369     0.366129    -0.125851   \n",
       "BsmtFinSF1         -0.071873     0.107511  0.142376     0.193002    -0.017521   \n",
       "BsmtUnfSF          -0.129118     0.141324  0.092427     0.285256    -0.143153   \n",
       "TotalBsmtSF        -0.245604     0.288790  0.287169     0.507715    -0.162739   \n",
       "1stFlrSF           -0.253736     0.344437  0.391483     0.429465    -0.136004   \n",
       "2ndFlrSF            0.317736     0.058318  0.117686     0.274435     0.031213   \n",
       "GrLivArea           0.106203     0.305912  0.394339     0.556815    -0.067520   \n",
       "BsmtFullBath        0.006396     0.031903  0.065287     0.098288    -0.035720   \n",
       "FullBath            0.147537     0.169123  0.187991     0.539660    -0.202994   \n",
       "HalfBath            0.187404     0.040485  0.092749     0.246581    -0.050472   \n",
       "BedroomAbvGr       -0.032854     0.256733  0.285788     0.109499     0.014373   \n",
       "TotRmsAbvGrd        0.051450     0.305140  0.370356     0.386576    -0.047843   \n",
       "Fireplaces         -0.031299     0.203051  0.308544     0.377691    -0.011391   \n",
       "GarageCars         -0.040892     0.272192  0.258948     0.586480    -0.177895   \n",
       "GarageArea         -0.105249     0.304673  0.296699     0.539509    -0.136103   \n",
       "WoodDeckSF         -0.030496     0.079295  0.136089     0.219328     0.003890   \n",
       "OpenPorchSF         0.004843     0.134818  0.133542     0.358891    -0.055243   \n",
       "SalePrice          -0.069081     0.344203  0.377013     0.800134    -0.068653   \n",
       "Old_NewGarage       0.040748     0.078414 -0.017580     0.563296    -0.290144   \n",
       "Remodelled         -0.047843    -0.046754  0.002721    -0.100826     0.317140   \n",
       "Built_RemodelAge   -0.048133    -0.065145 -0.039391    -0.548074    -0.073388   \n",
       "\n",
       "                  MasVnrArea  BsmtFinSF1  BsmtUnfSF  TotalBsmtSF  1stFlrSF  \\\n",
       "MSSubClass          0.024548   -0.071873  -0.129118    -0.245604 -0.253736   \n",
       "LotFrontage         0.162426    0.107511   0.141324     0.288790  0.344437   \n",
       "LotArea             0.096369    0.142376   0.092427     0.287169  0.391483   \n",
       "OverallQual         0.366129    0.193002   0.285256     0.507715  0.429465   \n",
       "OverallCond        -0.125851   -0.017521  -0.143153    -0.162739 -0.136004   \n",
       "MasVnrArea          1.000000    0.213629   0.086560     0.307658  0.299629   \n",
       "BsmtFinSF1          0.213629    1.000000  -0.558481     0.427601  0.348825   \n",
       "BsmtUnfSF           0.086560   -0.558481   1.000000     0.437582  0.311913   \n",
       "TotalBsmtSF         0.307658    0.427601   0.437582     1.000000  0.770977   \n",
       "1stFlrSF            0.299629    0.348825   0.311913     0.770977  1.000000   \n",
       "2ndFlrSF            0.106767   -0.194927  -0.004303    -0.252999 -0.277935   \n",
       "GrLivArea           0.306692    0.070643   0.227733     0.330743  0.477915   \n",
       "BsmtFullBath        0.092870    0.651551  -0.428141     0.282508  0.205899   \n",
       "FullBath            0.239007    0.029620   0.266432     0.305160  0.365099   \n",
       "HalfBath            0.162853   -0.031305  -0.062549    -0.113918 -0.187185   \n",
       "BedroomAbvGr        0.119338   -0.118604   0.161291     0.050113  0.131634   \n",
       "TotRmsAbvGrd        0.241253   -0.038561   0.238135     0.214726  0.361194   \n",
       "Fireplaces          0.224874    0.204278   0.039859     0.277055  0.360585   \n",
       "GarageCars          0.336142    0.189729   0.204051     0.421145  0.419848   \n",
       "GarageArea          0.338542    0.230009   0.175354     0.444024  0.447568   \n",
       "WoodDeckSF          0.123847    0.195079  -0.040374     0.202218  0.188469   \n",
       "OpenPorchSF         0.164114    0.064733   0.137854     0.224610  0.182961   \n",
       "SalePrice           0.407368    0.354492   0.203009     0.606054  0.584060   \n",
       "Old_NewGarage       0.165754    0.011541   0.335792     0.329473  0.217796   \n",
       "Remodelled         -0.162473   -0.126288   0.031564    -0.110048 -0.042623   \n",
       "Built_RemodelAge   -0.142217   -0.121970  -0.174480    -0.303012 -0.242892   \n",
       "\n",
       "                  ...  TotRmsAbvGrd  Fireplaces  GarageCars  GarageArea  \\\n",
       "MSSubClass        ...      0.051450   -0.031299   -0.040892   -0.105249   \n",
       "LotFrontage       ...      0.305140    0.203051    0.272192    0.304673   \n",
       "LotArea           ...      0.370356    0.308544    0.258948    0.296699   \n",
       "OverallQual       ...      0.386576    0.377691    0.586480    0.539509   \n",
       "OverallCond       ...     -0.047843   -0.011391   -0.177895   -0.136103   \n",
       "MasVnrArea        ...      0.241253    0.224874    0.336142    0.338542   \n",
       "BsmtFinSF1        ...     -0.038561    0.204278    0.189729    0.230009   \n",
       "BsmtUnfSF         ...      0.238135    0.039859    0.204051    0.175354   \n",
       "TotalBsmtSF       ...      0.214726    0.277055    0.421145    0.444024   \n",
       "1stFlrSF          ...      0.361194    0.360585    0.419848    0.447568   \n",
       "2ndFlrSF          ...      0.613164    0.188738    0.169987    0.122111   \n",
       "GrLivArea         ...      0.831437    0.430994    0.449132    0.428382   \n",
       "BsmtFullBath      ...     -0.085609    0.097903    0.110291    0.147879   \n",
       "FullBath          ...      0.530166    0.226585    0.470290    0.411200   \n",
       "HalfBath          ...      0.333775    0.197636    0.193556    0.132143   \n",
       "BedroomAbvGr      ...      0.696947    0.107451    0.085815    0.075006   \n",
       "TotRmsAbvGrd      ...      1.000000    0.295986    0.340693    0.307983   \n",
       "Fireplaces        ...      0.295986    1.000000    0.276506    0.236324   \n",
       "GarageCars        ...      0.340693    0.276506    1.000000    0.888469   \n",
       "GarageArea        ...      0.307983    0.236324    0.888469    1.000000   \n",
       "WoodDeckSF        ...      0.113176    0.182376    0.209245    0.207016   \n",
       "OpenPorchSF       ...      0.236318    0.171194    0.255260    0.267820   \n",
       "SalePrice         ...      0.513014    0.447660    0.643519    0.631235   \n",
       "Old_NewGarage     ...      0.147266    0.066082    0.470487    0.432165   \n",
       "Remodelled        ...      0.064305    0.046228   -0.160422   -0.155576   \n",
       "Built_RemodelAge  ...     -0.171862   -0.099869   -0.416031   -0.367153   \n",
       "\n",
       "                  WoodDeckSF  OpenPorchSF  SalePrice  Old_NewGarage  \\\n",
       "MSSubClass         -0.030496     0.004843  -0.069081       0.040748   \n",
       "LotFrontage         0.079295     0.134818   0.344203       0.078414   \n",
       "LotArea             0.136089     0.133542   0.377013      -0.017580   \n",
       "OverallQual         0.219328     0.358891   0.800134       0.563296   \n",
       "OverallCond         0.003890    -0.055243  -0.068653      -0.290144   \n",
       "MasVnrArea          0.123847     0.164114   0.407368       0.165754   \n",
       "BsmtFinSF1          0.195079     0.064733   0.354492       0.011541   \n",
       "BsmtUnfSF          -0.040374     0.137854   0.203009       0.335792   \n",
       "TotalBsmtSF         0.202218     0.224610   0.606054       0.329473   \n",
       "1stFlrSF            0.188469     0.182961   0.584060       0.217796   \n",
       "2ndFlrSF            0.049387     0.221724   0.302405       0.068471   \n",
       "GrLivArea           0.180318     0.337239   0.696071       0.213452   \n",
       "BsmtFullBath        0.164522     0.057039   0.217104       0.024967   \n",
       "FullBath            0.186001     0.311626   0.573275       0.437739   \n",
       "HalfBath            0.071854     0.213432   0.267858       0.076901   \n",
       "BedroomAbvGr        0.025231     0.102451   0.184950      -0.076318   \n",
       "TotRmsAbvGrd        0.113176     0.236318   0.513014       0.147266   \n",
       "Fireplaces          0.182376     0.171194   0.447660       0.066082   \n",
       "GarageCars          0.209245     0.255260   0.643519       0.470487   \n",
       "GarageArea          0.207016     0.267820   0.631235       0.432165   \n",
       "WoodDeckSF          1.000000     0.052953   0.299413       0.136778   \n",
       "OpenPorchSF         0.052953     1.000000   0.399439       0.268201   \n",
       "SalePrice           0.299413     0.399439   1.000000       0.510298   \n",
       "Old_NewGarage       0.136778     0.268201   0.510298       1.000000   \n",
       "Remodelled         -0.036002    -0.025425  -0.051476      -0.152086   \n",
       "Built_RemodelAge   -0.231531    -0.291852  -0.545670      -0.590025   \n",
       "\n",
       "                  Remodelled  Built_RemodelAge  \n",
       "MSSubClass         -0.047843         -0.048133  \n",
       "LotFrontage        -0.046754         -0.065145  \n",
       "LotArea             0.002721         -0.039391  \n",
       "OverallQual        -0.100826         -0.548074  \n",
       "OverallCond         0.317140         -0.073388  \n",
       "MasVnrArea         -0.162473         -0.142217  \n",
       "BsmtFinSF1         -0.126288         -0.121970  \n",
       "BsmtUnfSF           0.031564         -0.174480  \n",
       "TotalBsmtSF        -0.110048         -0.303012  \n",
       "1stFlrSF           -0.042623         -0.242892  \n",
       "2ndFlrSF            0.101400         -0.126112  \n",
       "GrLivArea           0.073066         -0.284055  \n",
       "BsmtFullBath       -0.068157         -0.122513  \n",
       "FullBath           -0.091317         -0.440756  \n",
       "HalfBath           -0.095517         -0.168496  \n",
       "BedroomAbvGr        0.008141          0.033651  \n",
       "TotRmsAbvGrd        0.064305         -0.171862  \n",
       "Fireplaces          0.046228         -0.099869  \n",
       "GarageCars         -0.160422         -0.416031  \n",
       "GarageArea         -0.155576         -0.367153  \n",
       "WoodDeckSF         -0.036002         -0.231531  \n",
       "OpenPorchSF        -0.025425         -0.291852  \n",
       "SalePrice          -0.051476         -0.545670  \n",
       "Old_NewGarage      -0.152086         -0.590025  \n",
       "Remodelled          1.000000         -0.067664  \n",
       "Built_RemodelAge   -0.067664          1.000000  \n",
       "\n",
       "[26 rows x 26 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#check correlation of the numerical variables\n",
    "corr=house.corr()\n",
    "corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualizing the correlations using the heat map\n",
    "plt.figure(figsize=(20,10))\n",
    "sns.heatmap(corr, cmap=\"YlGnBu\", annot=True)\n",
    "plt.show;"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The heatmap shows some useful insights. Correlation amongst the independent variables:\n",
    "\n",
    "Independent variables GrLivArea,TotRmsAbvGrd, GarageArea, GarageCars are highly correlated.\n",
    "-GrLivArea and TotRmsAbvGrd = 83%\n",
    "-GarageArea and GarageCars = 89%\n",
    "\n",
    "Thus, while building the model, we'll have to pay attention to multicollinearity. Highly correlated two independent variables will be dropped."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 'TotRmsAbvGrd' and  'GarageArea' variables are dropped\n",
    "house.drop(['TotRmsAbvGrd', 'GarageArea'], axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1358, 46)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the shape of dataset\n",
    "house.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Data preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "#categorical variables are converted to numericals\n",
    "# Values are assigned to ordered lists. Sequence value for values which are in order, \n",
    "#if not sequence it is categorized 0 and 1\n",
    "\n",
    "house['d_LotShape'] = house['LotShape'].map({'Reg': 3, 'IR1': 2, 'IR2': 1, 'IR3': 0})\n",
    "house['d_ExterQual'] = house['ExterQual'].map({'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'None': 0 })\n",
    "house['d_BsmtQual'] = house['BsmtQual'].map({'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'None': 0})\n",
    "house['d_BsmtExposure'] = house['BsmtExposure'].map({'Gd': 4, 'Av': 3, 'Mn': 2, 'No': 1, 'None': 0})\n",
    "house['d_BsmtFinType1'] = house['BsmtFinType1'].map({'GLQ': 6, 'ALQ': 5, 'BLQ': 4, 'Rec': 3, 'LwQ': 2, 'Unf': 1, \n",
    "                                                                 'None': 0})\n",
    "house['d_HeatingQC'] = house['HeatingQC'].map({'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'None': 0})\n",
    "house['d_KitchenQual'] = house['KitchenQual'].map({'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'None': 0})\n",
    "house['d_FireplaceQu'] = house['FireplaceQu'].map({'Ex': 5, 'Gd': 4, 'TA': 3, 'Fa': 2, 'Po': 1, 'None': 0})\n",
    "house['d_GarageFinish'] = house['GarageFinish'].map({'Fin': 3, 'RFn': 2, 'Unf': 1, 'None': 0 })\n",
    "house['d_BldgType'] = house['BldgType'].map({'Twnhs': 5, 'TwnhsE': 4, 'Duplex': 3, '2fmCon': 2, '1Fam': 1, \n",
    "                                                                 'None': 0 })\n",
    "house['d_HouseStyle'] = house['HouseStyle'].map({'SLvl': 8, 'SFoyer': 7, '2.5Fin': 6, '2.5Unf': 5, '2Story': 4, \n",
    "                                                                 '1.5Fin': 3, '1.5Unf': 2, '1Story': 1, 'None': 0 })\n",
    "house['d_Fence'] = house['Fence'].map({'GdPrv': 4, 'GdWo': 3, 'MnPrv': 2, 'MnWw': 1, 'None': 0 })\n",
    "house['d_LotConfig'] = house['LotConfig'].map({'Inside': 5, 'Corner': 4, 'CulDSac': 3, 'FR2': 2, 'FR3': 1, \n",
    "                                                           'None': 0  })\n",
    "house['d_MasVnrType'] = house['MasVnrType'].map({'BrkCmn': 1, 'BrkFace': 1, 'CBlock': 1, 'Stone': 1, 'None': 0 })\n",
    "house['d_SaleCondition'] = house['SaleCondition'].map({'Normal': 1, 'Partial': 1, 'Abnorml': 0, 'Family': 0, \n",
    "                                                                   'Alloca': 0, 'AdjLand': 0, 'None': 0})\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>...</th>\n",
       "      <th>d_HeatingQC</th>\n",
       "      <th>d_KitchenQual</th>\n",
       "      <th>d_FireplaceQu</th>\n",
       "      <th>d_GarageFinish</th>\n",
       "      <th>d_BldgType</th>\n",
       "      <th>d_HouseStyle</th>\n",
       "      <th>d_Fence</th>\n",
       "      <th>d_LotConfig</th>\n",
       "      <th>d_MasVnrType</th>\n",
       "      <th>d_SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Reg</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Inside</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>IR1</td>\n",
       "      <td>FR2</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea LotShape LotConfig Neighborhood  \\\n",
       "0          60       RL         65.0     8450      Reg    Inside      CollgCr   \n",
       "1          20       RL         80.0     9600      Reg       FR2      Veenker   \n",
       "2          60       RL         68.0    11250      IR1    Inside      CollgCr   \n",
       "3          70       RL         60.0     9550      IR1    Corner      Crawfor   \n",
       "4          60       RL         84.0    14260      IR1       FR2      NoRidge   \n",
       "\n",
       "  BldgType HouseStyle  OverallQual  ...  d_HeatingQC d_KitchenQual  \\\n",
       "0     1Fam     2Story            7  ...            5             4   \n",
       "1     1Fam     1Story            6  ...            5             3   \n",
       "2     1Fam     2Story            7  ...            5             4   \n",
       "3     1Fam     2Story            7  ...            4             4   \n",
       "4     1Fam     2Story            8  ...            5             4   \n",
       "\n",
       "  d_FireplaceQu d_GarageFinish d_BldgType  d_HouseStyle d_Fence d_LotConfig  \\\n",
       "0             0              2          1             4       0           5   \n",
       "1             3              2          1             1       0           2   \n",
       "2             3              2          1             4       0           5   \n",
       "3             4              1          1             4       0           4   \n",
       "4             3              2          1             4       0           2   \n",
       "\n",
       "  d_MasVnrType d_SaleCondition  \n",
       "0            1               1  \n",
       "1            0               1  \n",
       "2            1               1  \n",
       "3            0               0  \n",
       "4            1               1  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop old columns \n",
    "house = house.drop(['LotShape', 'ExterQual', 'BsmtQual', 'BsmtExposure', 'BsmtFinType1', 'HeatingQC', \n",
    "                                'KitchenQual', 'FireplaceQu', 'GarageFinish', 'BldgType', 'HouseStyle', 'Fence', \n",
    "                                'LotConfig', 'MasVnrType', 'SaleCondition'], axis=1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>...</th>\n",
       "      <th>d_HeatingQC</th>\n",
       "      <th>d_KitchenQual</th>\n",
       "      <th>d_FireplaceQu</th>\n",
       "      <th>d_GarageFinish</th>\n",
       "      <th>d_BldgType</th>\n",
       "      <th>d_HouseStyle</th>\n",
       "      <th>d_Fence</th>\n",
       "      <th>d_LotConfig</th>\n",
       "      <th>d_MasVnrType</th>\n",
       "      <th>d_SaleCondition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Gable</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
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       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>Gable</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Gable</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Gable</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>Gable</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 46 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Neighborhood  OverallQual  \\\n",
       "0          60       RL         65.0     8450      CollgCr            7   \n",
       "1          20       RL         80.0     9600      Veenker            6   \n",
       "2          60       RL         68.0    11250      CollgCr            7   \n",
       "3          70       RL         60.0     9550      Crawfor            7   \n",
       "4          60       RL         84.0    14260      NoRidge            8   \n",
       "\n",
       "   OverallCond RoofStyle Exterior1st Exterior2nd  ...  d_HeatingQC  \\\n",
       "0            5     Gable     VinylSd     VinylSd  ...            5   \n",
       "1            8     Gable     MetalSd     MetalSd  ...            5   \n",
       "2            5     Gable     VinylSd     VinylSd  ...            5   \n",
       "3            5     Gable     Wd Sdng     Wd Shng  ...            4   \n",
       "4            5     Gable     VinylSd     VinylSd  ...            5   \n",
       "\n",
       "  d_KitchenQual  d_FireplaceQu  d_GarageFinish  d_BldgType  d_HouseStyle  \\\n",
       "0             4              0               2           1             4   \n",
       "1             3              3               2           1             1   \n",
       "2             4              3               2           1             4   \n",
       "3             4              4               1           1             4   \n",
       "4             4              3               2           1             4   \n",
       "\n",
       "   d_Fence  d_LotConfig  d_MasVnrType  d_SaleCondition  \n",
       "0        0            5             1                1  \n",
       "1        0            2             0                1  \n",
       "2        0            5             1                1  \n",
       "3        0            4             0                0  \n",
       "4        0            2             1                1  \n",
       "\n",
       "[5 rows x 46 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#dummies are created for categorical variables\n",
    "\n",
    "\n",
    "d_Exterior1st = pd.get_dummies(house['Exterior1st'], prefix='Exterior1st', drop_first = True)\n",
    "house = pd.concat([house, d_Exterior1st], axis = 1)\n",
    "\n",
    "d_Exterior2nd = pd.get_dummies(house['Exterior2nd'], prefix='Exterior2nd', drop_first = True)\n",
    "house = pd.concat([house, d_Exterior2nd], axis = 1)\n",
    "\n",
    "d_Foundation = pd.get_dummies(house['Foundation'], prefix='Foundation', drop_first = True)\n",
    "house = pd.concat([house, d_Foundation], axis = 1)\n",
    "\n",
    "d_GarageType = pd.get_dummies(house['GarageType'], prefix='GarageType', drop_first = True)\n",
    "house = pd.concat([house, d_GarageType], axis = 1)\n",
    "\n",
    "d_MSZoning = pd.get_dummies(house['MSZoning'], prefix='MSZoning', drop_first = True)\n",
    "house = pd.concat([house, d_MSZoning], axis = 1)\n",
    "\n",
    "d_Neighborhood = pd.get_dummies(house['Neighborhood'], prefix='Neighborhood', drop_first = True)\n",
    "house = pd.concat([house, d_Neighborhood], axis = 1)\n",
    "\n",
    "d_RoofStyle = pd.get_dummies(house['RoofStyle'], prefix='RoofStyle', drop_first = True)\n",
    "house = pd.concat([house, d_RoofStyle], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "        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>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>...</th>\n",
       "      <th>Neighborhood_SawyerW</th>\n",
       "      <th>Neighborhood_Somerst</th>\n",
       "      <th>Neighborhood_StoneBr</th>\n",
       "      <th>Neighborhood_Timber</th>\n",
       "      <th>Neighborhood_Veenker</th>\n",
       "      <th>RoofStyle_Gable</th>\n",
       "      <th>RoofStyle_Gambrel</th>\n",
       "      <th>RoofStyle_Hip</th>\n",
       "      <th>RoofStyle_Mansard</th>\n",
       "      <th>RoofStyle_Shed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Gable</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>Gable</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Gable</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>Gable</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>Gable</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 119 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass MSZoning  LotFrontage  LotArea Neighborhood  OverallQual  \\\n",
       "0          60       RL         65.0     8450      CollgCr            7   \n",
       "1          20       RL         80.0     9600      Veenker            6   \n",
       "2          60       RL         68.0    11250      CollgCr            7   \n",
       "3          70       RL         60.0     9550      Crawfor            7   \n",
       "4          60       RL         84.0    14260      NoRidge            8   \n",
       "\n",
       "   OverallCond RoofStyle Exterior1st Exterior2nd  ...  Neighborhood_SawyerW  \\\n",
       "0            5     Gable     VinylSd     VinylSd  ...                     0   \n",
       "1            8     Gable     MetalSd     MetalSd  ...                     0   \n",
       "2            5     Gable     VinylSd     VinylSd  ...                     0   \n",
       "3            5     Gable     Wd Sdng     Wd Shng  ...                     0   \n",
       "4            5     Gable     VinylSd     VinylSd  ...                     0   \n",
       "\n",
       "  Neighborhood_Somerst  Neighborhood_StoneBr  Neighborhood_Timber  \\\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",
       "   Neighborhood_Veenker  RoofStyle_Gable  RoofStyle_Gambrel  RoofStyle_Hip  \\\n",
       "0                     0                1                  0              0   \n",
       "1                     1                1                  0              0   \n",
       "2                     0                1                  0              0   \n",
       "3                     0                1                  0              0   \n",
       "4                     0                1                  0              0   \n",
       "\n",
       "   RoofStyle_Mansard  RoofStyle_Shed  \n",
       "0                  0               0  \n",
       "1                  0               0  \n",
       "2                  0               0  \n",
       "3                  0               0  \n",
       "4                  0               0  \n",
       "\n",
       "[5 rows x 119 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
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       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
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       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>...</th>\n",
       "      <th>Neighborhood_SawyerW</th>\n",
       "      <th>Neighborhood_Somerst</th>\n",
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       "      <th>Neighborhood_Timber</th>\n",
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       "  </thead>\n",
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       "      <td>196.0</td>\n",
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       "      <td>6</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>7</td>\n",
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       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
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       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>1145</td>\n",
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       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 112 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  MasVnrArea  \\\n",
       "0          60         65.0     8450            7            5       196.0   \n",
       "1          20         80.0     9600            6            8         0.0   \n",
       "2          60         68.0    11250            7            5       162.0   \n",
       "3          70         60.0     9550            7            5         0.0   \n",
       "4          60         84.0    14260            8            5       350.0   \n",
       "\n",
       "   BsmtFinSF1  BsmtUnfSF  TotalBsmtSF  1stFlrSF  ...  Neighborhood_SawyerW  \\\n",
       "0         706        150          856       856  ...                     0   \n",
       "1         978        284         1262      1262  ...                     0   \n",
       "2         486        434          920       920  ...                     0   \n",
       "3         216        540          756       961  ...                     0   \n",
       "4         655        490         1145      1145  ...                     0   \n",
       "\n",
       "   Neighborhood_Somerst  Neighborhood_StoneBr  Neighborhood_Timber  \\\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",
       "   Neighborhood_Veenker  RoofStyle_Gable  RoofStyle_Gambrel  RoofStyle_Hip  \\\n",
       "0                     0                1                  0              0   \n",
       "1                     1                1                  0              0   \n",
       "2                     0                1                  0              0   \n",
       "3                     0                1                  0              0   \n",
       "4                     0                1                  0              0   \n",
       "\n",
       "   RoofStyle_Mansard  RoofStyle_Shed  \n",
       "0                  0               0  \n",
       "1                  0               0  \n",
       "2                  0               0  \n",
       "3                  0               0  \n",
       "4                  0               0  \n",
       "\n",
       "[5 rows x 112 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# drop the old columns as new ones are created\n",
    "\n",
    "house = house.drop(['MSZoning', 'Neighborhood', 'RoofStyle', 'Exterior1st', 'Exterior2nd', 'Foundation', \n",
    "                                'GarageType'], axis=1)\n",
    "house.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1358.000000</td>\n",
       "      <td>1358.000000</td>\n",
       "      <td>1358.000000</td>\n",
       "      <td>1358.000000</td>\n",
       "      <td>1358.000000</td>\n",
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       "      <td>1358.000000</td>\n",
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       "      <td>1358.000000</td>\n",
       "      <td>1358.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>57.157585</td>\n",
       "      <td>68.771723</td>\n",
       "      <td>9434.943299</td>\n",
       "      <td>6.023564</td>\n",
       "      <td>5.590574</td>\n",
       "      <td>83.878498</td>\n",
       "      <td>415.770250</td>\n",
       "      <td>557.894698</td>\n",
       "      <td>1017.749632</td>\n",
       "      <td>1127.438881</td>\n",
       "      <td>...</td>\n",
       "      <td>0.041973</td>\n",
       "      <td>0.058910</td>\n",
       "      <td>0.015464</td>\n",
       "      <td>0.023564</td>\n",
       "      <td>0.007364</td>\n",
       "      <td>0.804124</td>\n",
       "      <td>0.008100</td>\n",
       "      <td>0.175994</td>\n",
       "      <td>0.005155</td>\n",
       "      <td>0.001473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>42.291826</td>\n",
       "      <td>19.427951</td>\n",
       "      <td>3616.150840</td>\n",
       "      <td>1.319668</td>\n",
       "      <td>1.121361</td>\n",
       "      <td>134.061884</td>\n",
       "      <td>410.462751</td>\n",
       "      <td>429.213847</td>\n",
       "      <td>377.761875</td>\n",
       "      <td>340.208571</td>\n",
       "      <td>...</td>\n",
       "      <td>0.200602</td>\n",
       "      <td>0.235543</td>\n",
       "      <td>0.123434</td>\n",
       "      <td>0.151742</td>\n",
       "      <td>0.085527</td>\n",
       "      <td>0.397020</td>\n",
       "      <td>0.089669</td>\n",
       "      <td>0.380955</td>\n",
       "      <td>0.071637</td>\n",
       "      <td>0.038362</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.000000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>7416.250000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>218.250000</td>\n",
       "      <td>784.000000</td>\n",
       "      <td>865.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9246.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>364.500000</td>\n",
       "      <td>477.500000</td>\n",
       "      <td>970.000000</td>\n",
       "      <td>1065.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>70.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>11268.750000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>144.750000</td>\n",
       "      <td>686.000000</td>\n",
       "      <td>796.750000</td>\n",
       "      <td>1251.750000</td>\n",
       "      <td>1349.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>190.000000</td>\n",
       "      <td>182.000000</td>\n",
       "      <td>25095.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>640.000000</td>\n",
       "      <td>1646.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>2898.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 112 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        MSSubClass  LotFrontage       LotArea  OverallQual  OverallCond  \\\n",
       "count  1358.000000  1358.000000   1358.000000  1358.000000  1358.000000   \n",
       "mean     57.157585    68.771723   9434.943299     6.023564     5.590574   \n",
       "std      42.291826    19.427951   3616.150840     1.319668     1.121361   \n",
       "min      20.000000    21.000000   1300.000000     1.000000     1.000000   \n",
       "25%      20.000000    60.000000   7416.250000     5.000000     5.000000   \n",
       "50%      50.000000    69.000000   9246.500000     6.000000     5.000000   \n",
       "75%      70.000000    78.000000  11268.750000     7.000000     6.000000   \n",
       "max     190.000000   182.000000  25095.000000    10.000000     9.000000   \n",
       "\n",
       "        MasVnrArea   BsmtFinSF1    BsmtUnfSF  TotalBsmtSF     1stFlrSF  ...  \\\n",
       "count  1358.000000  1358.000000  1358.000000  1358.000000  1358.000000  ...   \n",
       "mean     83.878498   415.770250   557.894698  1017.749632  1127.438881  ...   \n",
       "std     134.061884   410.462751   429.213847   377.761875   340.208571  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000   334.000000  ...   \n",
       "25%       0.000000     0.000000   218.250000   784.000000   865.250000  ...   \n",
       "50%       0.000000   364.500000   477.500000   970.000000  1065.000000  ...   \n",
       "75%     144.750000   686.000000   796.750000  1251.750000  1349.750000  ...   \n",
       "max     640.000000  1646.000000  2002.000000  2006.000000  2898.000000  ...   \n",
       "\n",
       "       Neighborhood_SawyerW  Neighborhood_Somerst  Neighborhood_StoneBr  \\\n",
       "count           1358.000000           1358.000000           1358.000000   \n",
       "mean               0.041973              0.058910              0.015464   \n",
       "std                0.200602              0.235543              0.123434   \n",
       "min                0.000000              0.000000              0.000000   \n",
       "25%                0.000000              0.000000              0.000000   \n",
       "50%                0.000000              0.000000              0.000000   \n",
       "75%                0.000000              0.000000              0.000000   \n",
       "max                1.000000              1.000000              1.000000   \n",
       "\n",
       "       Neighborhood_Timber  Neighborhood_Veenker  RoofStyle_Gable  \\\n",
       "count          1358.000000           1358.000000      1358.000000   \n",
       "mean              0.023564              0.007364         0.804124   \n",
       "std               0.151742              0.085527         0.397020   \n",
       "min               0.000000              0.000000         0.000000   \n",
       "25%               0.000000              0.000000         1.000000   \n",
       "50%               0.000000              0.000000         1.000000   \n",
       "75%               0.000000              0.000000         1.000000   \n",
       "max               1.000000              1.000000         1.000000   \n",
       "\n",
       "       RoofStyle_Gambrel  RoofStyle_Hip  RoofStyle_Mansard  RoofStyle_Shed  \n",
       "count        1358.000000    1358.000000        1358.000000     1358.000000  \n",
       "mean            0.008100       0.175994           0.005155        0.001473  \n",
       "std             0.089669       0.380955           0.071637        0.038362  \n",
       "min             0.000000       0.000000           0.000000        0.000000  \n",
       "25%             0.000000       0.000000           0.000000        0.000000  \n",
       "50%             0.000000       0.000000           0.000000        0.000000  \n",
       "75%             0.000000       0.000000           0.000000        0.000000  \n",
       "max             1.000000       1.000000           1.000000        1.000000  \n",
       "\n",
       "[8 rows x 112 columns]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "house.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1358 entries, 0 to 1458\n",
      "Columns: 112 entries, MSSubClass to RoofStyle_Shed\n",
      "dtypes: float64(2), int64(37), uint8(73)\n",
      "memory usage: 521.2 KB\n"
     ]
    }
   ],
   "source": [
    "house.info()"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "All columns are numerical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Split the data into train set and test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assign predictor variables to X and response variable to y\n",
    "X = house.drop(['SalePrice'], axis=1)\n",
    "y = house['SalePrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "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>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>...</th>\n",
       "      <th>Neighborhood_SawyerW</th>\n",
       "      <th>Neighborhood_Somerst</th>\n",
       "      <th>Neighborhood_StoneBr</th>\n",
       "      <th>Neighborhood_Timber</th>\n",
       "      <th>Neighborhood_Veenker</th>\n",
       "      <th>RoofStyle_Gable</th>\n",
       "      <th>RoofStyle_Gambrel</th>\n",
       "      <th>RoofStyle_Hip</th>\n",
       "      <th>RoofStyle_Mansard</th>\n",
       "      <th>RoofStyle_Shed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>196.0</td>\n",
       "      <td>706</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>856</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>978</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>1262</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>162.0</td>\n",
       "      <td>486</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>920</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>216</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>961</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>350.0</td>\n",
       "      <td>655</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>1145</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 111 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  MasVnrArea  \\\n",
       "0          60         65.0     8450            7            5       196.0   \n",
       "1          20         80.0     9600            6            8         0.0   \n",
       "2          60         68.0    11250            7            5       162.0   \n",
       "3          70         60.0     9550            7            5         0.0   \n",
       "4          60         84.0    14260            8            5       350.0   \n",
       "\n",
       "   BsmtFinSF1  BsmtUnfSF  TotalBsmtSF  1stFlrSF  ...  Neighborhood_SawyerW  \\\n",
       "0         706        150          856       856  ...                     0   \n",
       "1         978        284         1262      1262  ...                     0   \n",
       "2         486        434          920       920  ...                     0   \n",
       "3         216        540          756       961  ...                     0   \n",
       "4         655        490         1145      1145  ...                     0   \n",
       "\n",
       "   Neighborhood_Somerst  Neighborhood_StoneBr  Neighborhood_Timber  \\\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",
       "   Neighborhood_Veenker  RoofStyle_Gable  RoofStyle_Gambrel  RoofStyle_Hip  \\\n",
       "0                     0                1                  0              0   \n",
       "1                     1                1                  0              0   \n",
       "2                     0                1                  0              0   \n",
       "3                     0                1                  0              0   \n",
       "4                     0                1                  0              0   \n",
       "\n",
       "   RoofStyle_Mansard  RoofStyle_Shed  \n",
       "0                  0               0  \n",
       "1                  0               0  \n",
       "2                  0               0  \n",
       "3                  0               0  \n",
       "4                  0               0  \n",
       "\n",
       "[5 rows x 111 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    208500\n",
       "1    181500\n",
       "2    223500\n",
       "3    140000\n",
       "4    250000\n",
       "Name: SalePrice, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Feature scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',\n",
       "       'MasVnrArea', 'BsmtFinSF1', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF',\n",
       "       ...\n",
       "       'Neighborhood_SawyerW', 'Neighborhood_Somerst', 'Neighborhood_StoneBr',\n",
       "       'Neighborhood_Timber', 'Neighborhood_Veenker', 'RoofStyle_Gable',\n",
       "       'RoofStyle_Gambrel', 'RoofStyle_Hip', 'RoofStyle_Mansard',\n",
       "       'RoofStyle_Shed'],\n",
       "      dtype='object', length=111)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "from sklearn.preprocessing import scale\n",
    "\n",
    "# storing column names in cols\n",
    "# scaling (the dataframe is converted to a numpy array)\n",
    "\n",
    "cols = X.columns\n",
    "X = pd.DataFrame(scale(X))\n",
    "X.columns = cols\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# once scaling is done we will split the data into train and test set\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, \n",
    "                                                    train_size=0.7,\n",
    "                                                    test_size = 0.3, random_state=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "lm = LinearRegression()\n",
    "lm.fit(X_train, y_train)\n",
    "\n",
    "# running RFE\n",
    "rfe = RFE(lm, 50)            \n",
    "rfe = rfe.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "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>Variable</th>\n",
       "      <th>rfe_support</th>\n",
       "      <th>rfe_ranking</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LotArea</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OverallCond</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MasVnrArea</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BsmtFinSF1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BsmtUnfSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TotalBsmtSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1stFlrSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2ndFlrSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GrLivArea</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BedroomAbvGr</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GarageCars</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WoodDeckSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OpenPorchSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Old_NewGarage</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Built_RemodelAge</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>d_ExterQual</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>d_BsmtExposure</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>d_KitchenQual</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>d_GarageFinish</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>d_BldgType</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>d_SaleCondition</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Exterior1st_CemntBd</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Exterior1st_MetalSd</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Exterior1st_VinylSd</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Exterior1st_Wd Sdng</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Exterior2nd_CmentBd</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Exterior2nd_HdBoard</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Exterior2nd_Plywood</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Exterior2nd_VinylSd</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Exterior2nd_Wd Shng</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Foundation_CBlock</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Foundation_PConc</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Foundation_Slab</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>GarageType_Attchd</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>GarageType_Basment</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>GarageType_BuiltIn</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>GarageType_Detchd</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>GarageType_None</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>MSZoning_FV</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>MSZoning_RL</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>MSZoning_RM</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Neighborhood_Crawfor</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Neighborhood_Edwards</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Neighborhood_Mitchel</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Neighborhood_NAmes</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Neighborhood_NoRidge</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Neighborhood_NridgHt</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Neighborhood_OldTown</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Neighborhood_StoneBr</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Variable  rfe_support  rfe_ranking\n",
       "0                LotArea         True            1\n",
       "1            OverallQual         True            1\n",
       "2            OverallCond         True            1\n",
       "3             MasVnrArea         True            1\n",
       "4             BsmtFinSF1         True            1\n",
       "5              BsmtUnfSF         True            1\n",
       "6            TotalBsmtSF         True            1\n",
       "7               1stFlrSF         True            1\n",
       "8               2ndFlrSF         True            1\n",
       "9              GrLivArea         True            1\n",
       "10          BedroomAbvGr         True            1\n",
       "11            GarageCars         True            1\n",
       "12            WoodDeckSF         True            1\n",
       "13           OpenPorchSF         True            1\n",
       "14         Old_NewGarage         True            1\n",
       "15      Built_RemodelAge         True            1\n",
       "16           d_ExterQual         True            1\n",
       "17        d_BsmtExposure         True            1\n",
       "18         d_KitchenQual         True            1\n",
       "19        d_GarageFinish         True            1\n",
       "20            d_BldgType         True            1\n",
       "21       d_SaleCondition         True            1\n",
       "22   Exterior1st_CemntBd         True            1\n",
       "23   Exterior1st_MetalSd         True            1\n",
       "24   Exterior1st_VinylSd         True            1\n",
       "25   Exterior1st_Wd Sdng         True            1\n",
       "26   Exterior2nd_CmentBd         True            1\n",
       "27   Exterior2nd_HdBoard         True            1\n",
       "28   Exterior2nd_Plywood         True            1\n",
       "29   Exterior2nd_VinylSd         True            1\n",
       "30   Exterior2nd_Wd Shng         True            1\n",
       "31     Foundation_CBlock         True            1\n",
       "32      Foundation_PConc         True            1\n",
       "33       Foundation_Slab         True            1\n",
       "34     GarageType_Attchd         True            1\n",
       "35    GarageType_Basment         True            1\n",
       "36    GarageType_BuiltIn         True            1\n",
       "37     GarageType_Detchd         True            1\n",
       "38       GarageType_None         True            1\n",
       "39           MSZoning_FV         True            1\n",
       "40           MSZoning_RL         True            1\n",
       "41           MSZoning_RM         True            1\n",
       "42  Neighborhood_Crawfor         True            1\n",
       "43  Neighborhood_Edwards         True            1\n",
       "44  Neighborhood_Mitchel         True            1\n",
       "45    Neighborhood_NAmes         True            1\n",
       "46  Neighborhood_NoRidge         True            1\n",
       "47  Neighborhood_NridgHt         True            1\n",
       "48  Neighborhood_OldTown         True            1\n",
       "49  Neighborhood_StoneBr         True            1"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "col = X_train.columns[rfe.support_]\n",
    "\n",
    "# assign the 50 features selected using RFE to a dataframe and view them\n",
    "\n",
    "temp_df = pd.DataFrame(list(zip(X_train.columns,rfe.support_,rfe.ranking_)), columns=['Variable', 'rfe_support', 'rfe_ranking'])\n",
    "temp_df = temp_df.loc[temp_df['rfe_support'] == True]\n",
    "temp_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_rfe = X_train[col]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = X_train_rfe[X_train_rfe.columns]\n",
    "X_test =  X_test[X_train.columns]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Ridge regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 26 candidates, totalling 130 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 130 out of 130 | elapsed:    1.8s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Ridge(),\n",
       "             param_grid={'alpha': [0.1, 0.5, 1.0, 5.0, 20, 40, 70, 100, 150,\n",
       "                                   200, 250, 300, 350, 400, 450, 500, 550, 600,\n",
       "                                   650, 700, 750, 800, 850, 900, 950, 1000]},\n",
       "             return_train_score=True, scoring='neg_mean_absolute_error',\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Alpha list to tune parameters\n",
    "list_alpha = [0.1, 0.5, 1.0, 5.0, 20, \n",
    " 40, 70, 100, 150, 200, 250, 300, 350, 400, 450, 500, \n",
    " 550, 600, 650, 700, 750, 800, 850, 900, 950, 1000]\n",
    "params = {'alpha': list_alpha}\n",
    "\n",
    "ridge = Ridge()\n",
    "\n",
    "# cross validation\n",
    "folds = 5\n",
    "model_cv = GridSearchCV(estimator = ridge, \n",
    "                        param_grid = params, \n",
    "                        scoring= 'neg_mean_absolute_error', \n",
    "                        cv = folds, \n",
    "                        return_train_score=True,\n",
    "                        verbose = 1)            \n",
    "model_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_alpha</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.010255</td>\n",
       "      <td>0.005593</td>\n",
       "      <td>0.005847</td>\n",
       "      <td>0.002378</td>\n",
       "      <td>0.1</td>\n",
       "      <td>{'alpha': 0.1}</td>\n",
       "      <td>-16414.145105</td>\n",
       "      <td>-13161.519636</td>\n",
       "      <td>-16386.855160</td>\n",
       "      <td>-13668.542914</td>\n",
       "      <td>...</td>\n",
       "      <td>-14850.174631</td>\n",
       "      <td>1349.686453</td>\n",
       "      <td>5</td>\n",
       "      <td>-13115.717477</td>\n",
       "      <td>-14240.568014</td>\n",
       "      <td>-13357.074487</td>\n",
       "      <td>-14033.180109</td>\n",
       "      <td>-13842.731953</td>\n",
       "      <td>-13717.854408</td>\n",
       "      <td>419.758323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.008212</td>\n",
       "      <td>0.001285</td>\n",
       "      <td>0.004553</td>\n",
       "      <td>0.000272</td>\n",
       "      <td>0.5</td>\n",
       "      <td>{'alpha': 0.5}</td>\n",
       "      <td>-16413.194931</td>\n",
       "      <td>-13157.758562</td>\n",
       "      <td>-16378.633729</td>\n",
       "      <td>-13660.172714</td>\n",
       "      <td>...</td>\n",
       "      <td>-14846.790369</td>\n",
       "      <td>1349.859793</td>\n",
       "      <td>4</td>\n",
       "      <td>-13119.728969</td>\n",
       "      <td>-14240.925026</td>\n",
       "      <td>-13355.532226</td>\n",
       "      <td>-14037.669666</td>\n",
       "      <td>-13842.696216</td>\n",
       "      <td>-13719.310421</td>\n",
       "      <td>419.640549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.012057</td>\n",
       "      <td>0.005528</td>\n",
       "      <td>0.005155</td>\n",
       "      <td>0.001037</td>\n",
       "      <td>1</td>\n",
       "      <td>{'alpha': 1.0}</td>\n",
       "      <td>-16412.428463</td>\n",
       "      <td>-13155.913092</td>\n",
       "      <td>-16368.727121</td>\n",
       "      <td>-13650.786539</td>\n",
       "      <td>...</td>\n",
       "      <td>-14843.662424</td>\n",
       "      <td>1349.352177</td>\n",
       "      <td>2</td>\n",
       "      <td>-13124.311674</td>\n",
       "      <td>-14241.702214</td>\n",
       "      <td>-13353.805353</td>\n",
       "      <td>-14043.079137</td>\n",
       "      <td>-13843.397517</td>\n",
       "      <td>-13721.259179</td>\n",
       "      <td>419.694043</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.004822</td>\n",
       "      <td>0.001045</td>\n",
       "      <td>0.003098</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>5</td>\n",
       "      <td>{'alpha': 5.0}</td>\n",
       "      <td>-16424.775688</td>\n",
       "      <td>-13181.605645</td>\n",
       "      <td>-16301.595120</td>\n",
       "      <td>-13590.869418</td>\n",
       "      <td>...</td>\n",
       "      <td>-14835.797342</td>\n",
       "      <td>1340.435747</td>\n",
       "      <td>1</td>\n",
       "      <td>-13154.815273</td>\n",
       "      <td>-14256.913089</td>\n",
       "      <td>-13353.481821</td>\n",
       "      <td>-14081.086778</td>\n",
       "      <td>-13867.685611</td>\n",
       "      <td>-13742.796514</td>\n",
       "      <td>422.289311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.005346</td>\n",
       "      <td>0.000640</td>\n",
       "      <td>0.003440</td>\n",
       "      <td>0.000605</td>\n",
       "      <td>20</td>\n",
       "      <td>{'alpha': 20}</td>\n",
       "      <td>-16491.748939</td>\n",
       "      <td>-13294.553910</td>\n",
       "      <td>-16192.228708</td>\n",
       "      <td>-13477.988994</td>\n",
       "      <td>...</td>\n",
       "      <td>-14844.361629</td>\n",
       "      <td>1327.084132</td>\n",
       "      <td>3</td>\n",
       "      <td>-13234.402558</td>\n",
       "      <td>-14309.245196</td>\n",
       "      <td>-13389.966298</td>\n",
       "      <td>-14157.310568</td>\n",
       "      <td>-13933.157800</td>\n",
       "      <td>-13804.816484</td>\n",
       "      <td>422.526833</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_alpha  \\\n",
       "0       0.010255      0.005593         0.005847        0.002378         0.1   \n",
       "1       0.008212      0.001285         0.004553        0.000272         0.5   \n",
       "2       0.012057      0.005528         0.005155        0.001037           1   \n",
       "3       0.004822      0.001045         0.003098        0.000640           5   \n",
       "4       0.005346      0.000640         0.003440        0.000605          20   \n",
       "\n",
       "           params  split0_test_score  split1_test_score  split2_test_score  \\\n",
       "0  {'alpha': 0.1}      -16414.145105      -13161.519636      -16386.855160   \n",
       "1  {'alpha': 0.5}      -16413.194931      -13157.758562      -16378.633729   \n",
       "2  {'alpha': 1.0}      -16412.428463      -13155.913092      -16368.727121   \n",
       "3  {'alpha': 5.0}      -16424.775688      -13181.605645      -16301.595120   \n",
       "4   {'alpha': 20}      -16491.748939      -13294.553910      -16192.228708   \n",
       "\n",
       "   split3_test_score  ...  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0      -13668.542914  ...    -14850.174631     1349.686453                5   \n",
       "1      -13660.172714  ...    -14846.790369     1349.859793                4   \n",
       "2      -13650.786539  ...    -14843.662424     1349.352177                2   \n",
       "3      -13590.869418  ...    -14835.797342     1340.435747                1   \n",
       "4      -13477.988994  ...    -14844.361629     1327.084132                3   \n",
       "\n",
       "   split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0       -13115.717477       -14240.568014       -13357.074487   \n",
       "1       -13119.728969       -14240.925026       -13355.532226   \n",
       "2       -13124.311674       -14241.702214       -13353.805353   \n",
       "3       -13154.815273       -14256.913089       -13353.481821   \n",
       "4       -13234.402558       -14309.245196       -13389.966298   \n",
       "\n",
       "   split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "0       -14033.180109       -13842.731953     -13717.854408       419.758323  \n",
       "1       -14037.669666       -13842.696216     -13719.310421       419.640549  \n",
       "2       -14043.079137       -13843.397517     -13721.259179       419.694043  \n",
       "3       -14081.086778       -13867.685611     -13742.796514       422.289311  \n",
       "4       -14157.310568       -13933.157800     -13804.816484       422.526833  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results = pd.DataFrame(model_cv.cv_results_)\n",
    "cv_results = cv_results[cv_results['param_alpha'] <= 1000]\n",
    "cv_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting mean test and train scoes with alpha \n",
    "cv_results['param_alpha'] = cv_results['param_alpha'].astype('int32')\n",
    "\n",
    "# plotting\n",
    "plt.plot(cv_results['param_alpha'], cv_results['mean_train_score'])\n",
    "plt.plot(cv_results['param_alpha'], cv_results['mean_test_score'])\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('Negative Mean Absolute Error')\n",
    "plt.title(\"Negative Mean Absolute Error and alpha\")\n",
    "plt.legend(['train score', 'test score'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 5.0}"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Best parameter (alpha)\n",
    "model_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4048.98617905, 12679.99816138,  4141.55313685,  2504.87478402,\n",
       "        4647.01212171, -2321.15326579,  9729.2044071 ,  6981.73581297,\n",
       "        9361.14323382,  9065.74559752, -2018.17239874,  7720.03904748,\n",
       "        2227.52565979,  2464.29149263,  2683.74698301, -1807.11234895,\n",
       "        3984.53038408,  3611.82535315,  4808.74794615,  2817.96699918,\n",
       "       -7180.28409975,  2030.99390454, -9490.0567197 , -2861.99082655,\n",
       "       -7992.03297748, -4159.28070036, 10242.70114916, -3798.0708471 ,\n",
       "       -3197.64082305,  3204.43889604, -2670.40428522,  3397.95072428,\n",
       "        5115.92514997,  3846.26637319,  3950.58693147,  1612.51411689,\n",
       "        3677.63631413,  3570.43034984,  5467.25435894,  4942.15367797,\n",
       "        4037.71833529,  3253.14851749,  4282.67186066, -1870.18684442,\n",
       "       -1273.24994955, -1198.98023452,  3837.21259263,  9131.56336564,\n",
       "       -3932.73486002,  3910.86108545])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha = 5.0\n",
    "ridge = Ridge(alpha=alpha)\n",
    "\n",
    "ridge.fit(X_train, y_train)\n",
    "ridge.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 5.0\n",
    "ridge = Ridge(alpha=alpha)\n",
    "\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "pred = ridge.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "686979474.1272863"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = np.mean((pred - y_test)**2)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.867626975233855"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ridge.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4048.98617905, 12679.99816138,  4141.55313685,  2504.87478402,\n",
       "        4647.01212171, -2321.15326579,  9729.2044071 ,  6981.73581297,\n",
       "        9361.14323382,  9065.74559752, -2018.17239874,  7720.03904748,\n",
       "        2227.52565979,  2464.29149263,  2683.74698301, -1807.11234895,\n",
       "        3984.53038408,  3611.82535315,  4808.74794615,  2817.96699918,\n",
       "       -7180.28409975,  2030.99390454, -9490.0567197 , -2861.99082655,\n",
       "       -7992.03297748, -4159.28070036, 10242.70114916, -3798.0708471 ,\n",
       "       -3197.64082305,  3204.43889604, -2670.40428522,  3397.95072428,\n",
       "        5115.92514997,  3846.26637319,  3950.58693147,  1612.51411689,\n",
       "        3677.63631413,  3570.43034984,  5467.25435894,  4942.15367797,\n",
       "        4037.71833529,  3253.14851749,  4282.67186066, -1870.18684442,\n",
       "       -1273.24994955, -1198.98023452,  3837.21259263,  9131.56336564,\n",
       "       -3932.73486002,  3910.86108545])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alpha = 5.0\n",
    "ridge = Ridge(alpha=alpha)\n",
    "\n",
    "ridge.fit(X_train, y_train)\n",
    "ridge.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "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>Features</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LotArea</td>\n",
       "      <td>4069.6134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>12667.5886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OverallCond</td>\n",
       "      <td>4077.0220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MasVnrArea</td>\n",
       "      <td>2527.3370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BsmtFinSF1</td>\n",
       "      <td>4873.3092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BsmtUnfSF</td>\n",
       "      <td>-2039.1186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TotalBsmtSF</td>\n",
       "      <td>9315.2381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1stFlrSF</td>\n",
       "      <td>6516.9004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2ndFlrSF</td>\n",
       "      <td>8565.5468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GrLivArea</td>\n",
       "      <td>9755.9585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BedroomAbvGr</td>\n",
       "      <td>-1926.4977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GarageCars</td>\n",
       "      <td>7619.7582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WoodDeckSF</td>\n",
       "      <td>2219.2974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OpenPorchSF</td>\n",
       "      <td>2496.1353</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Old_NewGarage</td>\n",
       "      <td>2656.5311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Built_RemodelAge</td>\n",
       "      <td>-1830.9954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>d_ExterQual</td>\n",
       "      <td>4082.0583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>d_BsmtExposure</td>\n",
       "      <td>3668.8142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>d_KitchenQual</td>\n",
       "      <td>4854.7114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>d_GarageFinish</td>\n",
       "      <td>2825.1758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>d_BldgType</td>\n",
       "      <td>-7098.7186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>d_SaleCondition</td>\n",
       "      <td>2071.6881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Exterior1st_CemntBd</td>\n",
       "      <td>-7289.3136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Exterior1st_MetalSd</td>\n",
       "      <td>-2750.8402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Exterior1st_VinylSd</td>\n",
       "      <td>-6957.7843</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Exterior1st_Wd Sdng</td>\n",
       "      <td>-4056.5805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Exterior2nd_CmentBd</td>\n",
       "      <td>8155.9924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Exterior2nd_HdBoard</td>\n",
       "      <td>-3661.7190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Exterior2nd_Plywood</td>\n",
       "      <td>-3103.9847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Exterior2nd_VinylSd</td>\n",
       "      <td>2342.7357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Exterior2nd_Wd Shng</td>\n",
       "      <td>-2648.8536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Foundation_CBlock</td>\n",
       "      <td>3172.1689</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Foundation_PConc</td>\n",
       "      <td>4893.0663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Foundation_Slab</td>\n",
       "      <td>3682.2271</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>GarageType_Attchd</td>\n",
       "      <td>2822.9111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>GarageType_Basment</td>\n",
       "      <td>1293.7299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>GarageType_BuiltIn</td>\n",
       "      <td>3143.3504</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>GarageType_Detchd</td>\n",
       "      <td>2506.0782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>GarageType_None</td>\n",
       "      <td>4790.4031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>MSZoning_FV</td>\n",
       "      <td>4709.4437</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>MSZoning_RL</td>\n",
       "      <td>3686.8860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>MSZoning_RM</td>\n",
       "      <td>2842.9902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Neighborhood_Crawfor</td>\n",
       "      <td>4233.4413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Neighborhood_Edwards</td>\n",
       "      <td>-1834.4356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Neighborhood_Mitchel</td>\n",
       "      <td>-1275.7120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Neighborhood_NAmes</td>\n",
       "      <td>-1193.0285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Neighborhood_NoRidge</td>\n",
       "      <td>3830.7495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Neighborhood_NridgHt</td>\n",
       "      <td>9036.3781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Neighborhood_OldTown</td>\n",
       "      <td>-3861.8164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Neighborhood_StoneBr</td>\n",
       "      <td>3858.1324</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Features  Coefficient\n",
       "0                LotArea    4069.6134\n",
       "1            OverallQual   12667.5886\n",
       "2            OverallCond    4077.0220\n",
       "3             MasVnrArea    2527.3370\n",
       "4             BsmtFinSF1    4873.3092\n",
       "5              BsmtUnfSF   -2039.1186\n",
       "6            TotalBsmtSF    9315.2381\n",
       "7               1stFlrSF    6516.9004\n",
       "8               2ndFlrSF    8565.5468\n",
       "9              GrLivArea    9755.9585\n",
       "10          BedroomAbvGr   -1926.4977\n",
       "11            GarageCars    7619.7582\n",
       "12            WoodDeckSF    2219.2974\n",
       "13           OpenPorchSF    2496.1353\n",
       "14         Old_NewGarage    2656.5311\n",
       "15      Built_RemodelAge   -1830.9954\n",
       "16           d_ExterQual    4082.0583\n",
       "17        d_BsmtExposure    3668.8142\n",
       "18         d_KitchenQual    4854.7114\n",
       "19        d_GarageFinish    2825.1758\n",
       "20            d_BldgType   -7098.7186\n",
       "21       d_SaleCondition    2071.6881\n",
       "22   Exterior1st_CemntBd   -7289.3136\n",
       "23   Exterior1st_MetalSd   -2750.8402\n",
       "24   Exterior1st_VinylSd   -6957.7843\n",
       "25   Exterior1st_Wd Sdng   -4056.5805\n",
       "26   Exterior2nd_CmentBd    8155.9924\n",
       "27   Exterior2nd_HdBoard   -3661.7190\n",
       "28   Exterior2nd_Plywood   -3103.9847\n",
       "29   Exterior2nd_VinylSd    2342.7357\n",
       "30   Exterior2nd_Wd Shng   -2648.8536\n",
       "31     Foundation_CBlock    3172.1689\n",
       "32      Foundation_PConc    4893.0663\n",
       "33       Foundation_Slab    3682.2271\n",
       "34     GarageType_Attchd    2822.9111\n",
       "35    GarageType_Basment    1293.7299\n",
       "36    GarageType_BuiltIn    3143.3504\n",
       "37     GarageType_Detchd    2506.0782\n",
       "38       GarageType_None    4790.4031\n",
       "39           MSZoning_FV    4709.4437\n",
       "40           MSZoning_RL    3686.8860\n",
       "41           MSZoning_RM    2842.9902\n",
       "42  Neighborhood_Crawfor    4233.4413\n",
       "43  Neighborhood_Edwards   -1834.4356\n",
       "44  Neighborhood_Mitchel   -1275.7120\n",
       "45    Neighborhood_NAmes   -1193.0285\n",
       "46  Neighborhood_NoRidge    3830.7495\n",
       "47  Neighborhood_NridgHt    9036.3781\n",
       "48  Neighborhood_OldTown   -3861.8164\n",
       "49  Neighborhood_StoneBr    3858.1324"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# features and coefficients in dataframe\n",
    "\n",
    "ridge_df = pd.DataFrame({'Features':X_train.columns, 'Coefficient':ridge.coef_.round(4)})\n",
    "ridge_df.reset_index(drop=True, inplace=True)\n",
    "ridge_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LotArea': 4069.6134,\n",
       " 'OverallQual': 12667.5886,\n",
       " 'OverallCond': 4077.022,\n",
       " 'MasVnrArea': 2527.337,\n",
       " 'BsmtFinSF1': 4873.3092,\n",
       " 'BsmtUnfSF': -2039.1186,\n",
       " 'TotalBsmtSF': 9315.2381,\n",
       " '1stFlrSF': 6516.9004,\n",
       " '2ndFlrSF': 8565.5468,\n",
       " 'GrLivArea': 9755.9585,\n",
       " 'BedroomAbvGr': -1926.4977,\n",
       " 'GarageCars': 7619.7582,\n",
       " 'WoodDeckSF': 2219.2974,\n",
       " 'OpenPorchSF': 2496.1353,\n",
       " 'Old_NewGarage': 2656.5311,\n",
       " 'Built_RemodelAge': -1830.9954,\n",
       " 'd_ExterQual': 4082.0583,\n",
       " 'd_BsmtExposure': 3668.8142,\n",
       " 'd_KitchenQual': 4854.7114,\n",
       " 'd_GarageFinish': 2825.1758,\n",
       " 'd_BldgType': -7098.7186,\n",
       " 'd_SaleCondition': 2071.6881,\n",
       " 'Exterior1st_CemntBd': -7289.3136,\n",
       " 'Exterior1st_MetalSd': -2750.8402,\n",
       " 'Exterior1st_VinylSd': -6957.7843,\n",
       " 'Exterior1st_Wd Sdng': -4056.5805,\n",
       " 'Exterior2nd_CmentBd': 8155.9924,\n",
       " 'Exterior2nd_HdBoard': -3661.719,\n",
       " 'Exterior2nd_Plywood': -3103.9847,\n",
       " 'Exterior2nd_VinylSd': 2342.7357,\n",
       " 'Exterior2nd_Wd Shng': -2648.8536,\n",
       " 'Foundation_CBlock': 3172.1689,\n",
       " 'Foundation_PConc': 4893.0663,\n",
       " 'Foundation_Slab': 3682.2271,\n",
       " 'GarageType_Attchd': 2822.9111,\n",
       " 'GarageType_Basment': 1293.7299,\n",
       " 'GarageType_BuiltIn': 3143.3504,\n",
       " 'GarageType_Detchd': 2506.0782,\n",
       " 'GarageType_None': 4790.4031,\n",
       " 'MSZoning_FV': 4709.4437,\n",
       " 'MSZoning_RL': 3686.886,\n",
       " 'MSZoning_RM': 2842.9902,\n",
       " 'Neighborhood_Crawfor': 4233.4413,\n",
       " 'Neighborhood_Edwards': -1834.4356,\n",
       " 'Neighborhood_Mitchel': -1275.712,\n",
       " 'Neighborhood_NAmes': -1193.0285,\n",
       " 'Neighborhood_NoRidge': 3830.7495,\n",
       " 'Neighborhood_NridgHt': 9036.3781,\n",
       " 'Neighborhood_OldTown': -3861.8164,\n",
       " 'Neighborhood_StoneBr': 3858.1324}"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Features and Cf assigned to a dictionary \n",
    "\n",
    "ridge_coeff_dict = dict(pd.Series(ridge.coef_.round(4), index = X_train.columns))\n",
    "ridge_coeff_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features and Cfs assigned to a dictionary \n",
    "\n",
    "ridge_coeff_dict = dict(pd.Series(ridge.coef_.round(4), index = X_train.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# RFE to minimize feature to 15\n",
    "X_train_ridge = X_train[ridge_df.Features]\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train_ridge, y_train)\n",
    "\n",
    "# RFE\n",
    "rfe = RFE(lm, 15)            \n",
    "rfe = rfe.fit(X_train_ridge, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "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>Features</th>\n",
       "      <th>rfe_support</th>\n",
       "      <th>rfe_ranking</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>12667.5886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GrLivArea</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>9755.9585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TotalBsmtSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>9315.2381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Neighborhood_NridgHt</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>9036.3781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2ndFlrSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>8565.5468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Exterior2nd_CmentBd</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>8155.9924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GarageCars</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>7619.7582</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1stFlrSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>6516.9004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BsmtFinSF1</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>4873.3092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>d_KitchenQual</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>4854.7114</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Features  rfe_support  rfe_ranking  Coefficient\n",
       "0            OverallQual         True            1   12667.5886\n",
       "5              GrLivArea         True            1    9755.9585\n",
       "2            TotalBsmtSF         True            1    9315.2381\n",
       "14  Neighborhood_NridgHt         True            1    9036.3781\n",
       "4               2ndFlrSF         True            1    8565.5468\n",
       "12   Exterior2nd_CmentBd         True            1    8155.9924\n",
       "6             GarageCars         True            1    7619.7582\n",
       "3               1stFlrSF         True            1    6516.9004\n",
       "1             BsmtFinSF1         True            1    4873.3092\n",
       "8          d_KitchenQual         True            1    4854.7114"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Deriving coefficient values\n",
    "\n",
    "def find(x):\n",
    "    return ridge_coeff_dict[x]\n",
    "\n",
    "# top 10 features to a temp dataframe \n",
    "\n",
    "temp1_df = pd.DataFrame(list(zip( X_train_ridge.columns, rfe.support_, rfe.ranking_)), columns=['Features', 'rfe_support', 'rfe_ranking'])\n",
    "temp1_df = temp1_df.loc[temp1_df['rfe_support'] == True]\n",
    "temp1_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "temp1_df['Coefficient'] = temp1_df['Features'].apply(find)\n",
    "temp1_df = temp1_df.sort_values(by=['Coefficient'], ascending=False)\n",
    "temp1_df = temp1_df.head(10)\n",
    "temp1_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot to determine the parameters affecting the sale price\n",
    "\n",
    "plt.figure(figsize=(20,20))\n",
    "plt.subplot(4,3,1)\n",
    "sns.barplot(y = 'Features', x='Coefficient', palette='Set1', data = temp1_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 6. Lasso"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 26 candidates, totalling 130 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.\n",
      "[Parallel(n_jobs=1)]: Done 130 out of 130 | elapsed:    2.1s finished\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=Lasso(),\n",
       "             param_grid={'alpha': [0.1, 0.5, 1.0, 5.0, 20, 40, 70, 100, 150,\n",
       "                                   200, 250, 300, 350, 400, 450, 500, 550, 600,\n",
       "                                   650, 700, 750, 800, 850, 900, 950, 1000]},\n",
       "             return_train_score=True, scoring='neg_mean_absolute_error',\n",
       "             verbose=1)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "lasso = Lasso()\n",
    "# Cross validation\n",
    "model_cv = GridSearchCV(estimator = lasso, \n",
    "                        param_grid = params, \n",
    "                        scoring= 'neg_mean_absolute_error', \n",
    "                        cv = folds, \n",
    "                        return_train_score=True,\n",
    "                        verbose = 1)            \n",
    "\n",
    "model_cv.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "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>mean_fit_time</th>\n",
       "      <th>std_fit_time</th>\n",
       "      <th>mean_score_time</th>\n",
       "      <th>std_score_time</th>\n",
       "      <th>param_alpha</th>\n",
       "      <th>params</th>\n",
       "      <th>split0_test_score</th>\n",
       "      <th>split1_test_score</th>\n",
       "      <th>split2_test_score</th>\n",
       "      <th>split3_test_score</th>\n",
       "      <th>...</th>\n",
       "      <th>mean_test_score</th>\n",
       "      <th>std_test_score</th>\n",
       "      <th>rank_test_score</th>\n",
       "      <th>split0_train_score</th>\n",
       "      <th>split1_train_score</th>\n",
       "      <th>split2_train_score</th>\n",
       "      <th>split3_train_score</th>\n",
       "      <th>split4_train_score</th>\n",
       "      <th>mean_train_score</th>\n",
       "      <th>std_train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.026740</td>\n",
       "      <td>0.005492</td>\n",
       "      <td>0.002621</td>\n",
       "      <td>0.000162</td>\n",
       "      <td>0.1</td>\n",
       "      <td>{'alpha': 0.1}</td>\n",
       "      <td>-16414.473421</td>\n",
       "      <td>-13163.100442</td>\n",
       "      <td>-16439.597396</td>\n",
       "      <td>-13670.507682</td>\n",
       "      <td>...</td>\n",
       "      <td>-14861.346505</td>\n",
       "      <td>1361.170213</td>\n",
       "      <td>5</td>\n",
       "      <td>-13114.720330</td>\n",
       "      <td>-14240.509479</td>\n",
       "      <td>-13357.466840</td>\n",
       "      <td>-14032.235404</td>\n",
       "      <td>-13842.843131</td>\n",
       "      <td>-13717.555037</td>\n",
       "      <td>419.827436</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.021539</td>\n",
       "      <td>0.006274</td>\n",
       "      <td>0.002784</td>\n",
       "      <td>0.000452</td>\n",
       "      <td>0.5</td>\n",
       "      <td>{'alpha': 0.5}</td>\n",
       "      <td>-16414.866494</td>\n",
       "      <td>-13163.918320</td>\n",
       "      <td>-16428.004448</td>\n",
       "      <td>-13669.409919</td>\n",
       "      <td>...</td>\n",
       "      <td>-14859.248157</td>\n",
       "      <td>1358.530292</td>\n",
       "      <td>4</td>\n",
       "      <td>-13114.659427</td>\n",
       "      <td>-14240.389400</td>\n",
       "      <td>-13357.407095</td>\n",
       "      <td>-14032.789336</td>\n",
       "      <td>-13842.806310</td>\n",
       "      <td>-13717.610314</td>\n",
       "      <td>419.906170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.020262</td>\n",
       "      <td>0.006282</td>\n",
       "      <td>0.002687</td>\n",
       "      <td>0.000327</td>\n",
       "      <td>1</td>\n",
       "      <td>{'alpha': 1.0}</td>\n",
       "      <td>-16415.401332</td>\n",
       "      <td>-13164.934753</td>\n",
       "      <td>-16385.997382</td>\n",
       "      <td>-13668.059682</td>\n",
       "      <td>...</td>\n",
       "      <td>-14851.132877</td>\n",
       "      <td>1348.963122</td>\n",
       "      <td>2</td>\n",
       "      <td>-13114.706275</td>\n",
       "      <td>-14240.237912</td>\n",
       "      <td>-13357.010177</td>\n",
       "      <td>-14033.318725</td>\n",
       "      <td>-13842.757768</td>\n",
       "      <td>-13717.606171</td>\n",
       "      <td>419.999769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.014905</td>\n",
       "      <td>0.001523</td>\n",
       "      <td>0.002466</td>\n",
       "      <td>0.000096</td>\n",
       "      <td>5</td>\n",
       "      <td>{'alpha': 5.0}</td>\n",
       "      <td>-16419.972593</td>\n",
       "      <td>-13173.083121</td>\n",
       "      <td>-16372.146735</td>\n",
       "      <td>-13657.792976</td>\n",
       "      <td>...</td>\n",
       "      <td>-14850.820600</td>\n",
       "      <td>1346.333017</td>\n",
       "      <td>1</td>\n",
       "      <td>-13115.830801</td>\n",
       "      <td>-14239.029979</td>\n",
       "      <td>-13355.485077</td>\n",
       "      <td>-14036.917159</td>\n",
       "      <td>-13842.370578</td>\n",
       "      <td>-13717.926719</td>\n",
       "      <td>420.160260</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.013743</td>\n",
       "      <td>0.003185</td>\n",
       "      <td>0.002713</td>\n",
       "      <td>0.000360</td>\n",
       "      <td>20</td>\n",
       "      <td>{'alpha': 20}</td>\n",
       "      <td>-16445.170098</td>\n",
       "      <td>-13208.086845</td>\n",
       "      <td>-16321.331734</td>\n",
       "      <td>-13624.522781</td>\n",
       "      <td>...</td>\n",
       "      <td>-14853.689196</td>\n",
       "      <td>1337.133494</td>\n",
       "      <td>3</td>\n",
       "      <td>-13121.505712</td>\n",
       "      <td>-14239.189043</td>\n",
       "      <td>-13353.989826</td>\n",
       "      <td>-14052.526487</td>\n",
       "      <td>-13847.010841</td>\n",
       "      <td>-13722.844382</td>\n",
       "      <td>421.516616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   mean_fit_time  std_fit_time  mean_score_time  std_score_time param_alpha  \\\n",
       "0       0.026740      0.005492         0.002621        0.000162         0.1   \n",
       "1       0.021539      0.006274         0.002784        0.000452         0.5   \n",
       "2       0.020262      0.006282         0.002687        0.000327           1   \n",
       "3       0.014905      0.001523         0.002466        0.000096           5   \n",
       "4       0.013743      0.003185         0.002713        0.000360          20   \n",
       "\n",
       "           params  split0_test_score  split1_test_score  split2_test_score  \\\n",
       "0  {'alpha': 0.1}      -16414.473421      -13163.100442      -16439.597396   \n",
       "1  {'alpha': 0.5}      -16414.866494      -13163.918320      -16428.004448   \n",
       "2  {'alpha': 1.0}      -16415.401332      -13164.934753      -16385.997382   \n",
       "3  {'alpha': 5.0}      -16419.972593      -13173.083121      -16372.146735   \n",
       "4   {'alpha': 20}      -16445.170098      -13208.086845      -16321.331734   \n",
       "\n",
       "   split3_test_score  ...  mean_test_score  std_test_score  rank_test_score  \\\n",
       "0      -13670.507682  ...    -14861.346505     1361.170213                5   \n",
       "1      -13669.409919  ...    -14859.248157     1358.530292                4   \n",
       "2      -13668.059682  ...    -14851.132877     1348.963122                2   \n",
       "3      -13657.792976  ...    -14850.820600     1346.333017                1   \n",
       "4      -13624.522781  ...    -14853.689196     1337.133494                3   \n",
       "\n",
       "   split0_train_score  split1_train_score  split2_train_score  \\\n",
       "0       -13114.720330       -14240.509479       -13357.466840   \n",
       "1       -13114.659427       -14240.389400       -13357.407095   \n",
       "2       -13114.706275       -14240.237912       -13357.010177   \n",
       "3       -13115.830801       -14239.029979       -13355.485077   \n",
       "4       -13121.505712       -14239.189043       -13353.989826   \n",
       "\n",
       "   split3_train_score  split4_train_score  mean_train_score  std_train_score  \n",
       "0       -14032.235404       -13842.843131     -13717.555037       419.827436  \n",
       "1       -14032.789336       -13842.806310     -13717.610314       419.906170  \n",
       "2       -14033.318725       -13842.757768     -13717.606171       419.999769  \n",
       "3       -14036.917159       -13842.370578     -13717.926719       420.160260  \n",
       "4       -14052.526487       -13847.010841     -13722.844382       421.516616  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cv_results = pd.DataFrame(model_cv.cv_results_)\n",
    "cv_results.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# plotting mean test and train scoes with alpha \n",
    "cv_results['param_alpha'] = cv_results['param_alpha'].astype('float32')\n",
    "\n",
    "# plotting\n",
    "plt.plot(cv_results['param_alpha'], cv_results['mean_train_score'])\n",
    "plt.plot(cv_results['param_alpha'], cv_results['mean_test_score'])\n",
    "plt.xlabel('alpha')\n",
    "plt.ylabel('Negative Mean Absolute Error')\n",
    "\n",
    "plt.title(\"Negative Mean Absolute Error and alpha\")\n",
    "plt.legend(['train score', 'test score'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'alpha': 5.0}"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model_cv.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lasso(alpha=5.0)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#It is confirmed by graph as well\n",
    "\n",
    "alpha = 5.0\n",
    "\n",
    "lasso = Lasso(alpha=alpha)\n",
    "        \n",
    "lasso.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "alpha = 5.0\n",
    "lasso = Lasso(alpha=alpha)\n",
    "\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "pred = lasso.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "692759479.2427036"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse = np.mean((pred - y_test)**2)\n",
    "mse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8665132348833684"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso.score(X_test,y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>Features</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LotArea</td>\n",
       "      <td>4010.3586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>12695.8315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>OverallCond</td>\n",
       "      <td>4191.9237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>MasVnrArea</td>\n",
       "      <td>2478.2254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BsmtFinSF1</td>\n",
       "      <td>4392.7196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>BsmtUnfSF</td>\n",
       "      <td>-2646.7598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>TotalBsmtSF</td>\n",
       "      <td>10166.2557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1stFlrSF</td>\n",
       "      <td>8733.7902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2ndFlrSF</td>\n",
       "      <td>11724.1445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GrLivArea</td>\n",
       "      <td>6648.7989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>BedroomAbvGr</td>\n",
       "      <td>-2090.4483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>GarageCars</td>\n",
       "      <td>7804.3737</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>WoodDeckSF</td>\n",
       "      <td>2224.8119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>OpenPorchSF</td>\n",
       "      <td>2416.9714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Old_NewGarage</td>\n",
       "      <td>2703.4029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Built_RemodelAge</td>\n",
       "      <td>-1790.9500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>d_ExterQual</td>\n",
       "      <td>3858.5267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>d_BsmtExposure</td>\n",
       "      <td>3528.5828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>d_KitchenQual</td>\n",
       "      <td>4768.9520</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>d_GarageFinish</td>\n",
       "      <td>2798.8167</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>d_BldgType</td>\n",
       "      <td>-7238.4067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>d_SaleCondition</td>\n",
       "      <td>1989.7870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Exterior1st_CemntBd</td>\n",
       "      <td>-12628.9655</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Exterior1st_MetalSd</td>\n",
       "      <td>-2886.7254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Exterior1st_VinylSd</td>\n",
       "      <td>-8978.2159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>Exterior1st_Wd Sdng</td>\n",
       "      <td>-4182.6553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Exterior2nd_CmentBd</td>\n",
       "      <td>13307.2479</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Exterior2nd_HdBoard</td>\n",
       "      <td>-3839.9604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Exterior2nd_Plywood</td>\n",
       "      <td>-3211.6768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Exterior2nd_VinylSd</td>\n",
       "      <td>4149.2763</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Exterior2nd_Wd Shng</td>\n",
       "      <td>-2665.2427</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Foundation_CBlock</td>\n",
       "      <td>3518.7937</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Foundation_PConc</td>\n",
       "      <td>5254.7890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Foundation_Slab</td>\n",
       "      <td>3967.0265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>GarageType_Attchd</td>\n",
       "      <td>5066.5113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>GarageType_Basment</td>\n",
       "      <td>1909.0092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>GarageType_BuiltIn</td>\n",
       "      <td>4208.3810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>GarageType_Detchd</td>\n",
       "      <td>4608.6707</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>GarageType_None</td>\n",
       "      <td>6131.5409</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>MSZoning_FV</td>\n",
       "      <td>5067.8591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>MSZoning_RL</td>\n",
       "      <td>4177.3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>MSZoning_RM</td>\n",
       "      <td>3456.1406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>Neighborhood_Crawfor</td>\n",
       "      <td>4321.9812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>Neighborhood_Edwards</td>\n",
       "      <td>-1892.5930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>Neighborhood_Mitchel</td>\n",
       "      <td>-1261.2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>Neighborhood_NAmes</td>\n",
       "      <td>-1185.3788</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>Neighborhood_NoRidge</td>\n",
       "      <td>3833.3908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>Neighborhood_NridgHt</td>\n",
       "      <td>9225.3317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>Neighborhood_OldTown</td>\n",
       "      <td>-3983.5464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>Neighborhood_StoneBr</td>\n",
       "      <td>3955.1904</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Features  Coefficient\n",
       "0                LotArea    4010.3586\n",
       "1            OverallQual   12695.8315\n",
       "2            OverallCond    4191.9237\n",
       "3             MasVnrArea    2478.2254\n",
       "4             BsmtFinSF1    4392.7196\n",
       "5              BsmtUnfSF   -2646.7598\n",
       "6            TotalBsmtSF   10166.2557\n",
       "7               1stFlrSF    8733.7902\n",
       "8               2ndFlrSF   11724.1445\n",
       "9              GrLivArea    6648.7989\n",
       "10          BedroomAbvGr   -2090.4483\n",
       "11            GarageCars    7804.3737\n",
       "12            WoodDeckSF    2224.8119\n",
       "13           OpenPorchSF    2416.9714\n",
       "14         Old_NewGarage    2703.4029\n",
       "15      Built_RemodelAge   -1790.9500\n",
       "16           d_ExterQual    3858.5267\n",
       "17        d_BsmtExposure    3528.5828\n",
       "18         d_KitchenQual    4768.9520\n",
       "19        d_GarageFinish    2798.8167\n",
       "20            d_BldgType   -7238.4067\n",
       "21       d_SaleCondition    1989.7870\n",
       "22   Exterior1st_CemntBd  -12628.9655\n",
       "23   Exterior1st_MetalSd   -2886.7254\n",
       "24   Exterior1st_VinylSd   -8978.2159\n",
       "25   Exterior1st_Wd Sdng   -4182.6553\n",
       "26   Exterior2nd_CmentBd   13307.2479\n",
       "27   Exterior2nd_HdBoard   -3839.9604\n",
       "28   Exterior2nd_Plywood   -3211.6768\n",
       "29   Exterior2nd_VinylSd    4149.2763\n",
       "30   Exterior2nd_Wd Shng   -2665.2427\n",
       "31     Foundation_CBlock    3518.7937\n",
       "32      Foundation_PConc    5254.7890\n",
       "33       Foundation_Slab    3967.0265\n",
       "34     GarageType_Attchd    5066.5113\n",
       "35    GarageType_Basment    1909.0092\n",
       "36    GarageType_BuiltIn    4208.3810\n",
       "37     GarageType_Detchd    4608.6707\n",
       "38       GarageType_None    6131.5409\n",
       "39           MSZoning_FV    5067.8591\n",
       "40           MSZoning_RL    4177.3400\n",
       "41           MSZoning_RM    3456.1406\n",
       "42  Neighborhood_Crawfor    4321.9812\n",
       "43  Neighborhood_Edwards   -1892.5930\n",
       "44  Neighborhood_Mitchel   -1261.2018\n",
       "45    Neighborhood_NAmes   -1185.3788\n",
       "46  Neighborhood_NoRidge    3833.3908\n",
       "47  Neighborhood_NridgHt    9225.3317\n",
       "48  Neighborhood_OldTown   -3983.5464\n",
       "49  Neighborhood_StoneBr    3955.1904"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_df = pd.DataFrame({'Features':X_train.columns, 'Coefficient':lasso.coef_.round(4)})\n",
    "lasso_df = lasso_df[lasso_df['Coefficient'] != 0.00]\n",
    "lasso_df.reset_index(drop=True, inplace=True)\n",
    "lasso_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'LotArea': 4015.5664462389323,\n",
       " 'OverallQual': 12662.991196905054,\n",
       " 'OverallCond': 4206.383293569565,\n",
       " 'MasVnrArea': 2477.6584502739815,\n",
       " 'BsmtFinSF1': 4348.002349001189,\n",
       " 'BsmtUnfSF': -2700.299932543721,\n",
       " 'TotalBsmtSF': 10242.615874391618,\n",
       " '1stFlrSF': 9023.216817471337,\n",
       " '2ndFlrSF': 12114.107644535306,\n",
       " 'GrLivArea': 6251.395252456276,\n",
       " 'BedroomAbvGr': -2104.250900006819,\n",
       " 'GarageCars': 7830.059158751654,\n",
       " 'WoodDeckSF': 2231.0978766625035,\n",
       " 'OpenPorchSF': 2415.0579760179444,\n",
       " 'Old_NewGarage': 2717.562179042699,\n",
       " 'Built_RemodelAge': -1792.0322412970568,\n",
       " 'd_ExterQual': 3844.4048562632497,\n",
       " 'd_BsmtExposure': 3515.795479329828,\n",
       " 'd_KitchenQual': 4762.4856607553975,\n",
       " 'd_GarageFinish': 2803.2374279353103,\n",
       " 'd_BldgType': -7244.360660685416,\n",
       " 'd_SaleCondition': 1979.4851551286877,\n",
       " 'Exterior1st_CemntBd': -13010.201368293572,\n",
       " 'Exterior1st_MetalSd': -2914.9766106991297,\n",
       " 'Exterior1st_VinylSd': -9245.313351646826,\n",
       " 'Exterior1st_Wd Sdng': -4207.989216631212,\n",
       " 'Exterior2nd_CmentBd': 13656.420241592063,\n",
       " 'Exterior2nd_HdBoard': -3878.147548293064,\n",
       " 'Exterior2nd_Plywood': -3242.6317470577774,\n",
       " 'Exterior2nd_VinylSd': 4369.1547583749825,\n",
       " 'Exterior2nd_Wd Shng': -2670.640192688286,\n",
       " 'Foundation_CBlock': 3572.5999473571633,\n",
       " 'Foundation_PConc': 5299.647423620178,\n",
       " 'Foundation_Slab': 4007.323344349289,\n",
       " 'GarageType_Attchd': 5602.856067301418,\n",
       " 'GarageType_Basment': 2054.5779080626257,\n",
       " 'GarageType_BuiltIn': 4462.086423540999,\n",
       " 'GarageType_Detchd': 5105.0849965158795,\n",
       " 'GarageType_None': 6422.980590675241,\n",
       " 'MSZoning_FV': 5124.227743609539,\n",
       " 'MSZoning_RL': 4269.396536197398,\n",
       " 'MSZoning_RM': 3560.436439407115,\n",
       " 'Neighborhood_Crawfor': 4335.32039349021,\n",
       " 'Neighborhood_Edwards': -1914.5088650084954,\n",
       " 'Neighborhood_Mitchel': -1264.2072543750094,\n",
       " 'Neighborhood_NAmes': -1195.4463542380263,\n",
       " 'Neighborhood_NoRidge': 3837.16028117233,\n",
       " 'Neighborhood_NridgHt': 9238.945728987253,\n",
       " 'Neighborhood_OldTown': -4001.404910255365,\n",
       " 'Neighborhood_StoneBr': 3969.0535611939154}"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lasso_coeff_dict = dict(pd.Series(lasso.coef_, index = X_train.columns))\n",
    "lasso_coeff_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "# RFE to minimise the features to 15\n",
    "\n",
    "X_train_lasso = X_train[lasso_df.Features]\n",
    "\n",
    "lm = LinearRegression()\n",
    "lm.fit(X_train_lasso, y_train)\n",
    "\n",
    "rfe = RFE(lm, 15)            \n",
    "rfe = rfe.fit(X_train_lasso, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>Features</th>\n",
       "      <th>rfe_support</th>\n",
       "      <th>rfe_ranking</th>\n",
       "      <th>Coefficient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Exterior2nd_CmentBd</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>13656.420242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>OverallQual</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>12662.991197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2ndFlrSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>12114.107645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>TotalBsmtSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>10242.615874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Neighborhood_NridgHt</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>9238.945729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1stFlrSF</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>9023.216817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GarageCars</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>7830.059159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GrLivArea</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>6251.395252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>d_KitchenQual</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>4762.485661</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Exterior2nd_VinylSd</td>\n",
       "      <td>True</td>\n",
       "      <td>1</td>\n",
       "      <td>4369.154758</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Features  rfe_support  rfe_ranking   Coefficient\n",
       "12   Exterior2nd_CmentBd         True            1  13656.420242\n",
       "0            OverallQual         True            1  12662.991197\n",
       "4               2ndFlrSF         True            1  12114.107645\n",
       "2            TotalBsmtSF         True            1  10242.615874\n",
       "14  Neighborhood_NridgHt         True            1   9238.945729\n",
       "3               1stFlrSF         True            1   9023.216817\n",
       "6             GarageCars         True            1   7830.059159\n",
       "5              GrLivArea         True            1   6251.395252\n",
       "8          d_KitchenQual         True            1   4762.485661\n",
       "13   Exterior2nd_VinylSd         True            1   4369.154758"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Deriving the Cf values\n",
    "\n",
    "def find(x):\n",
    "    return lasso_coeff_dict[x]\n",
    "\n",
    "# top 10 features to a temp dataframe \n",
    "temp2_df = pd.DataFrame(list(zip( X_train_lasso.columns, rfe.support_, rfe.ranking_)), columns=['Features', 'rfe_support', 'rfe_ranking'])\n",
    "temp2_df = temp2_df.loc[temp2_df['rfe_support'] == True]\n",
    "temp2_df.reset_index(drop=True, inplace=True)\n",
    "\n",
    "temp2_df['Coefficient'] = temp2_df['Features'].apply(find)\n",
    "temp2_df = temp2_df.sort_values(by=['Coefficient'], ascending=False)\n",
    "temp2_df = temp2_df.head(10)\n",
    "temp2_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bar plot to determine the variables that would affect sale price\n",
    "\n",
    "plt.figure(figsize=(20,20))\n",
    "plt.subplot(4,3,1)\n",
    "sns.barplot(y = 'Features', x='Coefficient', palette='Set1', data = temp2_df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. Conclusion"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "The optimal lambda value of ridge and lasso is the same:\n",
    "Ridge - 5.0\n",
    "Lasso - 5.0\n",
    "\n",
    "The Mean Squared error of ridge and lasso:\n",
    "\n",
    "Ridge - 686979474.1272863\n",
    "Lasso - 692759479.2427036\n",
    "Mean Squared Error of Ridge and Lasso has a very slight difference. Ridge is bit lower than Lasso.  \n",
    "\n",
    "Both models are giving a good R square.\n",
    "\n",
    "Top 5 features from both Ridge and Lasso regression model are:\n",
    "Important 5 predictors:\n",
    "1. OverallQual\n",
    "2. Exterior 2nd_CementBd\n",
    "3. TotalBsmtSF\n",
    "4. 2ndFlrSF\n",
    "5. Neighborhood_NridgHT\n",
    "\n"
   ]
  }
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