House_Prediction - Analysis and Data cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loaders "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import anderson\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "import scipy.stats as stats\n",
    "pd.pandas.set_option('display.max_columns',None)\n",
    "from IPython.display import HTML\n",
    "from sklearn import preprocessing \n",
    "from sklearn.model_selection import train_test_split \n",
    "from scipy.stats import skew\n",
    "from scipy.stats.stats import pearsonr\n",
    "\n",
    "main_train_df = pd.read_csv(..\"train.csv\")\n",
    "main_test_df = pd.read_csv(..\"test.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</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>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>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</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>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>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</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>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>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</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>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>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</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>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>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</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>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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>50</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>14115</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Mitchel</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1.5Fin</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1993</td>\n",
       "      <td>1995</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Wood</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>732</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>64</td>\n",
       "      <td>796</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>796</td>\n",
       "      <td>566</td>\n",
       "      <td>0</td>\n",
       "      <td>1362</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2</td>\n",
       "      <td>480</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>0</td>\n",
       "      <td>320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>10</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>143000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>10084</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Somerst</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2004</td>\n",
       "      <td>2005</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>Stone</td>\n",
       "      <td>186.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>1369</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>317</td>\n",
       "      <td>1686</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1694</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1694</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>636</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>255</td>\n",
       "      <td>57</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>307000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10382</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NWAmes</td>\n",
       "      <td>PosN</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>1973</td>\n",
       "      <td>1973</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>Stone</td>\n",
       "      <td>240.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>859</td>\n",
       "      <td>BLQ</td>\n",
       "      <td>32</td>\n",
       "      <td>216</td>\n",
       "      <td>1107</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1107</td>\n",
       "      <td>983</td>\n",
       "      <td>0</td>\n",
       "      <td>2090</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>484</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>235</td>\n",
       "      <td>204</td>\n",
       "      <td>228</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shed</td>\n",
       "      <td>350</td>\n",
       "      <td>11</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>50</td>\n",
       "      <td>RM</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6120</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>OldTown</td>\n",
       "      <td>Artery</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1.5Fin</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1931</td>\n",
       "      <td>1950</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>952</td>\n",
       "      <td>952</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>FuseF</td>\n",
       "      <td>1022</td>\n",
       "      <td>752</td>\n",
       "      <td>0</td>\n",
       "      <td>1774</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>8</td>\n",
       "      <td>Min1</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1931.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2</td>\n",
       "      <td>468</td>\n",
       "      <td>Fa</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>90</td>\n",
       "      <td>0</td>\n",
       "      <td>205</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>129900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>190</td>\n",
       "      <td>RL</td>\n",
       "      <td>50.0</td>\n",
       "      <td>7420</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>BrkSide</td>\n",
       "      <td>Artery</td>\n",
       "      <td>Artery</td>\n",
       "      <td>2fmCon</td>\n",
       "      <td>1.5Unf</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1939</td>\n",
       "      <td>1950</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>851</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>140</td>\n",
       "      <td>991</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1077</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1077</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1939.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>1</td>\n",
       "      <td>205</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>118000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>70.0</td>\n",
       "      <td>11200</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Sawyer</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1965</td>\n",
       "      <td>1965</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Rec</td>\n",
       "      <td>906</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>134</td>\n",
       "      <td>1040</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1040</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1040</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1</td>\n",
       "      <td>384</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</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>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>129500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>11924</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NridgHt</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2006</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>WdShing</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>Stone</td>\n",
       "      <td>286.0</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>998</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>177</td>\n",
       "      <td>1175</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1182</td>\n",
       "      <td>1142</td>\n",
       "      <td>0</td>\n",
       "      <td>2324</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Ex</td>\n",
       "      <td>11</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>Gd</td>\n",
       "      <td>BuiltIn</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>3</td>\n",
       "      <td>736</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>147</td>\n",
       "      <td>21</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2006</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12968</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR2</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Sawyer</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1962</td>\n",
       "      <td>1962</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>Plywood</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>737</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>175</td>\n",
       "      <td>912</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>912</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>912</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>4</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1962.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1</td>\n",
       "      <td>352</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>140</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>176</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>144000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>91.0</td>\n",
       "      <td>10652</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2006</td>\n",
       "      <td>2007</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>Stone</td>\n",
       "      <td>306.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>1494</td>\n",
       "      <td>1494</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1494</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1494</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>840</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>160</td>\n",
       "      <td>33</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>279500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10920</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1960</td>\n",
       "      <td>1960</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>212.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>BLQ</td>\n",
       "      <td>733</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>520</td>\n",
       "      <td>1253</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1253</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1253</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Fa</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>1</td>\n",
       "      <td>352</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>213</td>\n",
       "      <td>176</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdWo</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>157000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>45</td>\n",
       "      <td>RM</td>\n",
       "      <td>51.0</td>\n",
       "      <td>6120</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>BrkSide</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1.5Unf</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>1929</td>\n",
       "      <td>2001</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>832</td>\n",
       "      <td>832</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>FuseA</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1991.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2</td>\n",
       "      <td>576</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>48</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>132000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11241</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>CulDSac</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1970</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>180.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>578</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>426</td>\n",
       "      <td>1004</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1004</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1970.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2</td>\n",
       "      <td>480</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shed</td>\n",
       "      <td>700</td>\n",
       "      <td>3</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>149000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>90</td>\n",
       "      <td>RL</td>\n",
       "      <td>72.0</td>\n",
       "      <td>10791</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Sawyer</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Duplex</td>\n",
       "      <td>1Story</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>1967</td>\n",
       "      <td>1967</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Slab</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1296</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1296</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CarPort</td>\n",
       "      <td>1967.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2</td>\n",
       "      <td>516</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Shed</td>\n",
       "      <td>500</td>\n",
       "      <td>10</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>90000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>13695</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>SawyerW</td>\n",
       "      <td>RRAe</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2004</td>\n",
       "      <td>2004</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>646</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>468</td>\n",
       "      <td>1114</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1114</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1114</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2</td>\n",
       "      <td>576</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>102</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>159000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>70.0</td>\n",
       "      <td>7560</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1958</td>\n",
       "      <td>1965</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>Plywood</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>504</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>525</td>\n",
       "      <td>1029</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1339</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1339</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Min1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1958.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1</td>\n",
       "      <td>294</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</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>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2009</td>\n",
       "      <td>COD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>139000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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",
       "5    6          50       RL         85.0    14115   Pave   NaN      IR1   \n",
       "6    7          20       RL         75.0    10084   Pave   NaN      Reg   \n",
       "7    8          60       RL          NaN    10382   Pave   NaN      IR1   \n",
       "8    9          50       RM         51.0     6120   Pave   NaN      Reg   \n",
       "9   10         190       RL         50.0     7420   Pave   NaN      Reg   \n",
       "10  11          20       RL         70.0    11200   Pave   NaN      Reg   \n",
       "11  12          60       RL         85.0    11924   Pave   NaN      IR1   \n",
       "12  13          20       RL          NaN    12968   Pave   NaN      IR2   \n",
       "13  14          20       RL         91.0    10652   Pave   NaN      IR1   \n",
       "14  15          20       RL          NaN    10920   Pave   NaN      IR1   \n",
       "15  16          45       RM         51.0     6120   Pave   NaN      Reg   \n",
       "16  17          20       RL          NaN    11241   Pave   NaN      IR1   \n",
       "17  18          90       RL         72.0    10791   Pave   NaN      Reg   \n",
       "18  19          20       RL         66.0    13695   Pave   NaN      Reg   \n",
       "19  20          20       RL         70.0     7560   Pave   NaN      Reg   \n",
       "\n",
       "   LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0          Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1          Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2          Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3          Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4          Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "5          Lvl    AllPub    Inside       Gtl      Mitchel       Norm   \n",
       "6          Lvl    AllPub    Inside       Gtl      Somerst       Norm   \n",
       "7          Lvl    AllPub    Corner       Gtl       NWAmes       PosN   \n",
       "8          Lvl    AllPub    Inside       Gtl      OldTown     Artery   \n",
       "9          Lvl    AllPub    Corner       Gtl      BrkSide     Artery   \n",
       "10         Lvl    AllPub    Inside       Gtl       Sawyer       Norm   \n",
       "11         Lvl    AllPub    Inside       Gtl      NridgHt       Norm   \n",
       "12         Lvl    AllPub    Inside       Gtl       Sawyer       Norm   \n",
       "13         Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "14         Lvl    AllPub    Corner       Gtl        NAmes       Norm   \n",
       "15         Lvl    AllPub    Corner       Gtl      BrkSide       Norm   \n",
       "16         Lvl    AllPub   CulDSac       Gtl        NAmes       Norm   \n",
       "17         Lvl    AllPub    Inside       Gtl       Sawyer       Norm   \n",
       "18         Lvl    AllPub    Inside       Gtl      SawyerW       RRAe   \n",
       "19         Lvl    AllPub    Inside       Gtl        NAmes       Norm   \n",
       "\n",
       "   Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0        Norm     1Fam     2Story            7            5       2003   \n",
       "1        Norm     1Fam     1Story            6            8       1976   \n",
       "2        Norm     1Fam     2Story            7            5       2001   \n",
       "3        Norm     1Fam     2Story            7            5       1915   \n",
       "4        Norm     1Fam     2Story            8            5       2000   \n",
       "5        Norm     1Fam     1.5Fin            5            5       1993   \n",
       "6        Norm     1Fam     1Story            8            5       2004   \n",
       "7        Norm     1Fam     2Story            7            6       1973   \n",
       "8        Norm     1Fam     1.5Fin            7            5       1931   \n",
       "9      Artery   2fmCon     1.5Unf            5            6       1939   \n",
       "10       Norm     1Fam     1Story            5            5       1965   \n",
       "11       Norm     1Fam     2Story            9            5       2005   \n",
       "12       Norm     1Fam     1Story            5            6       1962   \n",
       "13       Norm     1Fam     1Story            7            5       2006   \n",
       "14       Norm     1Fam     1Story            6            5       1960   \n",
       "15       Norm     1Fam     1.5Unf            7            8       1929   \n",
       "16       Norm     1Fam     1Story            6            7       1970   \n",
       "17       Norm   Duplex     1Story            4            5       1967   \n",
       "18       Norm     1Fam     1Story            5            5       2004   \n",
       "19       Norm     1Fam     1Story            5            6       1958   \n",
       "\n",
       "    YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0           2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1           1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2           2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3           1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4           2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "5           1995     Gable  CompShg     VinylSd     VinylSd       None   \n",
       "6           2005     Gable  CompShg     VinylSd     VinylSd      Stone   \n",
       "7           1973     Gable  CompShg     HdBoard     HdBoard      Stone   \n",
       "8           1950     Gable  CompShg     BrkFace     Wd Shng       None   \n",
       "9           1950     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "10          1965       Hip  CompShg     HdBoard     HdBoard       None   \n",
       "11          2006       Hip  CompShg     WdShing     Wd Shng      Stone   \n",
       "12          1962       Hip  CompShg     HdBoard     Plywood       None   \n",
       "13          2007     Gable  CompShg     VinylSd     VinylSd      Stone   \n",
       "14          1960       Hip  CompShg     MetalSd     MetalSd    BrkFace   \n",
       "15          2001     Gable  CompShg     Wd Sdng     Wd Sdng       None   \n",
       "16          1970     Gable  CompShg     Wd Sdng     Wd Sdng    BrkFace   \n",
       "17          1967     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "18          2004     Gable  CompShg     VinylSd     VinylSd       None   \n",
       "19          1965       Hip  CompShg     BrkFace     Plywood       None   \n",
       "\n",
       "    MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond BsmtExposure  \\\n",
       "0        196.0        Gd        TA      PConc       Gd       TA           No   \n",
       "1          0.0        TA        TA     CBlock       Gd       TA           Gd   \n",
       "2        162.0        Gd        TA      PConc       Gd       TA           Mn   \n",
       "3          0.0        TA        TA     BrkTil       TA       Gd           No   \n",
       "4        350.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "5          0.0        TA        TA       Wood       Gd       TA           No   \n",
       "6        186.0        Gd        TA      PConc       Ex       TA           Av   \n",
       "7        240.0        TA        TA     CBlock       Gd       TA           Mn   \n",
       "8          0.0        TA        TA     BrkTil       TA       TA           No   \n",
       "9          0.0        TA        TA     BrkTil       TA       TA           No   \n",
       "10         0.0        TA        TA     CBlock       TA       TA           No   \n",
       "11       286.0        Ex        TA      PConc       Ex       TA           No   \n",
       "12         0.0        TA        TA     CBlock       TA       TA           No   \n",
       "13       306.0        Gd        TA      PConc       Gd       TA           Av   \n",
       "14       212.0        TA        TA     CBlock       TA       TA           No   \n",
       "15         0.0        TA        TA     BrkTil       TA       TA           No   \n",
       "16       180.0        TA        TA     CBlock       TA       TA           No   \n",
       "17         0.0        TA        TA       Slab      NaN      NaN          NaN   \n",
       "18         0.0        TA        TA      PConc       TA       TA           No   \n",
       "19         0.0        TA        TA     CBlock       TA       TA           No   \n",
       "\n",
       "   BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  BsmtUnfSF  TotalBsmtSF  \\\n",
       "0           GLQ         706          Unf           0        150          856   \n",
       "1           ALQ         978          Unf           0        284         1262   \n",
       "2           GLQ         486          Unf           0        434          920   \n",
       "3           ALQ         216          Unf           0        540          756   \n",
       "4           GLQ         655          Unf           0        490         1145   \n",
       "5           GLQ         732          Unf           0         64          796   \n",
       "6           GLQ        1369          Unf           0        317         1686   \n",
       "7           ALQ         859          BLQ          32        216         1107   \n",
       "8           Unf           0          Unf           0        952          952   \n",
       "9           GLQ         851          Unf           0        140          991   \n",
       "10          Rec         906          Unf           0        134         1040   \n",
       "11          GLQ         998          Unf           0        177         1175   \n",
       "12          ALQ         737          Unf           0        175          912   \n",
       "13          Unf           0          Unf           0       1494         1494   \n",
       "14          BLQ         733          Unf           0        520         1253   \n",
       "15          Unf           0          Unf           0        832          832   \n",
       "16          ALQ         578          Unf           0        426         1004   \n",
       "17          NaN           0          NaN           0          0            0   \n",
       "18          GLQ         646          Unf           0        468         1114   \n",
       "19          LwQ         504          Unf           0        525         1029   \n",
       "\n",
       "   Heating HeatingQC CentralAir Electrical  1stFlrSF  2ndFlrSF  LowQualFinSF  \\\n",
       "0     GasA        Ex          Y      SBrkr       856       854             0   \n",
       "1     GasA        Ex          Y      SBrkr      1262         0             0   \n",
       "2     GasA        Ex          Y      SBrkr       920       866             0   \n",
       "3     GasA        Gd          Y      SBrkr       961       756             0   \n",
       "4     GasA        Ex          Y      SBrkr      1145      1053             0   \n",
       "5     GasA        Ex          Y      SBrkr       796       566             0   \n",
       "6     GasA        Ex          Y      SBrkr      1694         0             0   \n",
       "7     GasA        Ex          Y      SBrkr      1107       983             0   \n",
       "8     GasA        Gd          Y      FuseF      1022       752             0   \n",
       "9     GasA        Ex          Y      SBrkr      1077         0             0   \n",
       "10    GasA        Ex          Y      SBrkr      1040         0             0   \n",
       "11    GasA        Ex          Y      SBrkr      1182      1142             0   \n",
       "12    GasA        TA          Y      SBrkr       912         0             0   \n",
       "13    GasA        Ex          Y      SBrkr      1494         0             0   \n",
       "14    GasA        TA          Y      SBrkr      1253         0             0   \n",
       "15    GasA        Ex          Y      FuseA       854         0             0   \n",
       "16    GasA        Ex          Y      SBrkr      1004         0             0   \n",
       "17    GasA        TA          Y      SBrkr      1296         0             0   \n",
       "18    GasA        Ex          Y      SBrkr      1114         0             0   \n",
       "19    GasA        TA          Y      SBrkr      1339         0             0   \n",
       "\n",
       "    GrLivArea  BsmtFullBath  BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  \\\n",
       "0        1710             1             0         2         1             3   \n",
       "1        1262             0             1         2         0             3   \n",
       "2        1786             1             0         2         1             3   \n",
       "3        1717             1             0         1         0             3   \n",
       "4        2198             1             0         2         1             4   \n",
       "5        1362             1             0         1         1             1   \n",
       "6        1694             1             0         2         0             3   \n",
       "7        2090             1             0         2         1             3   \n",
       "8        1774             0             0         2         0             2   \n",
       "9        1077             1             0         1         0             2   \n",
       "10       1040             1             0         1         0             3   \n",
       "11       2324             1             0         3         0             4   \n",
       "12        912             1             0         1         0             2   \n",
       "13       1494             0             0         2         0             3   \n",
       "14       1253             1             0         1         1             2   \n",
       "15        854             0             0         1         0             2   \n",
       "16       1004             1             0         1         0             2   \n",
       "17       1296             0             0         2         0             2   \n",
       "18       1114             1             0         1         1             3   \n",
       "19       1339             0             0         1         0             3   \n",
       "\n",
       "    KitchenAbvGr KitchenQual  TotRmsAbvGrd Functional  Fireplaces FireplaceQu  \\\n",
       "0              1          Gd             8        Typ           0         NaN   \n",
       "1              1          TA             6        Typ           1          TA   \n",
       "2              1          Gd             6        Typ           1          TA   \n",
       "3              1          Gd             7        Typ           1          Gd   \n",
       "4              1          Gd             9        Typ           1          TA   \n",
       "5              1          TA             5        Typ           0         NaN   \n",
       "6              1          Gd             7        Typ           1          Gd   \n",
       "7              1          TA             7        Typ           2          TA   \n",
       "8              2          TA             8       Min1           2          TA   \n",
       "9              2          TA             5        Typ           2          TA   \n",
       "10             1          TA             5        Typ           0         NaN   \n",
       "11             1          Ex            11        Typ           2          Gd   \n",
       "12             1          TA             4        Typ           0         NaN   \n",
       "13             1          Gd             7        Typ           1          Gd   \n",
       "14             1          TA             5        Typ           1          Fa   \n",
       "15             1          TA             5        Typ           0         NaN   \n",
       "16             1          TA             5        Typ           1          TA   \n",
       "17             2          TA             6        Typ           0         NaN   \n",
       "18             1          Gd             6        Typ           0         NaN   \n",
       "19             1          TA             6       Min1           0         NaN   \n",
       "\n",
       "   GarageType  GarageYrBlt GarageFinish  GarageCars  GarageArea GarageQual  \\\n",
       "0      Attchd       2003.0          RFn           2         548         TA   \n",
       "1      Attchd       1976.0          RFn           2         460         TA   \n",
       "2      Attchd       2001.0          RFn           2         608         TA   \n",
       "3      Detchd       1998.0          Unf           3         642         TA   \n",
       "4      Attchd       2000.0          RFn           3         836         TA   \n",
       "5      Attchd       1993.0          Unf           2         480         TA   \n",
       "6      Attchd       2004.0          RFn           2         636         TA   \n",
       "7      Attchd       1973.0          RFn           2         484         TA   \n",
       "8      Detchd       1931.0          Unf           2         468         Fa   \n",
       "9      Attchd       1939.0          RFn           1         205         Gd   \n",
       "10     Detchd       1965.0          Unf           1         384         TA   \n",
       "11    BuiltIn       2005.0          Fin           3         736         TA   \n",
       "12     Detchd       1962.0          Unf           1         352         TA   \n",
       "13     Attchd       2006.0          RFn           3         840         TA   \n",
       "14     Attchd       1960.0          RFn           1         352         TA   \n",
       "15     Detchd       1991.0          Unf           2         576         TA   \n",
       "16     Attchd       1970.0          Fin           2         480         TA   \n",
       "17    CarPort       1967.0          Unf           2         516         TA   \n",
       "18     Detchd       2004.0          Unf           2         576         TA   \n",
       "19     Attchd       1958.0          Unf           1         294         TA   \n",
       "\n",
       "   GarageCond PavedDrive  WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  \\\n",
       "0          TA          Y           0           61              0          0   \n",
       "1          TA          Y         298            0              0          0   \n",
       "2          TA          Y           0           42              0          0   \n",
       "3          TA          Y           0           35            272          0   \n",
       "4          TA          Y         192           84              0          0   \n",
       "5          TA          Y          40           30              0        320   \n",
       "6          TA          Y         255           57              0          0   \n",
       "7          TA          Y         235          204            228          0   \n",
       "8          TA          Y          90            0            205          0   \n",
       "9          TA          Y           0            4              0          0   \n",
       "10         TA          Y           0            0              0          0   \n",
       "11         TA          Y         147           21              0          0   \n",
       "12         TA          Y         140            0              0          0   \n",
       "13         TA          Y         160           33              0          0   \n",
       "14         TA          Y           0          213            176          0   \n",
       "15         TA          Y          48          112              0          0   \n",
       "16         TA          Y           0            0              0          0   \n",
       "17         TA          Y           0            0              0          0   \n",
       "18         TA          Y           0          102              0          0   \n",
       "19         TA          Y           0            0              0          0   \n",
       "\n",
       "    ScreenPorch  PoolArea PoolQC  Fence MiscFeature  MiscVal  MoSold  YrSold  \\\n",
       "0             0         0    NaN    NaN         NaN        0       2    2008   \n",
       "1             0         0    NaN    NaN         NaN        0       5    2007   \n",
       "2             0         0    NaN    NaN         NaN        0       9    2008   \n",
       "3             0         0    NaN    NaN         NaN        0       2    2006   \n",
       "4             0         0    NaN    NaN         NaN        0      12    2008   \n",
       "5             0         0    NaN  MnPrv        Shed      700      10    2009   \n",
       "6             0         0    NaN    NaN         NaN        0       8    2007   \n",
       "7             0         0    NaN    NaN        Shed      350      11    2009   \n",
       "8             0         0    NaN    NaN         NaN        0       4    2008   \n",
       "9             0         0    NaN    NaN         NaN        0       1    2008   \n",
       "10            0         0    NaN    NaN         NaN        0       2    2008   \n",
       "11            0         0    NaN    NaN         NaN        0       7    2006   \n",
       "12          176         0    NaN    NaN         NaN        0       9    2008   \n",
       "13            0         0    NaN    NaN         NaN        0       8    2007   \n",
       "14            0         0    NaN   GdWo         NaN        0       5    2008   \n",
       "15            0         0    NaN  GdPrv         NaN        0       7    2007   \n",
       "16            0         0    NaN    NaN        Shed      700       3    2010   \n",
       "17            0         0    NaN    NaN        Shed      500      10    2006   \n",
       "18            0         0    NaN    NaN         NaN        0       6    2008   \n",
       "19            0         0    NaN  MnPrv         NaN        0       5    2009   \n",
       "\n",
       "   SaleType SaleCondition  SalePrice  \n",
       "0        WD        Normal     208500  \n",
       "1        WD        Normal     181500  \n",
       "2        WD        Normal     223500  \n",
       "3        WD       Abnorml     140000  \n",
       "4        WD        Normal     250000  \n",
       "5        WD        Normal     143000  \n",
       "6        WD        Normal     307000  \n",
       "7        WD        Normal     200000  \n",
       "8        WD       Abnorml     129900  \n",
       "9        WD        Normal     118000  \n",
       "10       WD        Normal     129500  \n",
       "11      New       Partial     345000  \n",
       "12       WD        Normal     144000  \n",
       "13      New       Partial     279500  \n",
       "14       WD        Normal     157000  \n",
       "15       WD        Normal     132000  \n",
       "16       WD        Normal     149000  \n",
       "17       WD        Normal      90000  \n",
       "18       WD        Normal     159000  \n",
       "19      COD       Abnorml     139000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "main_train_df.head(20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data summary "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data shape: (1460, 80)\n",
      "\n",
      "Feature datatypes:\n",
      "int64 -> 34\n",
      "object -> 43\n",
      "float64 -> 3\n",
      "\n",
      "Features with NA values more than 20%:\n",
      "Alley -> 93.77\n",
      "FireplaceQu -> 47.26\n",
      "PoolQC -> 99.52\n",
      "Fence -> 80.75\n",
      "MiscFeature -> 96.3\n",
      "\n",
      "Data description:\n",
      "\n"
     ]
    },
    {
     "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>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</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>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>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>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>1379.000000</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>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>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>46.549315</td>\n",
       "      <td>567.240411</td>\n",
       "      <td>1057.429452</td>\n",
       "      <td>1162.626712</td>\n",
       "      <td>346.992466</td>\n",
       "      <td>5.844521</td>\n",
       "      <td>1515.463699</td>\n",
       "      <td>0.425342</td>\n",
       "      <td>0.057534</td>\n",
       "      <td>1.565068</td>\n",
       "      <td>0.382877</td>\n",
       "      <td>2.866438</td>\n",
       "      <td>1.046575</td>\n",
       "      <td>6.517808</td>\n",
       "      <td>0.613014</td>\n",
       "      <td>1978.506164</td>\n",
       "      <td>1.767123</td>\n",
       "      <td>472.980137</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>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>161.319273</td>\n",
       "      <td>441.866955</td>\n",
       "      <td>438.705324</td>\n",
       "      <td>386.587738</td>\n",
       "      <td>436.528436</td>\n",
       "      <td>48.623081</td>\n",
       "      <td>525.480383</td>\n",
       "      <td>0.518911</td>\n",
       "      <td>0.238753</td>\n",
       "      <td>0.550916</td>\n",
       "      <td>0.502885</td>\n",
       "      <td>0.815778</td>\n",
       "      <td>0.220338</td>\n",
       "      <td>1.625393</td>\n",
       "      <td>0.644666</td>\n",
       "      <td>24.689725</td>\n",
       "      <td>0.747315</td>\n",
       "      <td>213.804841</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>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>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</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>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</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",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\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>0.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>795.750000</td>\n",
       "      <td>882.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1129.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1961.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>334.500000</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>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>0.000000</td>\n",
       "      <td>477.500000</td>\n",
       "      <td>991.500000</td>\n",
       "      <td>1087.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1464.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>480.000000</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>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>0.000000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>1298.250000</td>\n",
       "      <td>1391.250000</td>\n",
       "      <td>728.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1776.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>576.000000</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>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>1474.000000</td>\n",
       "      <td>2336.000000</td>\n",
       "      <td>6110.000000</td>\n",
       "      <td>4692.000000</td>\n",
       "      <td>2065.000000</td>\n",
       "      <td>572.000000</td>\n",
       "      <td>5642.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1418.000000</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",
       "</div>"
      ],
      "text/plain": [
       "        MSSubClass  LotFrontage        LotArea  OverallQual  OverallCond  \\\n",
       "count  1460.000000  1201.000000    1460.000000  1460.000000  1460.000000   \n",
       "mean     56.897260    70.049958   10516.828082     6.099315     5.575342   \n",
       "std      42.300571    24.284752    9981.264932     1.382997     1.112799   \n",
       "min      20.000000    21.000000    1300.000000     1.000000     1.000000   \n",
       "25%      20.000000    59.000000    7553.500000     5.000000     5.000000   \n",
       "50%      50.000000    69.000000    9478.500000     6.000000     5.000000   \n",
       "75%      70.000000    80.000000   11601.500000     7.000000     6.000000   \n",
       "max     190.000000   313.000000  215245.000000    10.000000     9.000000   \n",
       "\n",
       "         YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1   BsmtFinSF2  \\\n",
       "count  1460.000000   1460.000000  1452.000000  1460.000000  1460.000000   \n",
       "mean   1971.267808   1984.865753   103.685262   443.639726    46.549315   \n",
       "std      30.202904     20.645407   181.066207   456.098091   161.319273   \n",
       "min    1872.000000   1950.000000     0.000000     0.000000     0.000000   \n",
       "25%    1954.000000   1967.000000     0.000000     0.000000     0.000000   \n",
       "50%    1973.000000   1994.000000     0.000000   383.500000     0.000000   \n",
       "75%    2000.000000   2004.000000   166.000000   712.250000     0.000000   \n",
       "max    2010.000000   2010.000000  1600.000000  5644.000000  1474.000000   \n",
       "\n",
       "         BsmtUnfSF  TotalBsmtSF     1stFlrSF     2ndFlrSF  LowQualFinSF  \\\n",
       "count  1460.000000  1460.000000  1460.000000  1460.000000   1460.000000   \n",
       "mean    567.240411  1057.429452  1162.626712   346.992466      5.844521   \n",
       "std     441.866955   438.705324   386.587738   436.528436     48.623081   \n",
       "min       0.000000     0.000000   334.000000     0.000000      0.000000   \n",
       "25%     223.000000   795.750000   882.000000     0.000000      0.000000   \n",
       "50%     477.500000   991.500000  1087.000000     0.000000      0.000000   \n",
       "75%     808.000000  1298.250000  1391.250000   728.000000      0.000000   \n",
       "max    2336.000000  6110.000000  4692.000000  2065.000000    572.000000   \n",
       "\n",
       "         GrLivArea  BsmtFullBath  BsmtHalfBath     FullBath     HalfBath  \\\n",
       "count  1460.000000   1460.000000   1460.000000  1460.000000  1460.000000   \n",
       "mean   1515.463699      0.425342      0.057534     1.565068     0.382877   \n",
       "std     525.480383      0.518911      0.238753     0.550916     0.502885   \n",
       "min     334.000000      0.000000      0.000000     0.000000     0.000000   \n",
       "25%    1129.500000      0.000000      0.000000     1.000000     0.000000   \n",
       "50%    1464.000000      0.000000      0.000000     2.000000     0.000000   \n",
       "75%    1776.750000      1.000000      0.000000     2.000000     1.000000   \n",
       "max    5642.000000      3.000000      2.000000     3.000000     2.000000   \n",
       "\n",
       "       BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd   Fireplaces  GarageYrBlt  \\\n",
       "count   1460.000000   1460.000000   1460.000000  1460.000000  1379.000000   \n",
       "mean       2.866438      1.046575      6.517808     0.613014  1978.506164   \n",
       "std        0.815778      0.220338      1.625393     0.644666    24.689725   \n",
       "min        0.000000      0.000000      2.000000     0.000000  1900.000000   \n",
       "25%        2.000000      1.000000      5.000000     0.000000  1961.000000   \n",
       "50%        3.000000      1.000000      6.000000     1.000000  1980.000000   \n",
       "75%        3.000000      1.000000      7.000000     1.000000  2002.000000   \n",
       "max        8.000000      3.000000     14.000000     3.000000  2010.000000   \n",
       "\n",
       "        GarageCars   GarageArea   WoodDeckSF  OpenPorchSF  EnclosedPorch  \\\n",
       "count  1460.000000  1460.000000  1460.000000  1460.000000    1460.000000   \n",
       "mean      1.767123   472.980137    94.244521    46.660274      21.954110   \n",
       "std       0.747315   213.804841   125.338794    66.256028      61.119149   \n",
       "min       0.000000     0.000000     0.000000     0.000000       0.000000   \n",
       "25%       1.000000   334.500000     0.000000     0.000000       0.000000   \n",
       "50%       2.000000   480.000000     0.000000    25.000000       0.000000   \n",
       "75%       2.000000   576.000000   168.000000    68.000000       0.000000   \n",
       "max       4.000000  1418.000000   857.000000   547.000000     552.000000   \n",
       "\n",
       "         3SsnPorch  ScreenPorch     PoolArea       MiscVal       MoSold  \\\n",
       "count  1460.000000  1460.000000  1460.000000   1460.000000  1460.000000   \n",
       "mean      3.409589    15.060959     2.758904     43.489041     6.321918   \n",
       "std      29.317331    55.757415    40.177307    496.123024     2.703626   \n",
       "min       0.000000     0.000000     0.000000      0.000000     1.000000   \n",
       "25%       0.000000     0.000000     0.000000      0.000000     5.000000   \n",
       "50%       0.000000     0.000000     0.000000      0.000000     6.000000   \n",
       "75%       0.000000     0.000000     0.000000      0.000000     8.000000   \n",
       "max     508.000000   480.000000   738.000000  15500.000000    12.000000   \n",
       "\n",
       "            YrSold      SalePrice  \n",
       "count  1460.000000    1460.000000  \n",
       "mean   2007.815753  180921.195890  \n",
       "std       1.328095   79442.502883  \n",
       "min    2006.000000   34900.000000  \n",
       "25%    2007.000000  129975.000000  \n",
       "50%    2008.000000  163000.000000  \n",
       "75%    2009.000000  214000.000000  \n",
       "max    2010.000000  755000.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df = main_train_df\n",
    "train_df.drop(columns=\"Id\", inplace = True)\n",
    "test_df = main_test_df\n",
    "print(\"Data shape:\",train_df.shape)\n",
    "print()\n",
    "type_ls = []\n",
    "type_dict = {}\n",
    "for col in train_df.columns:\n",
    "    if train_df[col].dtype not in type_ls:\n",
    "        type_ls.append(train_df[col].dtype)\n",
    "        type_dict[train_df[col].dtype] = 1\n",
    "    else:\n",
    "        counter = type_dict[train_df[col].dtype]\n",
    "        type_dict[train_df[col].dtype] = counter + 1\n",
    "print(\"Feature datatypes:\")\n",
    "for key, value in type_dict.items():\n",
    "    print(key,\"->\",value)    \n",
    "print()\n",
    "print(\"Features with NA values more than 20%:\")\n",
    "for col in train_df.columns:\n",
    "    na_ptg = round(100*(train_df[col].isna().sum()/len(train_df)),2)\n",
    "    if na_ptg >= 20:\n",
    "        print(\"{} -> {}\".format(col, na_ptg))\n",
    "print()\n",
    "print(\"Data description:\\n\")\n",
    "train_df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* The data provided has 1460 unique data-points along with 81 columns.\n",
    "* There are 35 \"Integer\" type columns, 43 columns of type \"Object\" and 3 of \"Float\" type.\n",
    "* There are 4 columns with blank values (>50%), they are: Alley (93.77%), PoolQC (99.52%), Fence (80.75%) and MiscFeature (96.3%)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Numeric Discrete variables:\n",
      " ['MSSubClass', 'OverallQual', 'OverallCond', 'LowQualFinSF', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageCars', '3SsnPorch', 'PoolArea', 'MiscVal', 'MoSold', 'YrSold']\n",
      "\n",
      "Categorical variables:\n",
      " ['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']\n",
      "\n",
      "Numeric variables:\n",
      " ['LotFrontage', 'LotArea', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'GarageYrBlt', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', 'ScreenPorch', 'SalePrice']\n"
     ]
    }
   ],
   "source": [
    "numeric_discrete_vars = []\n",
    "categorical_vars = []\n",
    "numeric_vars = []\n",
    "for col in train_df.columns:\n",
    "    if train_df[col].dtype == \"object\":\n",
    "        categorical_vars.append(col)\n",
    "    elif len(train_df[col].unique()) < 25:\n",
    "           numeric_discrete_vars.append(col)\n",
    "    else:\n",
    "        numeric_vars.append(col)\n",
    "print(\"Numeric Discrete variables:\\n\",numeric_discrete_vars)\n",
    "print()\n",
    "print(\"Categorical variables:\\n\",categorical_vars)\n",
    "print()\n",
    "print(\"Numeric variables:\\n\",numeric_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Data visualization, Variable analysis and EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "        }#T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col11,#T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col9,#T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col13,#T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col12{\n",
       "            background-color:  #f7a98b;\n",
       "            color:  #000000;\n",
       "        }#T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col18,#T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col9{\n",
       "            background-color:  #f59f80;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ef886b;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f6bea4;\n",
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       "            background-color:  #ead5c9;\n",
       "            color:  #000000;\n",
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       "            background-color:  #ec8165;\n",
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       "            background-color:  #6384eb;\n",
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       "            background-color:  #97b8ff;\n",
       "            color:  #000000;\n",
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       "            background-color:  #dbdcde;\n",
       "            color:  #000000;\n",
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       "            background-color:  #8caffe;\n",
       "            color:  #000000;\n",
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       "            background-color:  #f49a7b;\n",
       "            color:  #000000;\n",
       "        }#T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col15,#T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col14{\n",
       "            background-color:  #b6cefa;\n",
       "            color:  #000000;\n",
       "        }#T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col18,#T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col14{\n",
       "            background-color:  #e9d5cb;\n",
       "            color:  #000000;\n",
       "        }#T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col17,#T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col15{\n",
       "            background-color:  #bad0f8;\n",
       "            color:  #000000;\n",
       "        }#T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col17,#T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col16{\n",
       "            background-color:  #96b7ff;\n",
       "            color:  #000000;\n",
       "        }#T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col18,#T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col16{\n",
       "            background-color:  #8badfd;\n",
       "            color:  #000000;\n",
       "        }</style><table id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >LotFrontage</th>        <th class=\"col_heading level0 col1\" >LotArea</th>        <th class=\"col_heading level0 col2\" >YearBuilt</th>        <th class=\"col_heading level0 col3\" >YearRemodAdd</th>        <th class=\"col_heading level0 col4\" >MasVnrArea</th>        <th class=\"col_heading level0 col5\" >BsmtFinSF1</th>        <th class=\"col_heading level0 col6\" >BsmtFinSF2</th>        <th class=\"col_heading level0 col7\" >BsmtUnfSF</th>        <th class=\"col_heading level0 col8\" >TotalBsmtSF</th>        <th class=\"col_heading level0 col9\" >1stFlrSF</th>        <th class=\"col_heading level0 col10\" >2ndFlrSF</th>        <th class=\"col_heading level0 col11\" >GrLivArea</th>        <th class=\"col_heading level0 col12\" >GarageYrBlt</th>        <th class=\"col_heading level0 col13\" >GarageArea</th>        <th class=\"col_heading level0 col14\" >WoodDeckSF</th>        <th class=\"col_heading level0 col15\" >OpenPorchSF</th>        <th class=\"col_heading level0 col16\" >EnclosedPorch</th>        <th class=\"col_heading level0 col17\" >ScreenPorch</th>        <th class=\"col_heading level0 col18\" >SalePrice</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row0\" class=\"row_heading level0 row0\" >LotFrontage</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col0\" class=\"data row0 col0\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col1\" class=\"data row0 col1\" >0.426095</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col2\" class=\"data row0 col2\" >0.123349</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col3\" class=\"data row0 col3\" >0.088866</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col4\" class=\"data row0 col4\" >0.193458</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col5\" class=\"data row0 col5\" >0.233633</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col6\" class=\"data row0 col6\" >0.049900</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col7\" class=\"data row0 col7\" >0.132644</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col8\" class=\"data row0 col8\" >0.392075</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col9\" class=\"data row0 col9\" >0.457181</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col10\" class=\"data row0 col10\" >0.080177</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col11\" class=\"data row0 col11\" >0.402797</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col12\" class=\"data row0 col12\" >0.070250</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col13\" class=\"data row0 col13\" >0.344997</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col14\" class=\"data row0 col14\" >0.088521</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col15\" class=\"data row0 col15\" >0.151972</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col16\" class=\"data row0 col16\" >0.010700</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col17\" class=\"data row0 col17\" >0.041383</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row0_col18\" class=\"data row0 col18\" >0.351799</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row1\" class=\"row_heading level0 row1\" >LotArea</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col0\" class=\"data row1 col0\" >0.426095</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col1\" class=\"data row1 col1\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col2\" class=\"data row1 col2\" >0.014228</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col3\" class=\"data row1 col3\" >0.013788</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col4\" class=\"data row1 col4\" >0.104160</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col5\" class=\"data row1 col5\" >0.214103</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col6\" class=\"data row1 col6\" >0.111170</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col7\" class=\"data row1 col7\" >-0.002618</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col8\" class=\"data row1 col8\" >0.260833</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col9\" class=\"data row1 col9\" >0.299475</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col10\" class=\"data row1 col10\" >0.050986</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col11\" class=\"data row1 col11\" >0.263116</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col12\" class=\"data row1 col12\" >-0.024947</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col13\" class=\"data row1 col13\" >0.180403</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col14\" class=\"data row1 col14\" >0.171698</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col15\" class=\"data row1 col15\" >0.084774</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col16\" class=\"data row1 col16\" >-0.018340</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col17\" class=\"data row1 col17\" >0.043160</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row1_col18\" class=\"data row1 col18\" >0.263843</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row2\" class=\"row_heading level0 row2\" >YearBuilt</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col0\" class=\"data row2 col0\" >0.123349</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col1\" class=\"data row2 col1\" >0.014228</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col2\" class=\"data row2 col2\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col3\" class=\"data row2 col3\" >0.592855</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col4\" class=\"data row2 col4\" >0.315707</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col5\" class=\"data row2 col5\" >0.249503</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col6\" class=\"data row2 col6\" >-0.049107</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col7\" class=\"data row2 col7\" >0.149040</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col8\" class=\"data row2 col8\" >0.391452</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col9\" class=\"data row2 col9\" >0.281986</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col10\" class=\"data row2 col10\" >0.010308</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col11\" class=\"data row2 col11\" >0.199010</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col12\" class=\"data row2 col12\" >0.825667</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col13\" class=\"data row2 col13\" >0.478954</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col14\" class=\"data row2 col14\" >0.224880</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col15\" class=\"data row2 col15\" >0.188686</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col16\" class=\"data row2 col16\" >-0.387268</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col17\" class=\"data row2 col17\" >-0.050364</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row2_col18\" class=\"data row2 col18\" >0.522897</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row3\" class=\"row_heading level0 row3\" >YearRemodAdd</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col0\" class=\"data row3 col0\" >0.088866</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col1\" class=\"data row3 col1\" >0.013788</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col2\" class=\"data row3 col2\" >0.592855</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col3\" class=\"data row3 col3\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col4\" class=\"data row3 col4\" >0.179618</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col5\" class=\"data row3 col5\" >0.128451</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col6\" class=\"data row3 col6\" >-0.067759</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col7\" class=\"data row3 col7\" >0.181133</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col8\" class=\"data row3 col8\" >0.291066</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col9\" class=\"data row3 col9\" >0.240379</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col10\" class=\"data row3 col10\" >0.140024</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col11\" class=\"data row3 col11\" >0.287389</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col12\" class=\"data row3 col12\" >0.642277</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col13\" class=\"data row3 col13\" >0.371600</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col14\" class=\"data row3 col14\" >0.205726</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col15\" class=\"data row3 col15\" >0.226298</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col16\" class=\"data row3 col16\" >-0.193919</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col17\" class=\"data row3 col17\" >-0.038740</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row3_col18\" class=\"data row3 col18\" >0.507101</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row4\" class=\"row_heading level0 row4\" >MasVnrArea</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col0\" class=\"data row4 col0\" >0.193458</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col1\" class=\"data row4 col1\" >0.104160</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col2\" class=\"data row4 col2\" >0.315707</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col3\" class=\"data row4 col3\" >0.179618</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col4\" class=\"data row4 col4\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col5\" class=\"data row4 col5\" >0.264736</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col6\" class=\"data row4 col6\" >-0.072319</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col7\" class=\"data row4 col7\" >0.114442</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col8\" class=\"data row4 col8\" >0.363936</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col9\" class=\"data row4 col9\" >0.344501</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col10\" class=\"data row4 col10\" >0.174561</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col11\" class=\"data row4 col11\" >0.390857</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col12\" class=\"data row4 col12\" >0.252691</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col13\" class=\"data row4 col13\" >0.373066</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col14\" class=\"data row4 col14\" >0.159718</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col15\" class=\"data row4 col15\" >0.125703</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col16\" class=\"data row4 col16\" >-0.110204</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col17\" class=\"data row4 col17\" >0.061466</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row4_col18\" class=\"data row4 col18\" >0.477493</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row5\" class=\"row_heading level0 row5\" >BsmtFinSF1</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col0\" class=\"data row5 col0\" >0.233633</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col1\" class=\"data row5 col1\" >0.214103</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col2\" class=\"data row5 col2\" >0.249503</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col3\" class=\"data row5 col3\" >0.128451</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col4\" class=\"data row5 col4\" >0.264736</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col5\" class=\"data row5 col5\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col6\" class=\"data row5 col6\" >-0.050117</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col7\" class=\"data row5 col7\" >-0.495251</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col8\" class=\"data row5 col8\" >0.522396</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col9\" class=\"data row5 col9\" >0.445863</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col10\" class=\"data row5 col10\" >-0.137079</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col11\" class=\"data row5 col11\" >0.208171</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col12\" class=\"data row5 col12\" >0.153484</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col13\" class=\"data row5 col13\" >0.296970</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col14\" class=\"data row5 col14\" >0.204306</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col15\" class=\"data row5 col15\" >0.111761</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col16\" class=\"data row5 col16\" >-0.102303</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col17\" class=\"data row5 col17\" >0.062021</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row5_col18\" class=\"data row5 col18\" >0.386420</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row6\" class=\"row_heading level0 row6\" >BsmtFinSF2</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col0\" class=\"data row6 col0\" >0.049900</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col1\" class=\"data row6 col1\" >0.111170</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col2\" class=\"data row6 col2\" >-0.049107</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col3\" class=\"data row6 col3\" >-0.067759</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col4\" class=\"data row6 col4\" >-0.072319</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col5\" class=\"data row6 col5\" >-0.050117</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col6\" class=\"data row6 col6\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col7\" class=\"data row6 col7\" >-0.209294</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col8\" class=\"data row6 col8\" >0.104810</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col9\" class=\"data row6 col9\" >0.097117</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col10\" class=\"data row6 col10\" >-0.099260</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col11\" class=\"data row6 col11\" >-0.009640</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col12\" class=\"data row6 col12\" >-0.088011</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col13\" class=\"data row6 col13\" >-0.018227</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col14\" class=\"data row6 col14\" >0.067898</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col15\" class=\"data row6 col15\" >0.003093</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col16\" class=\"data row6 col16\" >0.036543</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col17\" class=\"data row6 col17\" >0.088871</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row6_col18\" class=\"data row6 col18\" >-0.011378</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row7\" class=\"row_heading level0 row7\" >BsmtUnfSF</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col0\" class=\"data row7 col0\" >0.132644</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col1\" class=\"data row7 col1\" >-0.002618</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col2\" class=\"data row7 col2\" >0.149040</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col3\" class=\"data row7 col3\" >0.181133</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col4\" class=\"data row7 col4\" >0.114442</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col5\" class=\"data row7 col5\" >-0.495251</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col6\" class=\"data row7 col6\" >-0.209294</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col7\" class=\"data row7 col7\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col8\" class=\"data row7 col8\" >0.415360</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col9\" class=\"data row7 col9\" >0.317987</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col10\" class=\"data row7 col10\" >0.004469</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col11\" class=\"data row7 col11\" >0.240257</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col12\" class=\"data row7 col12\" >0.190708</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col13\" class=\"data row7 col13\" >0.183303</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col14\" class=\"data row7 col14\" >-0.005316</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col15\" class=\"data row7 col15\" >0.129005</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col16\" class=\"data row7 col16\" >-0.002538</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col17\" class=\"data row7 col17\" >-0.012579</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row7_col18\" class=\"data row7 col18\" >0.214479</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row8\" class=\"row_heading level0 row8\" >TotalBsmtSF</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col0\" class=\"data row8 col0\" >0.392075</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col1\" class=\"data row8 col1\" >0.260833</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col2\" class=\"data row8 col2\" >0.391452</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col3\" class=\"data row8 col3\" >0.291066</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col4\" class=\"data row8 col4\" >0.363936</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col5\" class=\"data row8 col5\" >0.522396</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col6\" class=\"data row8 col6\" >0.104810</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col7\" class=\"data row8 col7\" >0.415360</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col8\" class=\"data row8 col8\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col9\" class=\"data row8 col9\" >0.819530</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col10\" class=\"data row8 col10\" >-0.174512</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col11\" class=\"data row8 col11\" >0.454868</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col12\" class=\"data row8 col12\" >0.322445</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col13\" class=\"data row8 col13\" >0.486665</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col14\" class=\"data row8 col14\" >0.232019</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col15\" class=\"data row8 col15\" >0.247264</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col16\" class=\"data row8 col16\" >-0.095478</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col17\" class=\"data row8 col17\" >0.084489</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row8_col18\" class=\"data row8 col18\" >0.613581</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row9\" class=\"row_heading level0 row9\" >1stFlrSF</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col0\" class=\"data row9 col0\" >0.457181</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col1\" class=\"data row9 col1\" >0.299475</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col2\" class=\"data row9 col2\" >0.281986</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col3\" class=\"data row9 col3\" >0.240379</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col4\" class=\"data row9 col4\" >0.344501</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col5\" class=\"data row9 col5\" >0.445863</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col6\" class=\"data row9 col6\" >0.097117</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col7\" class=\"data row9 col7\" >0.317987</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col8\" class=\"data row9 col8\" >0.819530</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col9\" class=\"data row9 col9\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col10\" class=\"data row9 col10\" >-0.202646</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col11\" class=\"data row9 col11\" >0.566024</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col12\" class=\"data row9 col12\" >0.233449</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col13\" class=\"data row9 col13\" >0.489782</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col14\" class=\"data row9 col14\" >0.235459</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col15\" class=\"data row9 col15\" >0.211671</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col16\" class=\"data row9 col16\" >-0.065292</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col17\" class=\"data row9 col17\" >0.088758</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row9_col18\" class=\"data row9 col18\" >0.605852</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row10\" class=\"row_heading level0 row10\" >2ndFlrSF</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col0\" class=\"data row10 col0\" >0.080177</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col1\" class=\"data row10 col1\" >0.050986</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col2\" class=\"data row10 col2\" >0.010308</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col3\" class=\"data row10 col3\" >0.140024</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col4\" class=\"data row10 col4\" >0.174561</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col5\" class=\"data row10 col5\" >-0.137079</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col6\" class=\"data row10 col6\" >-0.099260</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col7\" class=\"data row10 col7\" >0.004469</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col8\" class=\"data row10 col8\" >-0.174512</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col9\" class=\"data row10 col9\" >-0.202646</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col10\" class=\"data row10 col10\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col11\" class=\"data row10 col11\" >0.687501</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col12\" class=\"data row10 col12\" >0.070832</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col13\" class=\"data row10 col13\" >0.138347</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col14\" class=\"data row10 col14\" >0.092165</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col15\" class=\"data row10 col15\" >0.208026</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col16\" class=\"data row10 col16\" >0.061989</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col17\" class=\"data row10 col17\" >0.040606</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row10_col18\" class=\"data row10 col18\" >0.319334</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row11\" class=\"row_heading level0 row11\" >GrLivArea</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col0\" class=\"data row11 col0\" >0.402797</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col1\" class=\"data row11 col1\" >0.263116</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col2\" class=\"data row11 col2\" >0.199010</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col3\" class=\"data row11 col3\" >0.287389</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col4\" class=\"data row11 col4\" >0.390857</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col5\" class=\"data row11 col5\" >0.208171</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col6\" class=\"data row11 col6\" >-0.009640</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col7\" class=\"data row11 col7\" >0.240257</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col8\" class=\"data row11 col8\" >0.454868</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col9\" class=\"data row11 col9\" >0.566024</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col10\" class=\"data row11 col10\" >0.687501</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col11\" class=\"data row11 col11\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col12\" class=\"data row11 col12\" >0.231197</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col13\" class=\"data row11 col13\" >0.468997</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col14\" class=\"data row11 col14\" >0.247433</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col15\" class=\"data row11 col15\" >0.330224</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col16\" class=\"data row11 col16\" >0.009113</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col17\" class=\"data row11 col17\" >0.101510</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row11_col18\" class=\"data row11 col18\" >0.708624</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row12\" class=\"row_heading level0 row12\" >GarageYrBlt</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col0\" class=\"data row12 col0\" >0.070250</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col1\" class=\"data row12 col1\" >-0.024947</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col2\" class=\"data row12 col2\" >0.825667</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col3\" class=\"data row12 col3\" >0.642277</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col4\" class=\"data row12 col4\" >0.252691</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col5\" class=\"data row12 col5\" >0.153484</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col6\" class=\"data row12 col6\" >-0.088011</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col7\" class=\"data row12 col7\" >0.190708</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col8\" class=\"data row12 col8\" >0.322445</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col9\" class=\"data row12 col9\" >0.233449</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col10\" class=\"data row12 col10\" >0.070832</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col11\" class=\"data row12 col11\" >0.231197</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col12\" class=\"data row12 col12\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col13\" class=\"data row12 col13\" >0.564567</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col14\" class=\"data row12 col14\" >0.224577</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col15\" class=\"data row12 col15\" >0.228425</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col16\" class=\"data row12 col16\" >-0.297003</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col17\" class=\"data row12 col17\" >-0.075418</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row12_col18\" class=\"data row12 col18\" >0.486362</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row13\" class=\"row_heading level0 row13\" >GarageArea</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col0\" class=\"data row13 col0\" >0.344997</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col1\" class=\"data row13 col1\" >0.180403</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col2\" class=\"data row13 col2\" >0.478954</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col3\" class=\"data row13 col3\" >0.371600</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col4\" class=\"data row13 col4\" >0.373066</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col5\" class=\"data row13 col5\" >0.296970</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col6\" class=\"data row13 col6\" >-0.018227</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col7\" class=\"data row13 col7\" >0.183303</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col8\" class=\"data row13 col8\" >0.486665</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col9\" class=\"data row13 col9\" >0.489782</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col10\" class=\"data row13 col10\" >0.138347</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col11\" class=\"data row13 col11\" >0.468997</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col12\" class=\"data row13 col12\" >0.564567</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col13\" class=\"data row13 col13\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col14\" class=\"data row13 col14\" >0.224666</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col15\" class=\"data row13 col15\" >0.241435</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col16\" class=\"data row13 col16\" >-0.121777</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col17\" class=\"data row13 col17\" >0.051412</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row13_col18\" class=\"data row13 col18\" >0.623431</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row14\" class=\"row_heading level0 row14\" >WoodDeckSF</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col0\" class=\"data row14 col0\" >0.088521</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col1\" class=\"data row14 col1\" >0.171698</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col2\" class=\"data row14 col2\" >0.224880</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col3\" class=\"data row14 col3\" >0.205726</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col4\" class=\"data row14 col4\" >0.159718</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col5\" class=\"data row14 col5\" >0.204306</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col6\" class=\"data row14 col6\" >0.067898</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col7\" class=\"data row14 col7\" >-0.005316</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col8\" class=\"data row14 col8\" >0.232019</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col9\" class=\"data row14 col9\" >0.235459</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col10\" class=\"data row14 col10\" >0.092165</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col11\" class=\"data row14 col11\" >0.247433</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col12\" class=\"data row14 col12\" >0.224577</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col13\" class=\"data row14 col13\" >0.224666</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col14\" class=\"data row14 col14\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col15\" class=\"data row14 col15\" >0.058661</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col16\" class=\"data row14 col16\" >-0.125989</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col17\" class=\"data row14 col17\" >-0.074181</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row14_col18\" class=\"data row14 col18\" >0.324413</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row15\" class=\"row_heading level0 row15\" >OpenPorchSF</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col0\" class=\"data row15 col0\" >0.151972</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col1\" class=\"data row15 col1\" >0.084774</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col2\" class=\"data row15 col2\" >0.188686</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col3\" class=\"data row15 col3\" >0.226298</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col4\" class=\"data row15 col4\" >0.125703</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col5\" class=\"data row15 col5\" >0.111761</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col6\" class=\"data row15 col6\" >0.003093</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col7\" class=\"data row15 col7\" >0.129005</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col8\" class=\"data row15 col8\" >0.247264</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col9\" class=\"data row15 col9\" >0.211671</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col10\" class=\"data row15 col10\" >0.208026</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col11\" class=\"data row15 col11\" >0.330224</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col12\" class=\"data row15 col12\" >0.228425</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col13\" class=\"data row15 col13\" >0.241435</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col14\" class=\"data row15 col14\" >0.058661</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col15\" class=\"data row15 col15\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col16\" class=\"data row15 col16\" >-0.093079</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col17\" class=\"data row15 col17\" >0.074304</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row15_col18\" class=\"data row15 col18\" >0.315856</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row16\" class=\"row_heading level0 row16\" >EnclosedPorch</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col0\" class=\"data row16 col0\" >0.010700</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col1\" class=\"data row16 col1\" >-0.018340</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col2\" class=\"data row16 col2\" >-0.387268</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col3\" class=\"data row16 col3\" >-0.193919</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col4\" class=\"data row16 col4\" >-0.110204</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col5\" class=\"data row16 col5\" >-0.102303</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col6\" class=\"data row16 col6\" >0.036543</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col7\" class=\"data row16 col7\" >-0.002538</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col8\" class=\"data row16 col8\" >-0.095478</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col9\" class=\"data row16 col9\" >-0.065292</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col10\" class=\"data row16 col10\" >0.061989</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col11\" class=\"data row16 col11\" >0.009113</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col12\" class=\"data row16 col12\" >-0.297003</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col13\" class=\"data row16 col13\" >-0.121777</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col14\" class=\"data row16 col14\" >-0.125989</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col15\" class=\"data row16 col15\" >-0.093079</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col16\" class=\"data row16 col16\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col17\" class=\"data row16 col17\" >-0.082864</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row16_col18\" class=\"data row16 col18\" >-0.128578</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row17\" class=\"row_heading level0 row17\" >ScreenPorch</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col0\" class=\"data row17 col0\" >0.041383</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col1\" class=\"data row17 col1\" >0.043160</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col2\" class=\"data row17 col2\" >-0.050364</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col3\" class=\"data row17 col3\" >-0.038740</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col4\" class=\"data row17 col4\" >0.061466</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col5\" class=\"data row17 col5\" >0.062021</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col6\" class=\"data row17 col6\" >0.088871</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col7\" class=\"data row17 col7\" >-0.012579</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col8\" class=\"data row17 col8\" >0.084489</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col9\" class=\"data row17 col9\" >0.088758</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col10\" class=\"data row17 col10\" >0.040606</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col11\" class=\"data row17 col11\" >0.101510</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col12\" class=\"data row17 col12\" >-0.075418</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col13\" class=\"data row17 col13\" >0.051412</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col14\" class=\"data row17 col14\" >-0.074181</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col15\" class=\"data row17 col15\" >0.074304</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col16\" class=\"data row17 col16\" >-0.082864</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col17\" class=\"data row17 col17\" >1.000000</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row17_col18\" class=\"data row17 col18\" >0.111447</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899level0_row18\" class=\"row_heading level0 row18\" >SalePrice</th>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col0\" class=\"data row18 col0\" >0.351799</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col1\" class=\"data row18 col1\" >0.263843</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col2\" class=\"data row18 col2\" >0.522897</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col3\" class=\"data row18 col3\" >0.507101</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col4\" class=\"data row18 col4\" >0.477493</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col5\" class=\"data row18 col5\" >0.386420</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col6\" class=\"data row18 col6\" >-0.011378</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col7\" class=\"data row18 col7\" >0.214479</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col8\" class=\"data row18 col8\" >0.613581</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col9\" class=\"data row18 col9\" >0.605852</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col10\" class=\"data row18 col10\" >0.319334</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col11\" class=\"data row18 col11\" >0.708624</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col12\" class=\"data row18 col12\" >0.486362</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col13\" class=\"data row18 col13\" >0.623431</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col14\" class=\"data row18 col14\" >0.324413</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col15\" class=\"data row18 col15\" >0.315856</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col16\" class=\"data row18 col16\" >-0.128578</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col17\" class=\"data row18 col17\" >0.111447</td>\n",
       "                        <td id=\"T_547adb6e_4ea4_11eb_93ae_544810ca3899row18_col18\" class=\"data row18 col18\" >1.000000</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1844fd18c10>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr = train_df[numeric_vars].corr()\n",
    "corr.style.background_gradient(cmap='coolwarm', axis=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Highly correlated features:\n",
      " {'SalePrice', 'TotalBsmtSF', 'GarageArea', 'GrLivArea', '1stFlrSF', 'YearRemodAdd', 'GarageYrBlt'}\n"
     ]
    }
   ],
   "source": [
    "high_cor_feat = set()\n",
    "for i in range(len(corr.columns)):\n",
    "    for j in range(i):\n",
    "        if abs(corr.iloc[i, j]) > 0.5:\n",
    "            colname = corr.columns[i]\n",
    "            high_cor_feat.add(colname)\n",
    "print(\"Highly correlated features:\\n\",high_cor_feat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* There are outliers present in GrLivArea and GarageArea. These two area features show high correlations.\n",
    "* BsmtFinSF1 is highly negatively correlated with BsmtUnfSF. GrLivArea has a high correlation with the TotRmsAbvGrd, GarageCars has a high correlation with the GarageArea and vice-versa.\n",
    "* Majority of the GarageArea are between 300 and 700. A large number of GrLivArea are between 1000 and 2500.\n",
    "* The Slope between GrLivArea and SalePrice is steeper than GarageArea and SalePrice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "for i in ['TotalBsmtSF', 'GarageArea', 'GrLivArea']:\n",
    "    fig, ax = plt.subplots()\n",
    "    ax.set_title(\"Area feature with outliers present\", fontsize=15)\n",
    "    ax.scatter(x = train_df[i], y = train_df['SalePrice'], alpha = 0.5, c=\"red\")\n",
    "    plt.ylabel('SalePrice', fontsize=15)\n",
    "    plt.xlabel(i, fontsize=15)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The space or area related features having a correlation more than 50% have been plotted in the above. The plots  show that they have many outliers. The presence of outliers can often lead to a fluctuating correlation factor. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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FxcdoOgaDwWBYMoymYzAYDDNgWm4vLEboGAyGJWO1beAmYXThMeY1g8GwJBQ38MxYZsoGvpJLzZiW2wuPEToGg2FJWI0buEkYXXiM0DEYDEvCatzATcLowmOEjsFgWBJW4wZuEkYXHiN0DAbDkrAaN/CVnjC6GlsxmCrTM2CqTBsMC8tqi15byZRH1kXqI2QTWbJj2RUhFGeqMm1Cpg0Gw5JhMv4XjvLADKD0vfuB7hV9j415zWAwGFYhqzEwA4zQMRgMhlXJagzMACN0DAaDYVWyGgMzwAgdg8FgWJWs9Mi66TCBBAaDwbBKWY2BGUbTMRgMBsOSYYSOwWAwGJYMI3QMBoPBsGQYoWMwGAyGJcMIHYPBYDAsGUsudESkR0ReFJGDInLAH2sSkcdF5Jj/vbHs/Z8SkeMi8oqI3F42/nr/OMdF5AsiIv54WES+448/IyJbyubc45/jmIjcs4SXbTCsCVZjgUnDymK5NJ23KKWuKysI90ngCaXUDuAJ/3dE5Grg/cBu4A7gSyJi+3O+DHwY2OF/3eGPfwgYU0ptB/4G+Jx/rCbgM8AbgBuBz5QLN4PBMDOrsfOnYeWxUsxrdwJf93/+OvCesvFvK6VySqmTwHHgRhFpB+qUUk8pXSb7GxVzise6H3irrwXdDjyulBpVSo0Bj3NeUBkMhllYjZ0/DSuP5RA6CviBiDwnIh/2x9YrpfoB/O/r/PEO4EzZ3F5/rMP/uXJ8yhyllAMkgOYZjmUwGObAai0waVhZLEdFgjcppc6KyDrgcRGZ6TFJqoypGcYvds75E2pB+GGATZs2zbA0g+Hyon5TPZmxTKmEPqyOApOGlcWSazpKqbP+90Hgu2j/yjnfZIb/fdB/ey+wsWx6J3DWH++sMj5ljogEgHpgdIZjVa7vK0qpPUqpPa2trRd/oQbDGmO1Fpg0rCyWVOiISExEaos/A28HDgMPA8VosnuAh/yfHwbe70ekbUUHDPzcN8FNiMhNvr/mAxVzise6G3jS9/s8BrxdRBr9AIK3+2MGg2EOrNYCk6uFyyUycKnNa+uB7/rRzQHgH5RSj4rIs8B9IvIh4DTwXgCl1BERuQ94CXCAjyqlXP9YHwG+BkSBR/wvgL8Hvikix9Eazvv9Y42KyL3As/77/kQpNbqYF2swrDVWY4HJ1UB56+nyyMC1KNRFKwGGauzZs0cdOHBguZdhMBjWOF37ui7wlxV/37tv7/It7CIRkefKUmKmsFJCpg0Gg+Gy5XKKDDRCx2AwGJaZ1dp6+mIwQsdgMBiWmcspMtB0DjUYDKuegUMDdD/QTeJ0gvpN9ey6a9eqcsAXIwPLr+H6D12/qq5hrhihYzBcZqz2DbqStRL5dblEBhrzmsFwGbEWi3aamnCrCyN0DIbLiLW4QV9OkV9rASN0DIbLiLW4QV9OkV9rAePTMRguIxaraOdy+ol23bWLp/7yKUAL0GwiS3Ysy/Ufun5Jzm+YH0bTMRguIxYjNHe5/USmJtzqwmg6BsNlxGKE5pb7iYDS9+4Hupds458t8mutReytZozQMRguMxY6NDdxOkFdZ92UsZXkJ1orIdVrBSN0DIY1wnI9za/05m6LqYkZDWr+GJ+OwbAGWE6/ykov4VKM2EudS9HT1UP3Q90MHByg/2D/JR13uX1ZqxUjdAyGNcBy5t+sdEd+/aZ6Rl8d5czPzlDIFAjXhckmsoyfHL8kAbEWc56WAmNeMxjWAMvtV1nJJVx23bWLB3/zQRAIRAI4WQdRQsvulksysS33PV+tGKFjMKwBVrpfZTlpu7aN+q315MZyZJNZIvUR2q9vJ7YudkkCYqnv+VrxHxmhYzCsAVZSguRK3Bzbr2uv2pmzmoCY6/qX8p6vpQg849MxGNYAF+NXGTg0QNe+Lh764EN07etaEAf4SnWuzzXYYT7rX0pf1lryHxlNx2BYI8zHr7JYT84rIVG0GnNNip3v+pfKl7WW/EdG6BgMlyGLJRxW8uY4FwGxUte/lnx2xrxmMFyGlFebLuav9PzYz2G5BFPYaq/4vFLXv9JzoeaDEToGw2VIcXNNnUuV8lcCoQB2yL4kH8xK2Rwv1l+1UtZfSdE86OZcXnn4FU7tP0UwFlzWNV0s8xY6otkoIm8UkdhiLMpgMCwuxc21/xf92GEbADfn0n59+yU5qFdCouilBDOshPXPRGGywOY3b+bKd115yQ8Iy8W8fDoi8jvAfwLaAAXcAPxCRB4A9iulPr/gKzQYDAtOcXN98AMPAhBp0Lkr8bY4ylOX5MNY7kTRS/VXLff6p2OlBmnMlzkLHRH5/wP3Ap8DfgQ8WfZyF/AbwOcXcG0Gg+ESmC3fpO3aNna9Z9eyOqgXI6fnYoIBVmJuUSUrNchhvszHvPZR4I+UUp8BflLx2ivAzrkeSERsEXleRL7n/94kIo+LyDH/e2PZez8lIsdF5BURub1s/PUi8qL/2hdERPzxsIh8xx9/RkS2lM25xz/HMRG5Zx7XbjCsKuZqYlpOH8Zi5fTMNxhgpeYWVbJSgxzmy3yEThvw3DSveUBkmteq8bvAy2W/fxJ4Qim1A3jC/x0RuRp4P7AbuAP4kojY/pwvAx8Gdvhfd/jjHwLGlFLbgb9Ba2aISBPwGeANwI3AZ8qFm8Gw2qh0lh+5/0jp90c//iiu486aTLicPozFSnicryBdLYmXKzXIYb7Mx6dzHHgzWihUcivw0lwOIiKdwK8AnwX+vT98J7DX//nraHPdH/jj31ZK5YCTInIcuFFEeoA6pdRT/jG/AbwHeMSfs88/1v3AF30t6HbgcaXUqD/ncbSg+se5rNtgmAsLaaaZ6VgDhwZ48j8+SXoojZtz6Xu2j8zfZdj85s00b2+m9+leMqMZInUR4m1xYHpTzHL5MBbLXDTf7qirxWy1GF1fl4P5CJ3PozWNPHozB1gnIh9CC49/M4/j/AegtmxsvVKqH0Ap1S8i6/zxDuDpsvf1+mMF/+fK8eKcM/6xHBFJAM3l41XmlBCRD6M1KDZt2jTHSzKsJhbLfr+QWf6zHevAlw4w+uoo4bow4fowY8fHKEwWGDk6QsvOFmLrYmTGMwx3D5eEzkozxSxmwuN8BOlqSrxcqUEO82HO5jWl1P8A/hCtgRzxh78P/Fdgn1LqH2Y7hoj8KjColJrOTHfBlGpLmWH8YuecH1DqK0qpPUqpPa2trXNcpmG1sJj2+4U008x2rN6newnXhglGg4gInusRrAky0TsBQMtVLShPkR5MrxhTTKU5sPWa1hVhLlorZqvVwrxCppVS/0VE/jtwM9ACjAJPKaXmqoe+CXi3iLwT7QOqE5H/CZwTkXZfy2kHBv339wIby+Z3Amf98c4q4+VzekUkANT76+zlvAmvOKdrjus2rBEWM+x0Ic00/Qf7yY5lySVzROojtFzVQqy1rBS/gCp7ZgpEAuQzeSzRz5Hx9XHWv2Y9E30TJHuTU0wxyxGpVU1zO/rwUXa+eydDh4cWzVw0l2tdK2ar1cK8a68ppSaAH1zMyZRSnwI+BSAie4FPKKX+lYj8F+Ae4M/97w/5Ux4G/kFE/hrYgA4Y+LlSyhWRCRG5CXgG+ADwt2Vz7gGeAu4GnlRKKRF5DPizsuCBtxfXYrh8WEz7/UKZaQYODTB+chxEr62QKXDmZ2dYd806mnc0A9B5Uyc9XT2ICIFIgFBtiMxohvhmnWeTTWSxbIs7vnDHlM1zuUrkTyfshw4PsXff3kU553yudS2YrS6WpX4ImU+ezmeBFqXUv63y2n8HhpRSn77Idfw5cJ/vHzoNvBdAKXVERO5DByk4wEeVUq4/5yPA14AoOoDgEX/874Fv+kEHo+joN5RSoyJyL/Cs/74/KQYVGC4fFtN+P9f+KrP9k3c/0E3r7laGjwzjZB3d7TLnMHR4iDf9wZsA2PORPSR7k6QH02QTWYI1Qdqub6P1qtaSZrPplk10P9DNM59/pnSe5UowXApnfeV9TQ2k1kQy5WKyHA8h89F0fgP4o2le+wnwJ8CchY5SqgvfvKWUGgHeOs37PouOdKscPwBcU2U8iy+0qrz2VeCrc12jYe2xmI235mKmmcs/eeJ0gubtzUTqIgx3D5NNZInWRwk3hqccq66jjuSZJCJC++va2fORPVOi24rnsYIWxx45xqH/eQixhS17t0xZ91JEai22s77afT3xwxNsfevWKe9biVFpy8lyPITMR+hsAPqmee2s/7rBsKJZbPt9NTNN+RP42Ikxajtqp/yTTw5P8ujHH6VxWyP1m+qxQzbZRJZ4W7wUeVa+YZdvsFe++8qS4CynuJm4eZfep3tRnsLJOGTGMrz0Ty+x9batrLtaB4kuRaTWYnfZrLZ5RpujDBwcoLb9fKDsSo1KWy6WI1x8PkJnAHgdugROJa8DhhZkRQbDIrOU9vvKJ/Dep3uZHJ0kXBcG4OyzZxl9dRQrYNF8ZTOZsQzJvqSOq7yi+gY9l6fT4mZy7PvHmOifID+Rx7ItrJCFchU9T/YQbYwSiASWpK31Ygv7aptn23VtnHziJJmxzLK38F6pLEe4+HyEzn3AH4lIt1Lq/xQH/Ui0TwNfWejFGQyrnQNfOsDwK8O4eZdIfYRgTRDXcTn77Fncgsvk8CR2yMYKWPQ93UfnzZ00bmvEzetqAtU26Lk8ndZvqmfk2AhjJ8ZwCy5WwEJ5CgoQ3xQnM5yh7+d97Lpz15JFai2msK+2eQYiAbb98rZp76Nh8TXQasxH6PwRcB3wv0VkBOgH2oEmdDTbxQYRGAxrkoFDA5z44QmiTVHCdWEKmQK5ZA6AibEJGrY24OZdxBLi7XEs22K4e5jNt24m2ZucNqprLk+nu+7axYO/+SBW0KKQKWAFLB3pFg2QT+ZpvbqVpiuaFi1ybKmZbvNcSW0JViLLES4+Z6HjO+jf7hfdfAs6y38EXTPt8UVan8Gwaul+oJtosxYMIkIwqptu5dPa1OXlPUKxEMF4kHA8jFI61Hk280b5ButkHQYODpAZybDtbdsYODRQ0ijqt9YjAWHopSGUpwjVhrBsCyfjUNdZt2p9G9NF/5lcm4tjqcPFLyZP5zHgsUVYi8GwpkicTtB2XRu9T+uKTYFIAKUUbtZl16/twg7ZuHm31LkThQ4imMW8UdxgD3z5ACefOEm0OcrWt27FDttTIuHar2unYXMDjdsaOf2T03iuh7IUtR21WLa1KjPuZ4v+W2tCZjW0XJgvM5bBEZGa8p9n+1r85RoMq4f6TfUEIgE2vnEjwWiQXDKHiLDtl7ex5yN7yI5lsUM2nTfp4hqZ0Qzt17fPySTUdm0b8fVxdvzKDna8Ywe17bUXlMoplnep3VDLlXdeSeO2RkI1ITb90qZVa3ZaLRWhF4LV0nJhvsym6UyIyM1KqZ8DKarUKqvAnuV1g+GyoWgGizRG2Hzr5pKfoZhPU24O2vGOHfN+ip0toGAhzrFUzPWJfrVUhF4I1kqn0EpmEzofBF71f/7tRV6LwbCmmM3PcKnmoLkEFMx2jvmYb1ZCde7VVBH6UlmrAnZGoaOU+jqAiATR/XROKqXOzjTHYDCcZzH9DJca7jqfzX4hyqVMJ7Tm80S/HCG+y8VaFbBzbW3gAk8CVy3iWgwGwzy41K6f8/GPzPW9le0Liv6HmfwTidMJIvVTGw/P1HCueM0DhwY498I5ssks3Q90T+vrmG5NK5212nJhTtFrSilPRI4B6xd5PQaDYR5cjCZV1Dhe/NaLxDfEab26lfj6mbuLzsXUM5M2VC60UudSDL88THowzaMff5Sa1hpe/u7LTA5NAlDbWUvzzmZadrZMe80AYyfGaNjcQKQ+Mq3mtVxVtReCtRoGPp+Q6T8EPiciLyqlXlysBRkMa5GVEvpavgnXbqglk8hw5mdn2PjGjcTXx6c138zF1FPNTJYe0oJlom+C+IY4sfUxRl4ZIRAJEG2JMn5qnKGXh8in8gTjQeyAzdirY6T6U1x11/SGlbma5Fa7M34thoHPR+j8J3RC6EER6QPOURHNppS6cQHXZjCsCRa6jfWlCK/yTbjlqhZ6n+pFiWLopaELcoTKz2WHbZJnktPWg4MLtaHUQIqhw0N4jlcScCPdI9SsqyEYDZZykwCdJBsN4mQdQjUhIk0RTv7w5JQGb+XXWjxXaiBVqsQdqYsQbgxPud616oxfzcy5XTW6RfX3gG8ATwCH/bHyL4PBUMFC5ZYsRN5Guf8k3han8+ZOovVRUmdTU3xCleeyQzYIuDl3Wv9R/aZ6sonz1a6Hu4fBgti6GC1XtSBKcAoOuUSOQqaAm3UJhAMoFGIJDVsaaNnVQuN2XXvuxA9PTHut9Zvq6X22l+7vdjN4ZFAXSu1PkjiZmHI/KtcEa8MZv5qZTxmc31rEdRgMa5ZZW0/PkYUwFVWayeJtceywzZa3bJlSh63qubbp73v37S1pQeUN4iojy9KDaSQgtFzVQny9FnCZRIbceI7GbY20X9/OcPcwmfEMYkvp3E7WIZ/KU9dZN+21tl7TytP/9WkkoMsLORktzJpubppyPy6naLfVwqxCR0SuBf4NsAVd5PO7SqlHZpxkMFymVJq/Wq9pnbX19FyP1X+w/wLhMl9T0Vw24YFDA3Q/1I1SimhDtCQ0iueayVxY7viOrYtR26F72fR09ZBNZAnXhgmEAqx/7Xp9/mSW0eOjWEGL/GQeQchN5PAcD8/x6H6ou6qgHjo8RKQxgnIV+VQe5SoQGH5pWGtlPrM541eKr+1yQpSavsiAiNwGPIquRnAU2Ai0Af9BKfVXS7LCZWTPnj3qwIEDy70MwyqhfDMubuinuk4R2xBj/NVxCukCrqO7rUfqI7z3/vfOmIhZeayerp4LhFVRa5lPteiZNtrieYdfGdb+FtGax8Y3bsQO2SWNozKooNo6Bg4N8OQfPsno8VFCtaGSQKlpqWHdNetwc25JMJ984qSuUaegaUcT5144R6AmQKQ+gpN1cLJO6dr37tvLQx98iNFXR8mMZpgcnEQCgthCIVWgfmM97/nme2YVHtXusalMvTCIyHNKqT3VXptN0/lj4MfAe5RSaRGxgL8BPiMif6OU8hZ4rQbDqqWaSSo7kSV1MEU+nQcFgVAAbJgYmOBHf/Qj2q9rr/p0Xe1YrbtbddO346M4OYdAOEBsXYzrPzs/U9FMEVHF87Zf307vU73YERs7bNP/i35armzh+g9dzzOff2ZOzvm2a9uo66gjPZgu9RNqf107dti+QEA172wmvj5e6q7auL2RVF8KJ+sQiARwcg5Dh4d40x+8CdBmQifnaL+RgB2wcbIOVtCi9ZrWOZkcV1tk21rRymYLJNgN/LVSKg06Xwf4LBAHNi/y2gyGVUVlkmPqXIrseJbJoUk8x0MQCjndU8creJw7eI7ho8NTHOTFRMYXv/Ui5w6eIzWQInUuRU9XD2cPnCU5kKQwWUDwfSCzVUOcI5XnBei8uZNgNIiX93BzbkkDmI9z3s27bL99O7vu3MWWvVuIt8Wnze8ZOTbC6KujDBwcoP9AP9H10VKh1Gh9lPqt9VN8NZZtEYwFsUO2vicibL51M01XNM3J5DifpNTlZi0V/5xN02lA98wpp/h7I3ByoRdkMKw2ik+g/b/oZ/DIIO2vaye+Ps7ZZ8/iOR5KKd3HxnHwsh5YUNNaQz6dZ/jIMC27W0qRbE/95VO4jksulSPx8wRnnj6DFbZo2NRAIV0gFA0hltB5cyfx9XEyY5nSk/nFPgmXm5niG+JkEhl6n+ql8+ZOtuzdUjKdXYxzvjJwITWQov/5fty8S9e+LnbdtYvuB7rxXI/Bw4MEIgHCDWHy6TyDLwxy1V1Xla6z3JxX9NUkTidID6aJr4ujUIweHyVxOkH769qn/ZyK98cO22QT2VVRZma1aWUzMZeQ6a0icnXxC7jaH99WPu6/ZjBcVpQ/gXa8oYNcMkdPVw8T/ROMnxonEAoQadBP08rx1RIPnLSDHbSxIzapvhT9B/t59OOPcvqfT/PqY6+Sm9AdRj3Xw510SZ5JliK6ApEAwy8PA3rT7z/Yz/f+r+9x3133ceyRY1hBa15PwuUbWmxdjFR/isSZBMcfPc7w0eELSq/Mp/xOeSmXif4Jen7cQy6Zo+PGjtIa+w/2k+xNEogECEaD1LTUYFkWbsFl6KWhacu/tF3bxh1fuIPGKxrJp/OIJdhBm1wyR/JMcsq1V9MUkmeSjJ0YWxVlZlaTVjYbcwmZ/odpxu/jvHIv/s+mtYHhsqLyCXTLm7fQ/3w/fT/vw7Itos3aRDR2cgwn45Tmea6Hk3PwHI/EmQTh8TBuwcVzPNysi1KKUCxENpFFeQo8dOfPgEUgEiiZt0ZfHWX85Dip/hTRJr2G3qd7ad7ZTOJ0ggfveZBdd+6aUespT7QcPTpKbF2MbEILiuEjw9zy6VsumDvXTPny6LHuB7sJ14VLmmCRoZeGGD81jthCMKKFTk1rDZmRDIMvDpJL5ko9h6odfzq/UbkWUFVTuELnHUUboyu+zMxaKv45m9B5y5KswmBYpVRmvMfb4my/fTvJ3iT2G2yOP3acyaFJsuNZKIbdWOiSL0GbZG8S0P6Tib4J0kNpFNoc5zkewUgQT3kEY0FCtSHcrIuTc0r1xoYOD9G6u5Xhl4cJ14URESZHJjn+yHHsqI1lWYwcG5mxAkJxQxvuHsaO2ESiEYI1QZq2NbH+tesZOjwEd0+dMx9TXlFAFe+VWFNzctLn0vqRVYGTdxjrGSNUEyIQCVC/qZ7tt28nm8hOew1Fv1H5cZWnpmgB01UmSPYm5xX5t1yspXyjGc1rSqkfz+drqRZtMKwUZnKqb33bVvLpPNlkFhHR/20CdtjWPp68g1twabm6haYrmmi5qkWb0/KuTpBM51GWwgpYKBS1bbW07G4BRempvX5rPc3bm0uhxflUnlR/Cs/xCIQCWAGLwcODeK43bQWEogksPZjGDtsUMgWcrEPLVS1VTTgX69Sudq8GDg4Q3xBny1u2IAhewSMQDlDIFghEArS/rn3WqtZjJ8Z46f6X6OnqITWQmvIZzOVzKj/WSq1GfakVxVcS86m9BoDvu3k9Omfnq0qpARHZDpxTSk3MMjcC7AfC/rnvV0p9RkSagO+gE1B7gPcppcb8OZ8CPoRur/BxpdRj/vjrga8BUeD7wO8qpZSIhNGlel6PDnr4daVUjz/nHnQNOYA/LfYLMhgulpmeQLsf6Ca2Lqb9DAndqhobnciooGlbE+3XtxNv045yFITrwhTSBbyCh9iCJRaBmgDheJhwY5iWnS380id/qbTZdO3rIjOWoWWXrqOW7E/i5lwUilwiV2qZnexNEghX/3cvbmiPfvxR0oNpYutiJRNYZixzgQnnYp3a1e5VZiTD1rdupba9lprmGl1HbTxL4nSCzbdunmKGmy7qrbajlsnRSTLjunhp6zWt2AF7ihbQek0r++/dT34ir82Vou/1rZ++dcqxVnI16rVS/HPOtddEJC4i96Frrv0P4F5gg//ynwGfmcNhcsBtSqnXAtcBd4jITcAngSeUUjvQdd0+6Z/zauD96NDtO4AviUjRb/Rl4MPADv/rDn/8Q8CYUmo7Oqfoc/6xmvw1vgG4EZ1r1DjX6zcYqjHTE2jidAI7YNO0vYnaTbUopfDyHspTBKIBWq5sYc/v7ClpGv3P9xNfF6d5ZzORxgjhujBW0KJuQx3vvf+9/MaDv8HefXunbDzFuXbYpnFHI7lErpQPFKgJkBnJ4Dkek0OTM9r/i075DXs2sP6164m1xqZ1rF+sU7vavdr2tm0EIloYxtvibNm7hc1v3sz6a9eXxotMV9W6eUczm960iWhDFM/xSJ1NTREWA4cGOPrwUWo7a8mntOZZmCxQ21HL0YePlkyFC1EfzzA789F0/hp4I/BW4KdAua76feAT/te0KF3+IOX/GvS/FHAnsNcf/zrQBfyBP/5tpVQOOCkix4EbRaQHqFNKPQUgIt8A3gM84s/Z5x/rfuCLIiLA7cDjSqlRf87jaEH1j/O4BwbDBRQ3twNfPkD3Q9ph3nlTJ3ZIJ1ZOjkySG80RqAlQmCygHIWTcdj57p2luTd/4mYevOdBlFLUNNew6Zc2EV8fR3mKZG9yRn9J0VF/6seniLXEsKM2hVRB+4WUR7I3SW1b7axRWcVjHfjSAV55+BUQqjrwL8WpXfm0XtQwYKqmeMPHbuDow0cvGJ+uqnV8fXza+1UUKInTCRq2NpQqXDuTTkmwmGrU51nsJNT5CJ270CasH5VpG0VOMcdkUX/uc8B24L8ppZ4RkfVKqX4ApVS/iKzz394BPF02vdcfK/g/V44X55zxj+WISALdkqE0XmVO+fo+jNag2LRp01wuyXCZU17uRWwhN5bj8D8eJlwfJtocJT2QRmwdzksEok1RNtywYYqDvu3aNnbdueuiNvNyR70VtOh9updAJEA+lcdLeyhHVY1Am47CZIHNb95c2uwrzUxFM9nk8CTJ3iSTQ5PYQZtbPn3LvO/dbLXRfvRHP2L85DiC0LC1gZGjI6XX5ir8igIlm8gSrtOtD4oRgEXBspaiwy6FpTAzzkfoRLkwUbRILdrnMitKKRe4TkQagO+KyDUzvF2qjKkZxi92Tvn6vgJ8BXTttRnWZrhMqXwSTA2kSA+msQJWqQ5YMB7EyTsU0gWCMd0nRrmKhi0NbLhhwwVVpgcODZAaSHHihyeINkdpu65Nb4zziFAqbpwb37iR4ZeHsYM28fVx2l/Xzu67d8/pGHPx17Rd28bOd+9k/7378QoesdYY8Y44Rx8+SvPO5nlvTtV8FQOHBnjmvz7DRN+EjsqzhYn+CR77vccA2H337jlHdBXvS7HgarFvT3FO8Wl+rUSHXQpLkYQ6H6HzLPABdAHQSu4GfjafEyulxkWkC23iOici7b6W0w4M+m/rRQcsFOkEzvrjnVXGy+f0ikgAqAdG/fG9FXO65rNmg6H4JOi52mzV+3QvEwMTOocGCwkIKMin87h5VwuazQ1sfNPGCwpkFp+iy58ut751KwMHBzj5xEm2/fK2eT1hFjfOSGOEzbduLm2cez5Ste5iVeZqZiqGaqfOpnRUmEB8Q7zq5nQx5pruB7oZOzFWShgFEBHcgsuzX3yW3XfvnnM75+J9qe2o5dyL53ByDnjQeEVjSbDMdKzKZnYoHaa9muufTcdSmBnn2zn0hyLyQ+B/obWEd4rI/40WOrfOdgARaQUKvsCJAm9DO/ofBu4B/tz//pA/5WHgH0Tkr9FBCzuAnyulXBGZ8IMQnkELw78tm3MP8JS/rif9qLbHgD8rCx54O/CpeVy/YQ0z142xsmRLTWsNqXMpUn0pnREfsvFcD7EEK2BhBS3S59KMvTpWtevmwKGBKVFjLbta2PGOHReUnpkLc904Z7q+uZqZ+g/2M35inEA0oCPuMgWGDg9RmCxccF/nY64prvPFb71IZjRDqD5Ues0KWLiOy0Tf+SDZuUR0ld+XfDpPbjxHuEFHApbfh+k0ruL6raBFT1cPAJtv3bwiI9wulaUwM86nids/i8hb0YLhi2hz1R+jfS5vU0o9O4fDtANf9/06FnCfUup7IvIUcJ+IfAg4DbzXP+cRP2LuJcABPuqb5wA+wvmQ6Uf8L4C/B77pBx2MoqPfUEqNisi9aI0N4E+KQQWGy5vKjeXYI8c49D8Pse1t29j6tq1TWiYXG7IVn8DzqTxKKUQEz/PwMjoD1A7aBMIBapprCNYEqdtYd0HmO+haa+nBNNGWKIVMgd6nemna2cTk4CQTZ/XmWk1AVOvbU77ON/zeGy5oVzCXjX+u/prceE4nufpaSDAaxMk5eryM+ZhrKmvAJc8myY/nsRotAuFAqWhqsUfPfLjYcOPy9fd09ZR8QiOvjLBl75Zpr2W1shRmxnnl6Silfgrc4mspjcC4UmpyHvMPAResXik1go6Kqzbns+jK1pXjB4AL/EFKqSy+0Kry2leBr851vYbLg+LG4ubdkhM+2hTl9E9P8+oPXqXz5k6armhi5NgIfU/1kU/niTZHibXGmBzWm3KgRUem5Sd0CwPXcQkFQrg5l41v3Iibcy/IfO/a10WkMUJsXazkayhMFji9/zSx9pguvllFQFQKkeGjw7zwjRdK66ycU359p/afIpvIYods9t+7n3W7110guMbPjDPy0gjBeJD6zvqq/ppwQ5jJ0UkKGZ3E6WQdlKcIN4SnXON8zDXlG3zr1br53UT/BJPDk1gBS4eCx7R2+dAHH1oS81b5+ssDEYqJpmstwm2uJstLYd7JoQBKqQyQWbBVGAzLSHFjObX/VEmDUUoxdmIMCQgnnzjJyCsjZJNZQvUh8uk8+ck8hdM6idMO2USbo9gRm0K6oJMPXfAKWuvJTeg21V37uug/2F8y7yROJei4sYOadTWc+vEplKdw874ir6D16tYLNINq5rjU2RShuhATfRM072guzTnw5QPE18d58VsvEqoLkZvIEW2MEq4Lkx5Oc+z/HGO4exjLtuh7to/M32XY/ObNiCfE2mOgoOVqXQGh//n+KXXc2q9rJxTT5yxGgTVtb7qgG+p8zDWVIdAdN3Vw/NHjOJO6Zl2wJqgTZoPWkiVwlq+/GIgAlPKU1mKE22Inoc4odETkL+ZxLKWU+oNLXI/BsOQUN5ZsIotYwnjPONnxLLmU3qSxINWfwsk51G2uI9ocxc26FLIFnJyDk3N0pWJHnY+RtHXo8cS5CVLfSxFvi9NwRQOpvhRYMDk6CQpe/cGr2EGb2LoYuWROayFBu9TUDM4/TRc1nEpzXDaRxfM8kqd1HbeWq1pID6fpeaKHeFscJ++Q7kljiXW+PtvgJMpTZEez1LTVkHg1gZN3tAmpNqznZR1O/vgkudFcyU9VrOO28907GTsxdr7ttG+GqcwFupQ2CJODkzRuayTaEGXL3i30dPWQGc+QOpuiZWfLkpT333XXLp78wyfpHewlM54hO5IlVBviirdfUUqevdwi3C6V2TSdqmaqaVDohE6DYVVR3Bg9x2Pi7ARWwNIaTMAmP5En0hjBcz3ssE16IE3zjmZqWms4vf+01ky886VtijU+rICFl/d0aZvaMIGaAOeeP0e0JUqsSZvTPNcj359HbKFlVwvBmiBuziVUF2Jy6LzVuvg0XTQ/VZrj0kNprKA+8eCRQc69eA634BKsCVLTWoPYQqo/RTAe1KYq2yKfyhOqDVHIFkj1pfBcDytoaZ+MB1bQwg7bjJ8cJ1IX0W2rJx2OP3Kc2o5agrHgFDOMHbYJ1gR55vPPTDF7zcVcU+pHdLCfxMkErde00nRFUykMvWVXS+k+hOvCU2qoLYl5y0+cCEVDBNp1F9PUuRTt17Wv2KrUK5kZhY5SautSLcRgWC6KG+M/vf+fKKQLiCW4jquDAxyPXCJHqC6Ek3FwMy6p/hTjPeOE6kMEogFCtSGGu4e1WU3pxEPlKuyIjZf3yE/myY3lSgU5Yy0xApGA7ojZFCWbzJJLahPcpls2MfzKMOnBtNZEyjSDYpvolqtaOPOzMxQmC4yfGsfJOpCFUFyvZ3J4EjfnUttRi4hQ01xDuiFNfjJPfiJPcEuQUCwEFqiCwopY2CEbt+AillDTWqM1IVFYYqE8RX4iXyrLMzkyyYnHT7DnI3vYu2/vFB9TbJ0un/P9j30fZ9Ihl8hR21HLDR+7oWo15/K5bde2EawJMnh4kHw6T2yd9mvF285rfJnxDNGG6qa6xcik736gm8YrGtmwZ0NprKiNrYbq1CuROddeMxjWOm7epXF7I6H6EMpVKFcRjOvorELKbzONp008AynSA2kArJCF2HK+q5QCt+DiZl1dWLI+TCFXwM27ujYalJITI40RGjY3lNo5r9u9jvWvWU9sXaxUn2znu3dqTeD5fo4/dhwUNF/ZzMTZCQppP0TZ1ut3Jh0sS+cLFZLnw5frNtZh2RZN25vYfOtmwg1hcuM5CpkC2QltVvQcDytkkUvkKEwWyCVyWEELN+cSrgsTrAmWtMBoc7RUl6yybtngS4P0PdPH4OFBnJzDRP8EP/wPP+TI/UcuuOeVc1t2trBl7xbar2vnji/cgR2wS03WajtqySfzxDfEL2i6tljtnNdS87SVwrwCCfwaZm8CdgKRyteVUl9aoHUZDEtK9wPdRJv1E3QhVaCmuYbMeIbCREELE78XjhWxQCiZs/KpPPlUnmA0SK7gC5S8Tj7EgtrOWmLNMcZOjum2BUmXvmf7wILGLY1EGiOl3jhFn4dlW9zxhTtKgQNFTaDjxg5O7T9Fz497UChdODQcwHVcHVLsm8jC4TCBfIB8Jl+KLvNcT1cnuL6dgUMDWJZFTUsN+WQeJ6+byYXrwihPkUvliDRHCNeGyaVyZEeyWEELpRRO1sGyLdqua6P/YD9d+7p48VsvEt8Qp/XqViaHJzn949PaB+T39lGeIlAT4Kd//lN23717ikbS/3w/HTdOrUZV3NQrTXPNO5rZ9Wu7poSGF81bXfu68FyPcy+cKwU21HbUXrK/x5THWXjmLHREZD26AvTVTC0rU14qxggdw6okcTpB23Vt9D7dq1sf21Iyl5XjFTwiTRHqO+sZOTZCZiyDFbB0AzELxJNS6fxAOIDKq/PVCRxVilALhAMopYjURXjtPa+9YCMdOTrCox9/lHMvniMQCbDxjRtZt3sdW/Zuof8X/Zw7dI5oU5R4e5zk6STK043fMqMZQvGQPkckUKowbQUtbvuz29h992669nXRsLkBN+fSd6CPsRNjui6cBbXraxEldN7cCQI9XT0UwlpjKkwWsCyLTbdsIp/KM35ynIbNDYTqQgy/MszAwQHcvIvneiCUrtUO2rg5l5GjIxy5/whHHz5aCvcePDLIqf2n2LJ3SylwonxTrxpJVdFQDnSy6tiJMYLRYClZ9dyL58in87N+9jOZ5Ux5nIVnPprOXwEJdImZM+gWAeeAf4WuCPArC746g2GeXKxdv7xu2fFHjpMeTJcasIfjYXJJrcUoT5E+mya8XedriAh22MbJODo6rCmsNYecR01bDV7OY/zEOEoUEhQCQZ3B7+ZcgpEgjVc0MnR4aIp/4Mj9R/jhf/ghVtCikCmQT+c5+r+PkhnPsPlNm9l++3ZGj4/qJEkPAlHdwqBcQEYaIkQaIuQSOdqub6OmuYbj3z/O0OEh+g/203ZtG2IJV/7qlaQGUgy/PEz/8/1E66O0XNVS8qNsvnUzp358Cs/xqOusK9WEO9V1itbdrbg5V/eocZRut13QwRN4IAHRfjHXQzwh1Bzi2S8+y/rXri9pDu3Xt9Pz4x76f9Ff6hB6MZt6bjyHWDJrsmolsyXOLkXeyuXGfITOm4HfBfr930UpdRpdWsZCazm3L/D6DIY5M90GsvPdO6doEtUEUXndsituv4Ij3zmCk3VKpimxBKUUSincnA4mEEuoWVeDJRZuxKWQKZAZPb/5p86kqNtcp/09AEo7++2gjVK6BH81/8CzX3wWK2iRHdemNmzwHI/en/XSvL0ZO2TTuLWRyaFJ8hN5CtnCFIGTTWbxCtqcVrepjpGjI9Tu1f18jj1yjNFjo4y9OsamN20i3qYd9XbYBmGKQABtPoy3xUs+oGLUVrFj6an9p0q9f4a6hwAtbFRB+ZesUI7CDtp03tzJ4AuDbL71fEH6eFuczbdupu+ZPpK9yYsq3QM6WTUzmiE9nCafypcCQiKNF3gBptD9QDeu404xy1XWkFsrzdNWCvMJJGgAhpRSHpAE1pW99jN0rx2DYdmo1ojLdVx+cu9PZnUwlzcYS51L6URES3CzrjaD+SVyAMQWnLxDTUsNm960idxEjtxErlRIshhM4LkeiZMJnKyDl/cIxULaDIfWkKC6f2Cib4JCpoBuPwVORvtcvIJH/y90GZ43ffJN2EGbQr6Ak3am3ggHsGCif4Kzz54ln8prU9rTfYAuzDnRP0HPj3uY6J8oOeRv+NgNZMeyJcf9yLERep/qJb4hTtu1bTonpy5SSg7NJrI6R8jxmBye1KHWIYtwbVgXPhXABcu22HjLRuo66qjtqL2gbXQgEmDXe3Zx51fvLDWpm29gQPt17cQ74mSGM7qyd01Qa3qjuRmDCfoP9uuacZnClBpy/Qf7p51juDTmI3ROomunARwB/mXZa+9C1zkzGJacYm/7F7/1IgMHB0idS5VeS/WlcAvurB0hK3NF1l+/nviGuG4v7WgNxwpYWLZFMKZDjhu3N7Ju9zqCsaB+oi9UOIAUKFv7hZTSDvpsMkshU8DNuURbolUTKms7anVkWbqAILqDptK5QNmxLDd/4mZ2372blqtbsC2toVghS/83+0qVk9GtFDzHw3M9zh44y+TwJIlTCR3+HA8TrgvT86Me/ZSfzDJ0eIid795Z6uw50TdBx00dtOxs0QI45zL8yjAP3vMgqYEUYyfG8ByP8VPjFLI6YCEQCuDkdWReqCZEpDHCznfvZN3V68iOZdnxrh30dPVw5L4j9Pyoh+Gjw1XvwXw7ee66axepsylibTFar24l3hYnEAnQek3rjN0/y2vIifjmOYtZzXKGi2c+5rX/g67MfB/wp8BDItKLbqi2CZMYalgGyk1qtRtqySQynPnZGTa+cSPx9XESvQk8x6P7oW4i9RFarmqp2sumeIzcWA4lioneCTpv6tS1yHrGySfzYEO0OUrLjhYCsQAj3SO6rExtWcKizdTOUg4glJz7Xt5DRXS76s2/tJk9v7PnAtPNDR+7ge/+q++iUIitQ5kB7LB9vkwO+un+VNcpHdbtCyWldBCDcnVgQSAYwC24OlggYqNQOqLMVbrDaKZQqiqQGcvwwtdfoK7DrzU2nqX5Sl3WJjWQovepXuywNg0WS/x7rocq6N/rttbh5rXpMdoUZettW0HQArYxyqZbNnH04aOsu2Ydyd4k6aE02fFs1QZz8y2x33ZtGw1bG8iOnc95an9d+wWfdSVzrSE3Fxa74+ZaYT5Vpj9V9vMjIvIm4NfQodOPK6UemXaywbBIlD8Rt1zVQu9TvShRDL00RC6ZIzOaIbYuVjKdnPnZGdZds25KjbDyY2ST2q7vZB0mByfZ/b7dTPRP8NL/eom6Tdo/M3J8BCfjEGmOMHJ0hGBNULc0cDxtXgto7QgABVbYIhQLkXWyoKBhcwPv/uq7S+d+5vPPkJvIMX5yvJRMGe+Ik+5P4+ZcHXIcCxAIBxBbSo7uXXft4pm/fQZn0rlAy1JKEarXgi7Vl9J+Ic/DzbuI0sEPY8fHEBFe/IcXSxF3TtYhWBPUAnwow6s/eJXtt29nuHsYO6IbBkcbotrvcwUk+5Lsfv9uXZsukaWmuYaNb9xI6pwu/VOsNTc5OklPVw+1HbU072gu3f/MWGZKB9Uicw1VHjg0wIEvH6D36V5S/SlqWs+3+i4ef6bw5rnWkJuNpei4uVa4qIKfAH4rg7m0MzAYFpTp8jzibXE6b+5k+OVhJs5OICJ03NRB+my6FBTg5ByGDg/xpj94U+l45U/VxaKOgUiAiYEJerp6GDwyiJPVNdZyYzk8z8PJO6T6UkwOTtJ8dTNuzi1pGyUzV1Hu+GauSINfJHI8y8jRkVLo8PjpcU7/5LQ+f2OEwskC6XNpLSgnC1qY+QpOpDFSMjPtumsXodoQ2dGpPhKAQDygTWworJBO8Myn80hASgJYOQplKzLDmfOGdqU1k7qNdUSaIiROJTj909NYtlU6Tvvr2kkNpOh7to+R7hEdALC5ns6bOomvjzN8dJjEyQShWEgXTbWEzGiG3IQWPuG68AV15SqZS6hyeZvwUG2IaLNuHXH80eNc8fYr5tR5tXie2WrIzcbFdty8HLWji6pIICI1IvLvROS/icinRWTz7LMMhkun0sFsh2xO7T9V8uPE2+Ksv249r/mXr6FxWyOdN3TSeXOnTt5M5ojWR6nfWj/lH7t+U33JPFbTWsPo8VHOPH2G0WOjuo5ZziUYCzJ+cpz0SJrMcAZn0tFlbdJ5+n/eXwoQAM4HE4D+D7MptauONEaINkd59ovP6nYDvoO/2AAuP5Fn8pwu5Z+fyOMVdJJlIBoAD/LJPMm+JN0PdvO/7v5fOrS72J2quIQAUNCRZ4FggHA8TKQpov0+oqteu1lfivnfxJYp686OaK2lblNdqQ6ciLDxjRtBwcknT+rOnnGdbzT26hiv/vBVRo6NMHRkiNZrWpnomyAYDRJtiOr1KxBLGH55uHSrpku0LA/sKFZmqNQauh/oJj2ohXOoJkSsNUbdRm3i6/t5X9U5F3OeuXAxlQsWq4rCSme2KtN/BbxLKbWzbKwWreHsAMbQ7aB/X0RuVEodXczFGgyVT5TleR51G+vofaqX/ESe5p3N2safyJbCgoELTDZw/mk3PZRm4OBAqaKAZVnkkjkUipCE8HJ+WYLytGgXsHSBz2AsSGZId/yQgBCoCeiKBgVwcKjbVIdt27Rd18bxR46jlGLklRHdbVOBW5IA/nH9KDQ8yDpZmnc0I7ZwqusUjVc0kjiTKBUcFdsPOECb1gIhbYorhjMnziSwQzZOzjfFlWk2oPOPSigtJHOpHNmxLG7epfPmTpJnktghm3MHz5UqJzRsbqAwWSDZmyR5JsmZwhkatjbQdEUTQy8NlfrPBCLaPIhH1bpy1ZgtVDlxOoGTc6Zs9jXNNdgBm/br2+dcG20hQqIvpnLBxWpHq53ZNJ23AP+zYuwT6DI4/0Yp1YJuI90DfHrBV2cwVFD5RFnM80j1pzjx2Ak8x6NxeyOu49L7VC/9v+gvhQBX1urq2tfFQx98iO4Hutn57p2MHh3VrZD9MF+xtfYRCAbO1ziDUkh0+e/K1dFtlCk8qqB0fo+lwIPMcAYJConeBPl0nsHDg+eTUJl6PPwoaAmIjqDzFBMDE6QGUniOp/vjJHNaOCntQ3Iyji5KmtVJmm5OVyUIxUPUb6zXfYIK56slTDmvS2ntylWkh9OcO3iOyeFJatbXYIfsUlBAsaNp/Wa9oU6OTBKM6xDlYE2QzGiG0VdHS74x0EIs3h6n9ZrWKXXlLsXnUb+pnkA4UDpH8Tx22F7yMjW77to1Jdy8/G9tOi7Xum6z+XS2AM9VjP0L4CW/CydKqSFfI/rjhV+ewTCVak+UgUgAsYSGKxqmVCB2Mg5jr46RT+lunp03dXLzJ24GuMDp+8LXXyB9Lk0wphuFTQ5Nlkr8iy26LI5bKR18/Ciu7Gj2vObgKFzcUikdZSsUivGT45w7dK5UwfoCgVN5aMcXXKJwsy4uLvWb6xk6NIQVsHA9t1QXrrQ+yy84Ouly9tmz1LTWaD9Ke5zcRK5U3PMCLM5H3ilQooWlk3Fwc67ubdMYZevbtnL04aMkTid0BQILVEb7rYKRIC1XtzB0WJvYzr14rpS/1HhFI3bALtWVu1R23bWLs8+dZfT4qG4ZjpCbyNF0RdO8fTKXysVULrhc67rNJnQCQMlLKSJNwFXAf6t4Xw+wdvVBw4qhmoN57MQY2bEs+Uye3HiOmpYaQJvSvLzHle+6smTKgepmjd7BXgq5ArlUDi97fkP2CtqJP8VnU4WSj6SMUgQbfp6OX6pFKTXVnDUTntZyrJB25IsIbsElN5HTG62c16SKWAELz/NKvqTk2ST0QjAWBIGalppSxWs375aElSjRpsTGEG7G1VpeOIAqKIa7h9l862ZO/vgkqb6Urnrg6mg41HnzYjAe1FpMc5TmHc3k0/lSp9SWnS0L6ihvu7aN2z57Wyl6DQVb3rylahj6UjBfM93lWtdtNqFzFNiLLvQJ8Kv+98cq3rcOkxxqWAIqnyiL+SLBeBDP8XALLokzCSxbV0WONERKyYVAaV5dZ52uOdY9TDaRJXE6QT6V143XKlF+aZcyIVIenTYnPAjVhbS/ZNSvXDCzHDt/HsDLexS8Am2vb2PkyAie8lDeeeElAV1o1ApYBEIBnazqKpyCU/INuTmXQDRAeiiNHbCJtkR1yZjJArVttboTasah7TVtJE4lcAsuVsDCyTpkE1lGjo8w8vIIsbYYzTubSZ5J6koESmuEjdsaCcfDZMYziJIl6TfTdm0bv/rlX539jSuQy7Wu22xC54vA34lIPbq458fRlQl+UPG+twOHF355BsOFlD9Rdu3r0k/k0QAnfngC0E71zGiGQDTAhhvON98q2svrN9UzfHSY4SM690Qs0QED05jPrLBFMBwkl8+d1yiKddaLU4QpLRCqUawwUGIuQkvp6ymWxBk/No5TcM4HNViUfDrgCyelu4qWfB1+cqpX8LAClg6zHsuSHtTCp2lbE627W+n9WS/5Qp6x42OE68M4Iw6O62AFdJO3oSNDBGoCOqzcLzUjAQEFdtAmFAtRyBTA46KSKy9HLse6brN1Dv2aiLQDH0XXXvsF8FGlVMmrKiKtwJ0Yn45hGShqLUVN5uyzZ0vO3EA0wOTQJKkBnahYtJfvumsX3/3N74LoQpqJUwltplLVpYCX9chlq5RFUdP8PA1FJ7cdsnXuTmEGCSWU/EjKU6WKzaVKzn5UWzUhpwqKAoXzQrQoDH1tJ5/ME4gGiLXGqGmtoTBZ4NUfvEowFiTSHNHaT65AuD5MLpHDDuposImBCe0sH82UWkkHIgEduJD3SA+kibfHabyikZadLbPfEMNlyazJoUqp/wz85xleH8L4cwyLzHRJdOXO2HW711HTXEPPj3uwAhaFbIGh7iHOHTpHbUctDZsbuP7Pri+VTEmeSTLyykhpU6cw+zpmZDbB4+nunvWb6vEKno6Um2aOWKITMqMWytWVrcONOqlzxvP42teUsGjAdXTh0mKiacHTFxuIBsiOZglEAtRtqGPDDRvoe7aPxKkETtph93t3s+cje0qN0kaOjTDSPaLrvQUs3Ssn7xLv0GHp61+7/qKSKw2XDxddkcBgWCqO3H+E/ffuxyt4xFpjFLKFKaVgis5YJ+tw9HtHyafyRJuiTA5PlpIynZxDtOl8lFBtey0jr4wQiAYI1gS1b2KxEW2Gmuib0CbBeABcSqVuilghSzvpC35DNCi1zq6s0Dzl8AGtrYml/U/FcOKif6tkkkNrUfl0ntHjo7pGWzRAsjfJhj0buPJXr0R5uvVCub+keK8DsQDKURQmC9hBm023bMLLe0ycnWDLW7ZcFn4Jw8VjhI5hRTNwaICf3PsTHXXVWkMhW2D4yDAtu1s48KUD2myWzDL00hCpcyn91L0hzvjJcby8pzPtFTiTDqf/+TTf+7ff418/9a9L2kIgpAtizisooJyimWuOBMIB7LBNdly3BLACWiAUe+4oV4f+hmIh8hN5xNLh2hIU0v3p8z4ep2LBZQmrytG5OK1Xt+K5Hp7jMdw9XFqvHbRLIdOqoHAdFzfn4kZcTj55kq23ba2a61J0fCdOJ0gPpmnc1kjLLt3wLTOWIfqW6JIEDxhWN0sqdERkI/ANtDnOA76ilPqvfij2d9B5QT3A+5RSY/6cTwEfQmcQfFwp9Zg//nrga0AU+D7wu0opJSJh/xyvB0aAX1dK9fhz7gH+k7+cP1VKfX2RL9lwiXQ/0I1bcKlprdF+F1cxOTzJq4+9ivIUO965g7Zr20j1p3TrgYBF8lRSR6GVOdgB8KDvmT5++lc/xc27bL51M/3P6TbHS4KC/GSegBPQ5W3KmrsVBUWx9plbcEEgti5Wagzn5vyw7GLZmwphpzxVioqraa2haWcT+Yk8dR11TPRN4GSdkp9mSitupY/nKY/J0UlO//Q07de3Vw3dbbu2jTu+cAdP/eVTuI7L8MvDnNp/Citoceunb13Q2zWfumSrrYbZcq13Jdyni6q9dgk4wO8rpa4CbgI+KiJXA58EnlBK7UCHZ38SwH/t/cBu4A7gSyJi+8f6MvBhdDmeHf7roAXUmFJqO/A3wOf8YzUBn0G32b4R+IyINC7u5RoulcTpBDWtNThZh/RgmuHuYVJDKbJjWQqTBU788ARD3UNkE1lyEzldar98Qy1Spgn85N6faPNWJMDOX93J1rduLfVRmTflG/8cQqBVQenqBgqUpUrCsbi2yuMWHfbl5jcROR89R9lcD7Chfks9DZsbSJ1N0bitkXhbnN3v202wJoibd3WyZuX9scASfQMmhyZnrBTQdm0bO9+9k+Ejw6SH0tS01rDumnUcffjogtUNm09dstVWw+zI/Ud48Dcf5Mh9Rxh9dZSRYyNLst6Vcp+WVNNRSvXjt7tWSk2IyMtABzr6ba//tq8DXej+PHcC31ZK5YCTInIcuFFEeoA6pdRTACLyDeA9wCP+nH3+se4Hvii6TePt6BYMo/6cx9GC6h8X7YINl0z9pnomzk3Qf7CfQnKqp98reDg5h5M/PImTdUq9ZiQoF2oCZZtsLpHjhW++gBWwqGmuQXmKSHOEYCxI8kxy/osMoBNIRSdXztlUl5/heB7YAf185WSc8710ipWsixFpvnYktu7U2bi9kdr1ujV1NpHFyTp0P9hN/ZZ6gjVBUoOpCwWlBYIu+dO0rQk378769Dt0eIjNezdPyabPjGUWrG7YfOqSraYaZgOHBth/7/6SNupkHQYPD7LumnWLvt6Vcp+WzacjIluA64FngPW+QEIp1S8ixVbYHcDTZdN6/bGC/3PleHHOGf9YjogkgOby8Spzytf1YbQGxaZNmy7+Ag0LQus1rTz3d8/hTDoXvqggnyjbuf0w4wu0gCp4jqerRE/mS3kmkcbIVGHlR2jNWq7GT76cc5WB2fCTXQvJwvnyO5VaTTEa2g+lFlvwCjr8OxQLYdm6DcHL//QyCITrw7S+ppXJn0ziuZ6uPqC0gBTRiaWe5zF2YoxoU5SufV0zml7m22RtvlQ7flGAVpqGFnstC0n3A914Ba9kLg5GgwAke5O6Ht4islLu07IIHRGJA/8E/J5SKlnsF1/trVXGpttSytP05jvn/IBSXwG+ArBnz54F2kUM86Hc7jx2YkyXxZ8LfnQY6AgwJ+9M7eJZhlfwzpukANd1taO+3MSmuFDgTFeJYB7BBLPiUkrsLObaXFApoRga7SjsiK1Doj1dn23oyJBukzCZx7ZtGrY16KoC41kaNzeSGc0QaYyQGc2QGc7oVty2hXIUweYgW96yZdYmZItdN6zy+KmBFKf2nyJcFy6Zhp78wyep66jjzM/OkJvIEYqHqG2vpeWqFuzQ0hf9nAuJ04lSBGZR4AQiOp9s61u2Luq5V0qtt6X26SAiQbTA+ZZS6gF/+JyfhIr/fdAf7wU2lk3vBM76451VxqfMEZEAuvXC6AzHMqwQBg4N8L3/63vcd9d9HHvkGFbQIj2YZnJw8sJorUp8T1/RAa88XZcsUFchsCpK+l9ApfCofJSZbp5U/DyXEjczoPKzVQKdeq7i/bGCFl7B0+HMtk2wNkhmJINyFYFIADtkY1mWbugmoqsK+PXg7LBNrDmGiC4bVGwWV42Lqao8HyqP3/98PwDtr2tHLMHNu4weH+X0T0/jufp6MyMZUkMperp6GDsxtiJzheo31RPviONmXd1IzzeD2kF70de72J/ZXFlSoeP7Vv4eeFkp9ddlLz0M3OP/fA/wUNn4+0UkLCJb0QEDP/dNcRMicpN/zA9UzCke627gSaVTzR8D3i4ijX4Awdu5sIacYZkoOjn7n+8v5dP0Pt1LIVvQVaJnw68IXewF4xZcne+S8ZCQnP9Lv1iNRFESbNWww2Uvlpm/5sV8BJV/OrG0WQ0BO2QTrg8TWx8j0hDBjtgEQ0GsgMXk8KTunJpx6Li5g0AwoH1EFgSjQQLhAA1bGxBb6H2ql9RAakbTy0I1P5uOyuMXow2LHUeHXx4mVBticmiSaFOUpiuaCIQDZEeyWhvqqFtx/hzQG78dsGnZ3UIwEtTVzBXc8ulbFn29i/2ZzRWZrvTHopxM5JeAnwAvcv7f/z+i/Tr3AZuA08B7yxz+fwh8EG05/z2l1CP++B7Oh0w/Avw7P2Q6AnwT7S8aBd6vlDrhz/mgfz6Azyql/t+Z1rtnzx514MCBBbjyy4eLDcns2tfF8NFhep7sAfx2BQEh1ZeqHm01T+ywzk2Ztj3BMiO2nG85MEvFAbFEC9SCV5pjR2wCoQAINGxtIDOcIZvIEozoVg2e4xFvjyNK6Ly5EztsE22M0tPVQyGjm7ABNO9oppDRpp/1r12vw7SFUhXnzps6l6WKc9e+rimmoe6HurGDNonTCZqvbC6VMcolc1z5ritJ9ia586t3Luka58pKCFtebETkOaXUnqqvLaXQWW0YoTM/itqK67ik+lKkh9Kl/I3dd++ece4/vucfGT8xTmYkg6c8LLHIjmcpZAsL5i8J1YemBh6sBMTv2WOJ3uDn8u/oR5tFGiM0bGkg2ZfUHTTrIkRboli2RWGywOTgJNHWaKn7Z+2GWm799K0c//5x6jrrEEvofqibcF2YfCrP+MlxmnY0YYdtMsMZGrc1kpvIkR5ME6oNTelXc9uf3bakG2XxbyvSGCFSH+H4Y8fJJXMEY0Es2yIYDU4RltFGk6i6nMwkdJbcp2NYu3Q/0K0TBo8MU8gWqGmtAdF5MTPlAgwcGuDMT88w+NIgEwMT2o8zOqnbOC+gg76QutTiagtPuDGM2KL78cwh6g50sIQEhFhbDLF0m+pgNEjzVc2gYOSVEVL9KVpf00ooFiIYDXL13Vfza9/8NXbfvZv6TfWlcjrF7p5WQLcmCEaDZIYzxNbFqNtYpxNK68KEakIEa4KE68Kkh9LT+noWi0rTUPvr2mm6oomWK1soZApkxjM4GYf4hrip/bbCMWVwDAtG4nSCVF9K+xL8yJxIfYTJoclpcwGO3H+EH/z+D8iMZM5vup6OJltoVqJpzc25BKNBHW3ma2FW0MLDq1qAtNjmoLajlsxwhgwZQrUh3IJLz496dAmd2hDh+jCJngTb3rbtAnNYeb265iubObX/FACbb91MIBIgO5bl5k/czDOff0ZrUGUtlQORALlEblnCkSvbABTNVIvZKM6w8BihY1gw6jfV0/t0r9ZwfCZHJslP5nnxWy8ClJ5Aux/opv9gP31P95Edz57vGbPwsmZF42QcvIKntRfLz5dxvVKL6mLuUagmRKQhQn4iTz6TL5XE8TyPRG8CXIg0RrTWlHexLIuGKxoY7h7mmc8/M8V3UNk8bMubt4BoARhtj5YKdtZvqmfwyCBO1ik9RBTbM0wXZjtff8Wl+Dcux140awHj05kB49OZHwOHBkp9aiL1ESZHJpnomyC2Lka8LU7bdW26zpmCxisaOf3Ppxk6MqQ3UItSbbXLETtq42Z8iVvWEM6O2uz8lZ2s263zpX/+335OfiJPTXMNEhDdpjutNaSa1hrcrFuqzBCIBIi3xbn67qtLrZDnE600cGiAJ//wSUaPj87Jp1Ppd5ntnPN9v2H1YHw6hkVl4NAAXfu6eObzz9C8sxln0mFyaJJ8Kk9sXYxAJEDr1a1EG6OkB9Okh9K4eZfxk+O6j00x0bHyAegy+ut0s1NVPDtgE24IlyK0inkVbs7VwiaZJTOSKXXqRKG7n/q9gZy8Qz6dJ1ATKLXrninvphpt17Zx22dvY8veLXgFDzfvsuXNW6YNIigvszKXc873/Ya1gTGvGS6J8qfVus46Xe8r41C3sY6TPzxJvC1O69WtpfwKJ+fgZByOP3IcJ+vofjHVCl4CgZoATvrSw6VXBRVVD6LNUZp2NJHqT+HmXJK9Seo31RNpjJBL5HALWqMpaYaK86Vt0NFtYsmU9tgXU/Kk7dq2KT11ZmKuZVaKJrUXv/Ui8Q1T/z5mWuPlEGp8OWCEjqEq0/2DV46nBlJTigi6OZf0UJpkX5JwfZjMaIbep3uJ1EdouaqFQrrARN8EbsHfMGcQKE6qSr21ywCxRQvnrEOkMcKOd+wohf8e/d5RsmNZUNp8pjxVqktnhf0uo3kXK2hR21FbagIH05c8WajNfC5lVsofUmo31JJJZDjzszNsfONG4uvjM66x/OFmtjI9hpXLZWTAMMyV6UqgH7n/yAXjJ354olQnLDWQovcpnUSYT+dJj6RJnE7gFlzSw2kOfv0gY8fHcDKOLtuymjUYvzrzvJjj+1VBkR3LMvjiIOM946TOpUoh57WdtSWTpPIUlmURrAvqZNG8RygWYv1r1rPzXTuxgzZ2yJ6x5MmllrsvmlYf+uBDpM6lGHt1bMYyK+UmtZarWhClr2XopaEZy7IYU9zawWg6hguYrgT6s198tpR4VxyPNkc587Mz1DTXMNw9jNg6adHLe8TXxwnFQiR7kysvKfNS8QWmHbEv8MdUxYJQPEQ+ObUq9rSCV+kWDeG6MLlkrvRU335dO+cOndNFQXMOgUiAUG0IO2STS+TY8Ss7Sk55J+NQ11FXMs1VayN9KeXuq5lWi1Fw052z3AQXb4vTeXMnwy8PM3F2guhbolPeX66B9f+in443TC0Kv1IrSRtmxggdwwUkTifIJrO88tAr5CZyhGvDtO9pZ+zEGNmJLImeBG7O1UmJtUEmz01qk1DeoTBZINWfAqWPs6q1mdlQfgBAUeuZTvYUWyQUPKyQNoEppaomvkpAtymwbIuaxhqiTVEm+iZY/9r1dD/Qza67dtH93W6UKOrr63WduaxL85XNuiJBY7RkJrv+sxcKmUoupdx9VYG1jRmrAVSa4OJtceywzZa3bJkyp1KgDR4Z5NT+U2x58xbibdr/sxwVkg2XjjGvGS4gN5Hj1UdfJT2U1h07h9Ic+/4x0iNpxo6NUZgs4Hke+XSe1NkUnvJI9iXJjefwctMHBqxZxG+4VvnfZOkWC3bYJhjT1ZztiH6fWDL1/Zb+skO2Fk4RCyerNZlsIlsSBG3XtnHLp28BpTt8BiNB4h1xho8MMzEwAcD2d24H4JnPP0PXvq4ZTWXl1QmKlG/m5eazymMlTiemJI7C7AJrrpWOK81p7a9rB6D/+f5lrZBsuHSMpmO4gKEjQzoUt1xo+NUCXFXlcd5mqtnockP5LRUqNBcrYIHonjy5RI5APEDADlBQOszZCuiaa3bI1vOVFkaBcABn0kHCwtBLQ4RiIUaOj9CyswWA3XfvpnlncynBdvzkOK27W2ne3szIsRFe+MYLdNzUQbg2zLFHjnHofx6qWpkAplYnKM+Vuf5D11d13hd72Lh5l7ETYzg5h+YdzaXjzaZ9VCamTmf2q9TA4uvjbL51M30/75vRXGhY+RihY7iAxOlE9Zpn02kuLlOipC47pmlloDyF8hSBmBYiTtLBwdGhzAEhEA3oxmk1QdxxV4dBl/mHCm6BQCyAHbbpe7qPq+66qvRaMRu/a18XDZsbSuaqib4JQnUhRo+O4ha0CTTaFKX/+f6q0V4zCYGufV1TIxP9HjbpwTTbb99OIVvQgSNA0xVNUwTWTMylkkC1SLhAJMCuO3eZQp6rHCN0DCWKjttCeuUVxlyNFBurORNTQ7+Vp1B5RYECgpAZy2izZAVewaOQLjA5PEnthlpOPnGS5p3NHPjyAXp+1EN2PEt+Mk/TjiY6b+wshRyH68KMHh2lfnM9wWiwVPK/GO1VueEXfy8KnmJEWKW2Uexh4+ZdxJKS5jXRN0EgHFhQ7aOogaWH0hdULDesbozQuUz56V/9lGf/9lkyoxmiTVF2/YtdZIZ0G2PD0qAchWKG0HG//XaoNkQgGuDo944y9NIQidMJcskcVkC3mB45OkJ+Ik/ba9vIjmVJnErg5l1cxyVIUOf71EdK/pbKvJzWa1o5+vDRC3JggjVBsolsSdsodrgs9+M0b2+mMFmgflP9FIF1qYKn7do2dr57Jz+59ye4BZea1hrqOus4+vBRmnc2G7PaKsbUXpuBtVp77ad/9VO6Pt2lI6j8sF3P8WjY2oCX90icuozCUGcKWy5/mz1LXTiLi2vDMNP5BQLRAOHaMPWb60n2JhFECyq0QCpM6s6qVtDCczysgIWTc7DCFsFgkFhbDNu26by5k2wyy+jRUbLjWaLNUdquayMQCdDT1cO6a9ZN8c1kxjK4eZdCunBBD5vyCLKRYyMMHh5ky94tC14/rbJxW3FdplfOysfUXjNM4em/ehrP1RuUHbRLT8xjx8bITeSWe3lLhgSEYE1wTj1sZn04u1iX1iznFktK/XZC8RD5yXzpswMtlOyQFj5uwSXSEKF+Yz3BgG5qlh3L0nFTB9lklr6n+3SrgiatqfQ93Yebc/EKXqlzaJFIfQQ351btYWOHzyecDh0eonV366xJmzNFwU3HxUTHGVY+xrx2GTI5PImnPKgiX3LJNSB05qi9KE/NLnBEJ3U6WUf3uFnoeImZjmfrytvh+jASFDJnM1rb8rRmagdtlKPL3uDpRFURIVIfIdoU1e25PYVX8EidTdF5cydDLw0RjAYR0Rc+3D1MrDVGeig95dTFKLTpetgUTXP1W+tp3t48ZW6lYLjYEjZzKatjWH0YoXOZMXBoQG+2CrD15lVO0fm9mglEAjiZOdRt8yBUG9IVlHPTZHYKtFzVois4i2L8xDhe3rtQqInfmsHy7+2l9AUSXY2gcXMjDVc0kOpLkUvmcF3tpymMF7BDNqFYCDfn4uQdxBaCsSBuwSVxRgcAOBmH3e/bzd59e3nogw9R11nHRN9Eqa1zMQeo5aoWsuM6d6YybLqSSiHUta+L4aPDpM6mSvlE8Q3xUpDBwKEBHv34o6QH08TWxWjZ1VIyzc1W9WCmcG7D6sUInTVOtQKdsfYYqb4UXmFthjm7hTnu+JYWsp7nTRmbon347Z9d1yUYDhKqDZFL5LSWoZSOOvO1ofY97fQ/148dspkcmizNn+n8oM9nhS2C0SCR+gidb+wsdcB89OOPkpvI4WQc6jvqsUIWiZ4EhVQB5SjE9k1a9brBG+j8n2RvknhbvJQ8WdQaWq5q4czPzpTWZods7IDNLZ++haHDQzPmzlQrDNp6TSsvfOMFQnUhwnVhMuMZEqcTXHXXVSUNJz2YJtoSpZDRIdadN3cSWxeb1Uw215wew+rCCJ01TDWzxokfnqDzDZ2c/NFJcqNrwJRWhbk0gpOAgId+6q8J6uRWxXmBUxQ+Stc4swIWSimcjENNaw35RB7P8QjU6DyYXCLHuRfOaed7RoeciyVISPCyVTQj/OOL/z4Roi1ROm/sxLKtUqXnxm2NoKCQLZS6d4avCTM5NMnu9+0mcTqBFbToe7qPQDRALpnTFSMcj1s/fWtpgy5qDZHGCJ03dTJwcIDMaGZq0ujd1e/VwKEBDnz5ACceP1EKQChFuMWCdN7cyUTfhI50a4iybvc6hg4PMXR4iEhjhNi6WEm7Am3Sm6n7aDmmO+jawwidNUjxibT7oW7skE379e0lJ2+0OUryTJLGrY2M5EdKzbnWVMmaOfpz7KBdagNgR+ypJrYy4WMFLDzXw1Y2gUgAC4trf/NazvzsDIFIgMmRSQqTWtCE6kJYtkVuIodyFfWd9bgFl2RPmaNeOH8+pQWgHdSFQ8O14SmVAypbgOdTeSb6J/Acj+6Huul8Qyd2WEenDXcPYwUs4m1x2q9vZ/fdu0unrNQadrxjx5xaGBQfXIZfGZ4SgNB5cyeRxgin9p/iynddOSXyTXmqpMXUddZN0a7ssE16MG3MZJcxRuisMcq1G6W0f6Fo0ii2jD75xEnskE1tRy2jR0dXtcCxwxXCYo5BBKjzG3+xbpogOBlfqxGFclXJ4R6pjyCW4LkeuYkc/b/oxw7bAKQH0gRrg9i2FmISEMK1YYKxIHUbtB8l0hxBbMHLeTg5p2TaFFsIxUPUtNboSC2ZmuOy665dvPzdl8kmsli2VQpnj7XFsEM2yb5kqf335ls3l/wee37nwmjVi9EaijXQ3LxLuC48JQBh862bQTEllwemOvszYxni6+NsfONGhl8eLvl2TB+cyxcTMr3G6H6gG8/1OPfCOdIDupma53kMdw8D2sm+7Ze3EagJkDyTnOVoKx/llUmYuQocwLIt8hN53RogFtIajK01ntqOWmo7anVFaKWjv4qVot28S9POJtycSy6RI30uTSF33r8SiARo2dVC85XN2LbNb3X9Fv/u2L/jDR97A1e87QoatzfqMG20wLECFrUbaomvj6OU4sTjJ6aEE7dd28atn74VUaJNaSGLeHsc29YabOO2Ruo21pXCmqON0QXd0Ithy5H6SKlvUjEAIZvI0nlT57QFPIvFPUeOjTD00hDpwTR20OaGj91gBM5ljNF01hj9B/sZOzFGMBqktqOWxKkEE2cnyE/kOfbIMTIj2o7fuK1RtyjIu3rjXmkxBTZzigCbEgwxD40t3BAml8jp6D0bvIyHBIWWHbq7aS6Rw47YeCkPt+CSHc9ih3RI8i998pc4+cRJerp6iK2PkR3P4uQd3HG39MSfS+ao7agtna/oU2ne2Uw4FmbwpUHy6Ty1G2qpadamMxEh2hy9IKqrWODzwQ88CECkIVKKAlOeItmbvORkyem6h5YHIJx84iTJ3iRO3iEQCjB2YozbPnsbwLTO/p3v3sn+e/fjFTxirTHiHXFTVeAyZ0k1HRH5qogMisjhsrEmEXlcRI753xvLXvuUiBwXkVdE5Pay8deLyIv+a18QX+cXkbCIfMcff0ZEtpTNucc/xzERuWeJLnnJyY3nEEsIRoOEa8M0bGkAIHUuBcDWt27FDtsM/GKAUDykKx3LHLIjl5IAhGpCi3d8gVhrjGhzFOXq4IBQLETjtkaUp3BzuuyKZVulJEw3p4tx3vKfbtG+kjIBF2+Pl3rkuJ5LZjxDPpnnho/dUHpPsazL8JFh0kNp6jfVY1kWufEc2YkshUwBN+vSdl1b1aiutmvb2PWeXWx+82a27F3YnjIzdQ8taiu5ZE5rfY6HcpQul6TOr23vvr3c+dU72btv7xRhMnR4iC17t7D7fbvZ8pYttOxsMR0/L3OW2rz2NeCOirFPAk8opXYAT/i/IyJXA+8HdvtzviQitj/ny8CHgR3+V/GYHwLGlFLbgb8BPucfqwn4DPAG4EbgM+XCbS0RbgiXorKUUiUTTrwtzo537KC2vbYUUOAWtOO66JtYUoRpEzOjDdGSg30ux5nT+4pYek6yN0m0OUqgJkAoHiLaHCU76tctc3RwQVH7s0IWgbCu9tywuQHQFZc337q5lO/SdEUT0cYo7qSLFbBo39PO8e8fn5J9P3R4iM17N7P7fbu58l1X0rKrBbGFVF+KYFRHgQUigWmFyFx70cyXmVpBFwMQJvomsIM2rVe1cs2vX8Puu3fTeEXjrMLDVBUwVLKk5jWl1P5y7cPnTmCv//PXgS7gD/zxbyulcsBJETkO3CgiPUCdUuopABH5BvAe4BF/zj7/WPcDX/S1oNuBx5VSo/6cx9GC6h8X+hqXm/br2gnWBKck6wVrgjRsapjyvrbr2hjrGcOZdOae17KAFGuFAReYxTIjGWo7aokGoiR6Zt6crJA193wj0f4I19EJlW7Wpf117SR6Erq188Y6mq9qZuC5AcZPjGOFLEK29veATqTdf+9+mnc2l8xOW/ZuOb/usQxuzqUweb5e2fDRYb77m9+lYWsDiVMJOm4833J5ww0bcPIOylFTggCmi+parLyV2bqHFkO3N9+6WTefq/Ke6TBVBQyVrASfznqlVD+AUqpfRNb54x3A02Xv6/XHCv7PlePFOWf8YzkikgCay8erzFn1lNvj7bBNPpln/WvXT8nijnfEp8wJRALUb6wnO54leSaJkqnVjiUoqMLihbV5jkcgGsBJV6kcoKAwWWD3e3dz6FuHKKSmb7UQjofJjGYA7ZgXS6YXQqI1FKUUsXUx3vfA++h+oJvMVVM3xWhDlOOPHNemx6CU1lvM8i+2ja6WLR+MBUtaQ2ogxfCRYRDIjmmf0Kn9p7R5bH2c+Po461+znom+iTk3JluMvJW5CIaLFR6mqoChkpUgdKajms1EzTB+sXOmnlTkw2jTHZs2bZp9lcvMkfuPXFD+HdE+iIFDA+TGc0SaIgwdGQJ0KfriP35dZx3hWJhAJFDSKDzPw83qMGKnMHMpmaLZqZgJPy883eLZCThQ5TROwWG4e5jmK5s59/y5qVFqPhLQIcyBaAAn66BE6XbPzgzJmAGwlIXruhz40gEmBiaIr4/T09VT0gybr2ymZl0NmZGMLi8TCVLbXovYQqw1VmobXU3reObzzxBrjQF+EmRE5/bkkjk639BJz4976P9FP9tv314Kg77jC3csq1N9LoJhvsKj/EEoWBPEzbum46cBWBlC55yItPtaTjsw6I/3AhvL3tcJnPXHO6uMl8/pFZEAUA+M+uN7K+Z0VVuMUuorwFdAtza46KtaAo7cf4RHPvqIzoAXmOifYKR7hLY9bSC6xEshU8DN6Qi1/gP9FCYLtF/XzvUfup7uB7o5+PWDFCa1/8ctuKUimJU12S7AOp+74mQd3Tl0PhFworPspwgci1IXTq/gkR5Ms+mWTQwfG8ZJXiiZ7KCNZVtIREd9iSUU0tohb9fYxJpi1LTUMHhkUHfkFLAsi/iGOLHmGP3P95MZy3DuhXPE1sUI14UpZAqc2n+K9a9Zz+jRUZSoUriwm3VpvKKx9HRfTeso1wiKDdWK/WzibX7L5Wd0y2U7bBOsCfLM55+ZEjG21MzFbDcf015lJYyFbHdgWP2sBKHzMHAP8Of+94fKxv9BRP4a2IAOGPi5UsoVkQkRuQl4BvgA8LcVx3oKXdTjSaWUEpHHgD8rCx54O/Cpxb+0xWPg0AD7791PIVvQfgE/7NkJOvQ+1cvwkWHdi6UuTLheb3ye41HbVsuuu3bR/UA3/Qf7mRzUFafdvDslRHlKPbJqeJCfzOuKx66n+81U0UamRVUpV+OXhUHAEovYuhhewaN1RyvjveNkh7IodMKmHbEJxoLE18XJp/K6uVhjhEAkwODhQfKpPKHaEMFYkFA8hIopAqEAdRvrpnTTDIQDpEZTxNbFpiylpqWG3b++myf/45MM9Q0htlDTUkN+Ij+j436KRlAXIZPIgIL217UDfsvl9+yaUpYmti4258rLi8VczHZzNe2VByYApe+zFfg0XB4sqdARkX9EaxwtItKLjij7c+A+EfkQcBp4L4BS6oiI3Ae8hH4e/qhSqrgtfgQdCRdFBxA84o//PfBNP+hgFB39hlJqVETuBZ713/cnxaCC1Ur3A914BQ/P9aa0OnZdF7fg4mQc1r9mPcFokFwqR2Y4Qz6d59C3DnHkviMoTxFuCCMBwU26OiM/IPOqMu2knVKNsnkJHB/LtnCdiiAG/zBOzqHt9W04aYexE2NYQYvmXc1kx7K4BZdgTZBgNIgVsIi1xlCeIhjVnS4btjSQ7EuWOl8GQgGskFUyvY33jJNP5wnFQthhm0hzpDQ3Uh+h7bo23JxL885mmnc2kx5K68oFYXvWXKByjSDcGCY7nqX1mlZirbFStFlRy1yrG/NsgQmGy5uljl77jWleeus07/8s8Nkq4weAa6qMZ/GFVpXXvgp8dc6LXeEkTieItcYYfmX4whddcJWLQpFL5ZjonSht8IVUATfs6uTIZE4XuoTzGfeeO7uZrLwS8yUklU5rwvM7Zj735eewQ7Yu3Z+TkpmwaUcTVsAqFZAsNjYrRpIVo8jibfFScEXyTJLxU+OM94zrAAFLkICQPJPEClnUtdfReVMn8fVxbR5r10majVc0smHPhtLSMmOZWQVDuUZQmXRZNEk98/ln1uzGbCLWDDOxEsxrhougflO99olUe/L2m3/lJ/Lkkjlt+kJRmCiA5bc5ThWQ8vgKFzyZxgFfjnChoCkr0T8fqlWDtoK67lkgEiA7lgWBcJ0WkE5Wa1bJ3iS1bbW0X98OAj1dPVgBi54f9ZAeSmMFLW799K1TCl4OHBrg4Q8+jHIVVsQiVBciM5QBW2tcmfEMp396mvWvWY9lW6WggEsVDNOZpNbyxmwi1gwzYWqvrVJ23bULO2BPyZsASlqIFbRo2t6Em9eNv4pBAiLa8e9mXJxcmXN+HnXLSn81fqKlZftah7+UKcmaxTFLCDeEibZGZ/yrs8M2Nc01OJNO6dqCEd1nxgpaKFfh5T1adrcQW6eLXsbWxXBzLumhNDWtNay7Zh1HHz56QQ2z9deuZ/ev76Z5RzO5sRx22KZlZws1LTVEG6IoRzHRN1Hyq9RvqiebyE5Z30IJhsVK9FwJFE2Mi1UPzrC6MZrOCma6elhw/h/75QdeppAuTBEASikiDRFu++xtPPrxRxk/PY6TdghEA6UyL1N6x6BNa1bQwst72uxVTQhZEAgFCMaDZIYzU15XniIQC+BMOroqgoLseBY8CNQGtOCoi7Dtbds49qiuAedknQt8SMpVhGpCZEezupqCrSVUIBxALMHNu1z9L64umc7qN9XTelUrbde1TdEaqpnByhM6yyPLattr2bJ3S6mOWWUPGlj4J/a13qDM9MExTIcROiuUufSVb7u2jfYbdLfKclOV2ELzlbqg4h1fuIMHf/NBVJ0iko8w9urYBQIlGNe+EeUqXQmgJcrk4CSpc6lSewTlKrB0v5ia1hqy41ktMPwyNHbIJtwYJrY+RvJkEifnEGuNEYwFEaTULAyg+8FufaxYSGthWbe0fuXqp/5IS4TcmK4j5xbcUgO1WGtsSr8ZgIc++NAF0WfVzGDlQiRcFyabyCJKtJmOC7WYxRYMZmM2XI4YobNCmWt009Y3byUYDTL80jC5iRzh2jAtV7fQeaNOZWq7to36rfXkxnJkk1matjcxdmJMR425EKoNEWmI4DkehUxhSqvk/ffu59XHXtXVA2oDRJoiJU2orkMLQkSbvwKxALZtU9tWy4brN4DS2f/lGlpRkNa01uD26xplTsIp1YcL1YawQzb5VJ54XZymbU1kRjNkhjN4rq5SfNuf3TZjbkyRamawciESaYyQHc+WzHTlkWWVc4xgMBgWDiN0lpGZzGdzDTvdddcuxk6M0Xxn8xQTULlvoP269imbck9XD5nxDG7eJTuSLXW9tGyLsz8/WyrQWNNSQ21HLeE6XbXAyTqkB9OE42HWv3E9dsguJaUmTiZovaaVpiuapk0GLG72m960iZNPnqSQLpRK1oTqQzRsbiAUD+lotILHO7/4zmnvT/n96z/Yz/jJcVp3t06puFDNDDaXyDKDwbB4GKGzTMxmPruYp/fpNs9K30RtRy2J0wk6buogXBvm9E9PkzyVpH5LPRvfuBE7ZPPUXz5F7zO9SEBIn0vjZLVjP5fOkU/l2fHOHaXNvba9lobNDbNqZUVBKpaw9batDHcPMzk6CQKNWxsJxXU7A0FAzaxllN+/tmvbCMVCDB0emlJxYaGSHQ0Gw8JhhM4yMZv5bD5O7Nk2z0rB1LyjmV2/touhw0MkTid08c8t9VgBi5FXRmjZpXueTA5PIiKlsv7ZsSxe3kMFVKkEPsCp/ae48l1XTjlnuVZW0kie72fwyGDJhwI6Ws3L6xbOwVgQJ+uQm8ix5c1b5nX/mnc06yi0xuglNzQzGAyLhxE6y8RcyslfqhO70nzUek1r6dxAyQR33133EW2KEogGKGQK9D7VS8dNHQi6oKZC6Z/9ZM7y/juRet3MK5vIVtXKyjWSjhs7OLX/FMcf0xWcg7EgNa01FNIFUv0p3LxLpCFC0xVNpaCDi71/BoNhZWKEzjIxF/PZpZh/Ks13I8dGeOEbL9BxUwfN25tL5rxgLEi0Wa9BREpZ/gMHB6hprcFzPQqTumio2IIEhUjt+aZc2USWzps6dSInF2pllRrJlr1bOPrwUfKpPHUddWy8eSMo6H++HzfvsuMdO+ZU+HItJ1caDGsZkxy6TCx2cmBlN8iJvglCdSFSZ1NTukP2Pt1L23VtOFmn1G1UKUVmJMOWt2yh48YOmnc0E2+P03JlCzVNNYQbwlPWvOd39kybDFjZOTK+Pk60NUpdR935vjJtcbbfvp3269svaHe8XPfPYDAsDkbTWSYWOwek0vxUTIYsz7AvmsYCkQAb37iR4ZeHySZ0s7Ftv7yNPR/Zw1N/+dSUhnBjJ8ao66ir2htlruVeAuEL/+zmq6Ws9eRKg2GtYoTOIjBTKHQ5ixk9VbnZR+ojZMYzRBummqOKprFIY2RKy+Q9H9lTfWP/7Pw29moBEbF1MVC6asClZPpX3r+BQwN07eua9b4bDIblQ5Ra0X3KlpU9e/aoAwcOzGtOuS+lfENd6tpTlesYfXW0FCBQnsty8yduBpiTkJztfNMdo9prC3HOma7XNA4zGJYPEXlOKVU1GsgInRm4GKHTta/rAnNS8ffZQnnnqiHNlfLj2WGbyeFJRo+NgkDnTZ0lbeZSWQkb/qXcd4PBsLDMJHRMIMECU+k4h7mF8hY37sxYZkqyaHml5PnSdm0be/ft5Q2/9wYK6QK1G2q58t1XsvnWzbpI6AJRGbRQDFLofqB7wc4xGxd73w0Gw9JihM4Cc7Hl8Bdz415sobASNvzFbENgMBgWDiN0FpiLDeVdzI17sYXCStjwTQi1wbA6MEJngbnYBlaLuXEvtlBYCRu+aRxmMKwOTCDBDFxMIMHFspjO+KVw9C90EITBYFi9mOi1i2QphQ4s7sZthILBYFgqZhI6Jjl0BbGYyaKmjL/BYFgJGJ+OwWAwGJYMI3QMBoPBsGQYoWMwGAyGJcMIHYPBYDAsGUboGAwGg2HJMCHTMyAiQ8CpZVxCCzC8jOe/VMz6lxez/uVntV/Dxa5/s1KqtdoLRuisYETkwHSx7qsBs/7lxax/+Vnt17AY6zfmNYPBYDAsGUboGAwGg2HJMEJnZfOV5V7AJWLWv7yY9S8/q/0aFnz9xqdjMBgMhiXDaDoGg8FgWDKM0DEYDAbDkmGEzjIiIj0i8qKIHBSRA/5Yk4g8LiLH/O+NZe//lIgcF5FXROT2ZVjvV0VkUEQOl43Ne70i8nr/uo+LyBdERJZx/ftEpM//DA6KyDtX8Po3isiPRORlETkiIr/rj6+Kz2CG9a+mzyAiIj8XkRf8a/hjf3y1fAbTrX/pPgOllPlapi+gB2ipGPsL4JP+z58EPuf/fDXwAhAGtgKvAvYSr/dW4HXA4UtZL/Bz4GZAgEeAdyzj+vcBn6jy3pW4/nbgdf7PtcBRf52r4jOYYf2r6TMQIO7/HASeAW5aRZ/BdOtfss/AaDorjzuBr/s/fx14T9n4t5VSOaXUSeA4cONSLkwptR8YrRie13pFpB2oU0o9pfRf7jfK5iwq06x/Olbi+vuVUr/wf54AXgY6WCWfwQzrn44VtX4ApUn5vwb9L8Xq+QymW/90LPj6jdBZXhTwAxF5TkQ+7I+tV0r1g/4nBdb54x3AmbK5vcz8D7tUzHe9Hf7PlePLycdE5JBvfiuaRVb0+kVkC3A9+kl11X0GFeuHVfQZiIgtIgeBQeBxpdSq+gymWT8s0WdghM7y8ial1OuAdwAfFZFbZ3hvNXvpSo53n269K+06vgxcAVwH9AN/5Y+v2PWLSBz4J+D3lFLJmd5aZWzZr6HK+lfVZ6CUcpVS1wGd6Kf+a2Z4+4q7hmnWv2SfgRE6y4hS6qz/fRD4Ltpcds5XXfG/D/pv7wU2lk3vBM4u3WqnZb7r7fV/rhxfFpRS5/x/Qg/4O86bLFfk+kUkiN6wv6WUesAfXjWfQbX1r7bPoIhSahzoAu5gFX0GRcrXv5SfgRE6y4SIxESktvgz8HbgMPAwcI//tnuAh/yfHwbeLyJhEdkK7EA78pabea3XNz1MiMhNfrTLB8rmLDnFjcLn19CfAazA9fvn+3vgZaXUX5e9tCo+g+nWv8o+g1YRafB/jgJvA7pZPZ9B1fUv6WewmJES5mvGKJJt6KiQF4AjwB/6483AE8Ax/3tT2Zw/REePvMISRetUrPkf0ap3Af2k86GLWS+wx/+jfhX4In5ljGVa/zeBF4FD/j9Y+wpe/y+hTRiHgIP+1ztXy2cww/pX02dwLfC8v9bDwB/546vlM5hu/Uv2GZgyOAaDwWBYMox5zWAwGAxLhhE6BoPBYFgyjNAxGAwGw5JhhI7BYDAYlgwjdAwGg8GwZBihYzAYDIYlwwgdg8FgMCwZ/x9d+tNWdVWz+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "train_NoOutlier = train_df[train_df.GrLivArea < 3500]\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"GrLivArea with outliers removed\", fontsize=15)\n",
    "ax.scatter(x=train_NoOutlier['GrLivArea'], y=train_NoOutlier['SalePrice'], alpha = 0.5, c=\"purple\")\n",
    "plt.ylabel('SalePrice', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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": [
    "train_NoOutlier = train_df[(train_df.TotalBsmtSF < 2500) & (train_df.TotalBsmtSF > 200)]\n",
    "fig, ax = plt.subplots()\n",
    "ax.set_title(\"TotalBsmtSF with outliers removed\", fontsize=15)\n",
    "ax.scatter(x=train_NoOutlier['TotalBsmtSF'], y=train_NoOutlier['SalePrice'], alpha = 0.4, c=\"purple\")\n",
    "plt.ylabel('SalePrice', fontsize=15)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The demonstration done manually shows that the outliers have been removed from GrLivArea and TotalBsmtSF features. They have been plotted against SalePrice."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1584x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df = train_df.copy()\n",
    "col_colr = [\"red\", \"orange\", \"lime\", \"purple\"]\n",
    "labels = [i for i in train_df.columns if \"Yr\" in i or \"Year\" in i]\n",
    "cont = 0\n",
    "for year_cols in labels:\n",
    "    if \"Yr\" in year_cols or \"Year\" in year_cols: \n",
    "        temp_df = plot_df.groupby(year_cols)[\"SalePrice\"].median()\n",
    "        temp_df.plot(color = col_colr[cont], figsize=(22,10))\n",
    "        plt.xlabel(\"Timeline\", size=15)\n",
    "        plt.legend(labels = labels)\n",
    "        plt.ylabel(\"Median House Price\", size=15)\n",
    "        plt.title(\"House Sale Price and Year features\", size=15)\n",
    "        cont += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* From the above time-series visualization, we can see that with an increase in built year or year of establishment of a house YearBuilt, there is an increase in median price. In other words, if a house is recently built, it's median SP is highly likely to be more than a house which was built a decade earlier.\n",
    "* The same goes with house garage built year GarageYrBlt and house remodification year YearRemodAdd.\n",
    "* The year of selling YrSold is just the opposite as the median price is seen to be decreasing with an increasing in the year of house getting sold, i.e the houses sold recently have lower median price than the houses sold a few years back."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Skewed SalePrice')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "value = train_df[\"SalePrice\"]\n",
    "sns.distplot(value, kde=False, fit=stats.norm, color = \"blue\")\n",
    "plt.xlabel(\"Skewed SalePrice\", size = 15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anderson Darling test of Normality:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "AndersonResult(statistic=41.69199630641583, critical_values=array([0.574, 0.654, 0.785, 0.916, 1.089]), significance_level=array([15. , 10. ,  5. ,  2.5,  1. ]))"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "norm_test = anderson(train_df[\"SalePrice\"])\n",
    "print(\"Anderson Darling test of Normality:\")\n",
    "norm_test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SalesPrice isn't following a normal distibution as it is right-skewed. From the Aderson Darling test, we have achieved a statistic or around 41.69. We have also obtained a list of critical values which are far less than the statistic value. Thus, we can infer that the distribution in the data isn't Normal, thus fails to accept the null hypothesis. A Log transformation might change the shape of the distribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Log transformed SalePrice')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Znc3Ajh2Jb9DA6TiOGTJkCHFxcdrtonzmtaCLSCTwKnAz0Au4R0R6ua3TFHgNGG+MuQqY4v+oqr7Yl5vLkfz8etvdUik2NpYbb7yRlStXUlFR4XQcFQJ8OUIfAGQbYw4YY0qA+cAEt3W+A3xgjDkMYIzJ8W9MVZ9kZGcjwE1dujgdxXGjRo3i1KlTfPrpp05HUSHAl4LeDjji8vyovczVlUAzEVkjIltF5H5PGxKRaSKSJSJZp06dql1iFfYys7NJaduWlmE+s6Ivhg0bRnR0tF5kpHziS0H3NCOScXseBfQFxgFjgF+IyCW3ZjfGzDbG9DPG9EtMTKxxWBX+jnzzDXtzcxkVhjeCro34+HgGDhxIRkYGxrj/t1PqYr4U9KNAB5fn7YHjHtZZbowpNMbkAuuAa/0TUdUnmV98AUBqKF8d6mepqakcOXKEvXv3Oh1FBTlfCvoWoJuIdBaRGOBuYLHbOouAoSISJSINgeuBz/wbVdUHGfv30zMxkXZNmjgdJWjcdNNNRERE6GgX5ZXXgm6MKQMeB9KxivQCY8xuEZkuItPtdT4DlgM7gM3Am8aYXXUXW4WjU6dOsf3ECUbW89Et7lq0aEHfvn21oCuvfBqHboxZaoy50hiTbIz5rb1sljFmlss6LxljehljrjbG/LGO8qowtmrVKgzWPCbqYqmpqezfv5+DBw86HUUFMb1SVAWNjIwMOjZtSrcWLZyOEnQqb5Cto11UdbSgq6BQUFDApk2bGJWcXC9uNVdTbdq04ZprrtFuF1UtLegqKKxdu5aysjJSdbhilUaNGsWOHTs4efKk01FUkNKCroJCRkYGiYmJ9L7iCqejBK3U1FQAVq5c6XASFay0oCvHFRcXs379ekaOHEmEdrdUqXPnziQnJ2u3i6qSFnTluI8//phz585dOAJVVUtNTWXLli3k5eU5HUUFIS3oynGZmZk0adKE/v37Ox0l6KWmplJRUcGqVaucjqKCkBZ05ajS0lJWr159YRIqVb2ePXvSrl077XZRHmlBV47KysoiPz9fu1t8JCKkpqayYcMGCgsLnY6jgowWdOWozMxMYmNjGTJkiNNRQkZqaiqlpaWsXas3BlMX04KuHFNRUUFmZiY33HADcXFxTscJGX369KFly5ba7aIuoQVdOWbnzp3k5ORcuKxd+SYiIoKRI0eybt06zp8/73QcFUS0oCvHZGZmEhUVxfDhw52OEnJSU1MpKipiw4YNTkdRQUQLunKEMYb09HSuv/56mujc5zXWv39/EhIStNtFXUQLunLEnj17OHLkCGPHjnU6SkiKiYlh+PDhrFq1itLSUqfjqCChBV05Yvny5URFRTFy5Eino4Ss1NRU8vPzycrKcjqKChJa0FXAVXa3DBw4kGbNmjkdJ2QNGTKEuLg47XZRF2hBVwFX2d0yZswYp6OEtLi4OIYOHcrKlSupqKhwOo4KAlFOB1D1j3a3+Gj27KpfmzYNsOZIX7FiBTt27KBPnz6ByaWClh6hq4AyxrB8+XLtbvGTyjlwtNtFgRZ0FWB79uzh6NGjOrrFTxISEhg4cCCZmZkYY5yOoxymBV0F1LJly7S7xc9GjRrF4cOH2bdvn9NRlMO0D10FTOXolkGDBtG0aVOn44Q2l/71kUVFPCdC+syZdB88+EL/uqp/9AhdBUxld4uObvGvFg0bMqB9e5bu3avdLvWcFnQVMNrdUndu6d6dQ998w56cHKejKAdpQVcBod0tdSu1WzeiIyJYsnev01GUg3wq6CIyVkT2iki2iDxTzXr9RaRcRO7wX0QVDnbt2qXdLXWoaWwsQzp1YtnevXqRUT3mtaCLSCTwKnAz0Au4R0R6VbHei0C6v0Oq0JeWlkZ0dLTeaq4OjevRg5Nnz7Jt2zanoyiH+HKEPgDINsYcMMaUAPOBCR7W+yHwPqCdeOoi5eXlLF26lGHDhpGQkOB0nLA1oksXYqOiWLJkidNRlEN8KejtgCMuz4/ayy4QkXbAJGBWdRsSkWkikiUiWadOnappVhWiNm7cSG5uLrfddpvTUcJao5gYhnfpQnp6OmVlZU7HUQ7wpaCLh2XuY6P+CDxtjCmvbkPGmNnGmH7GmH6JiYk+RlShLi0tjfj4eIYNG+Z0lLA3rnt38vLy2Lhxo9NRlAN8KehHgQ4uz9sDx93W6QfMF5GDwB3AayIy0R8BVWgrLi4mIyOD1NRUGjRo4HScsHdjUhLx8fEsXrzY6SjKAb4U9C1ANxHpLCIxwN3ARb8txpjOxpgkY0wS8B7wqDHmQ3+HVaFn9erVFBYWandLgMRERTF27FgyMzMpLCx0Oo4KMK8F3RhTBjyONXrlM2CBMWa3iEwXkel1HVCFtrS0NFq1akX//v2djlJvTJgwgXPnzukMjPWQT3O5GGOWAkvdlnk8AWqMefDyY6lw8M033/DRRx9x3333ERkZ6XSceiMlJYUOHTqwaNEiJk6c6HQcFUB6paiqM0uXLqW0tFS7WwJMRJgwYQKbNm3ixIkTTsdRAaQFXdWZhQsX0qNHD3r27Ol0lHpn/PjxGGNIS0tzOooKIC3oqk7s27ePXbt2MWnSJKej1EsdOnSgb9++LFq0SGdgrEd0PnRVJz744AOio6M9d7dUd69M5Tfjx4/n2WefZffu3Vx99dVOx1EBoEfoyu9KS0v597//zfDhw/W+oQ4aO3YsMTExLFy40OkoKkC0oCu/W7t2LadPn9buFoclJCSQmppKWloaxcXFTsdRAaAFXfndwoULadmyJUOHDnU6Sr03ZcoUCgoKSE/XSVDrAy3oyq9yc3NZu3Yt48ePJypKT9E4bcCAAXTs2JF3333X6SgqALSgK79auHAh5eXl3H777U5HUVhj0u+44w62bt3KgQMHnI6j6pgWdOU3FRUVLFiwgH79+pGcnOx0HGWbNGkSUVFRvPfee05HUXVMC7rym48//pijR49y9913Ox1FuWjZsiUjRozgww8/pKSkxOk4qg5pJ6fym/nz59OiRQu9zVwQmjJlChkZGaxcuZKbjxypesVp0wIXSvmdHqErvzh+/Dhr1qxh8uTJxMTEOB1HuRk8eDBt27Zl/vz5TkdRdUgLuvKL9957D2MMd955p9NRlAeRkZHcc889bN68mX25uU7HUXVEC7q6bKWlpbz33nsMHTqUdu3aeX+DcsTkyZNp0KAB//rkE6ejqDqifeiqdlzmY1m5bx+nTp3iucGDv12ufbFBp1mzZtx22238+8MP+dHQoTSNjXU6kvIzPUJXl23utm10bNKEG5OSnI6ivLjvvvsoLivj/V27nI6i6oAWdHVZPjl+nO0nTnB/SgqREfrrFOy6d+/OgPbteXv7dsoqKpyOo/xMu1zUZZm7bRsJDRowsVevi1/QKXKD1r19+vBEWhqrDxwgtWtXp+MoP9JDKlVrR/PzycjO5q7evWmkQxVDxk3JybSJj2futm1OR1F+pgVd1do/PvmECBHu7dPH6SiqBqIiIngwJYWtx47xyfHjTsdRfqQFXdVKQXEx7+/axS3du9O6cWOn46gauuOaa2gaG8vszZudjqL8SAu6qpV3duygqLSUB1JSnI6iaqFhdDT3XXcda778Ui80CiNa0FWNFRYW8rdt2xialESvVq2cjqNq6d4+fWgYHc2bW7Y4HUX5iRZ0VWPz588n79w5Hrn+eqejqMvQNDaWu3r3ZunevRzNz3c6jvIDLeiqRs6dO8ecOXMY3LEj17Vt63QcdZkeSEkhQoS3srKcjqL8wKdx6CIyFvhfIBJ40xjzO7fX7wWetp+eBR4xxnzqz6AqOLzzzjucPn2ax0aNcjqKqkoNrgFo3bgxt191Fe/v2sX3+/dHZ+IJbV6P0EUkEngVuBnoBdwjIm5XkfAlMMwY0xt4HtCrSsJQcXExb731FgMHDiRFJ+EKG9Ovvx4R4bWNG52Ooi6TL10uA4BsY8wBY0wJMB+Y4LqCMWaDMSbPfroRaO/fmCoYzJ8/n9zcXB599FGnoyg/uiI+nnv79OHDPXv44osvnI6jLoMvBb0d4HqLk6P2sqo8BCy7nFAq+BQUFDBr1iwGDx5M//79nY6j/Ozh/v2JjYriz3/+s9NR1GXwpaCLh2XG44oiI7AK+tNVvD5NRLJEJOvUqVO+p1SOe/311ykoKODJJ590OoqqA83i4vhe376kp6eze/dup+OoWvKloB8FOrg8bw9ccr2wiPQG3gQmGGO+9rQhY8xsY0w/Y0y/xMTE2uRVgTR7NsyezbGXXuKfc+cysWdPeqxbpxNvhakHU1Jo0qQJL7/8MsZ4PGZTQc6Xgr4F6CYinUUkBrgbWOy6goh0BD4AvmuM2ef/mMpJr3z8MZEi/NfgwU5HUXWocYMGPProo2zYsIFVq1Y5HUfVgteCbowpAx4H0oHPgAXGmN0iMl1Eptur/RJoAbwmIttFRAe1homdJ0+yZO9eHkhJ4Yr4eKfjqDp2zz33kJyczO9+9zuKi4udjqNqyKcLi4wxS40xVxpjko0xv7WXzTLGzLK//74xppkxpo/96FeXoVVglFdU8JvVq2nRsCHf76f/pPVBdHQ0P//5zzl69Chz5sxxOo6qIb1SVFXpnZ072XHyJE/feCONGzRwOo4KkIEDBzJmzBjeeOMNjh075nQcVQNa0JVHOTk5vLJ+PYM6duTWHj2cjqMC7KmnngLg97//vcNJVE3oLeiURzNnzqSkvJxnR45ExNPIVRXO2rZty/Tp0/njH//IihUrGD16tPVCdSOcpk0LTDhVJT1CV5dYs2YNy5cv55Hrr6dT06ZOx1EOmTp1Kr169eK5557j9OnTTsdRPtCCri6Sl5fHL3/5S5KTk5mqJ0LrtejoaGbOnMmZM2d4/vnnnY6jfKAFXV1gjOEXv/gF33zzDS+99BIxkZFOR1IOu/LKK3n88cdZvnw5y5bpjB7BTgu6uuDdd99l5cqV/OhHP6Jnz55Ox1FBYurUqVxzzTU899xzHC8ocDqOqoYWdAXAgQMHmDlzJkOGDOH+++93Oo4KIlFRUbz44ouUlZXx/5YsoaS83OlIqgpa0BWFhYXMmDGD2NhYXnjhBSIi9NdCXaxz58688MIL7Dh5kt+vW+d0HFUFHbZYz1VUVPD0009z4MABZs+eTSu96XP9Vs2wxNFYE3j9fds2rmvblnHduwcul/KJHorVc3/6059YuXIlzzzzDIN18i3lxY9uuIG+bdvyixUr2HnypNNxlBst6PVYWloar7/+OlOmTOHee+91Oo4KAdGRkbxy6600b9iQ6R9+yKFvvnE6knKhBb2eWrNmDf/93/9Nv379+PnPf65XgyqfJTZqxBu3306FMUz74AO+LipyOpKyaUGvh/7zn//wxBNPcOWVV/Laa68RExPjdCQVYjo3a8asiRPJKSzkBwsXUqBT7QYFLej1zNatW3nsscdISkrijTfeIF7nOFe1dG2bNrwybhz7cnN58P33dXqAIKAFvR5ZvXo1Dz/8MFdccQVvvfUWzZo1czqSCnHDu3Th1QkTOPD119x///3k5OQ4Hale02GL9cSCBQt47rnn6NmzJ7NmzaJly5ZOR1JhYmhSErMnTeKRRYv47vjx/HXiRLo0b37xSjoTY0DoEXqYKy8v55VXXuHZZ5/lhhtuYO7cuVrMld8N6NCBtyZP5mxJCXfNm8fqAwecjlQvaUEPYzk5OUydOpXZs2czZcoUXn31VRo1auR0LBWm+rRpw7vf+Q4dmzblsUWL+OumTVQY43SsekULepj66KOPmDRpEjt37mTmzJn8+te/JipKe9hU3WqbkMC/7ryTW3v04E8bNvDAu+9yRMeqB4wW9DCTm5vLU089xbRp02jRogXvvvsuEydOdDqWqkfioqN5cexYfjt6NJ+fOsXEf/2LefPmUVFR4XS0sKcFPUyUlpYyb948xo0bZ91t6JFHWLBgAcnJyU5HU/WQiHD7VVex+P77ua5tW379618zZcoUNm/e7HS0sCbGoT6ufv36maysLEf2HU7KyspI+/GPeW3TJo7k5zOgfXueHTny21EG3kYXVHePSKX8wBjDkr17eXn9ek6cOcNNXbrw6MCBXNW69bcr6SgYn4nIVmOMx9uJaUEPUQUFBXz44Ye8/fbbHD58mJ6tWvHDQYMY3rmzXsavglJxWRn/3LaN2Vu2cLakhIEdOjC1Xz+GdOpExA9+4HS8kKEFPRzMnk15RQVZx46R9vnnLPn8c86VlXFtmzZM7duX1K5dtZCrkHDm/HkW7NzJP7ZtI6ewkHYJCUy47z4mTJhAx44dnY4X9LSgh7DCwkI2b97MR6+/Tsb+/eQWFREXFcUtPXpwT+/eF39sVSqElJSXk75vHwv37GHjkSMYY+jZsycjRoxgxIgR9OrVS2+24oEW9Jqqrl+5Dvv6KioqOHz4MLt372bXrl3s2LGDHTt2UFZWRlxUFMM6d2bslVdyY+fOxEVH11kOpQLtxG23kZaWxurVq9m+fTvGGJo0acJ1111H37Iyrm7dmu6JiTSLi6vZhsOwb766gu7TwGQRGQv8LxAJvGmM+Z3b62K/fgtQBDxojNl2WamDlbeTiF5+gUpLS8nLyyMnJ4fDhw9/+9i0ib25uZwtKQGgQWQkPVq1Yup11zG4Uyeua9OGGB1HrsJUmzZtePjhh3n44YfJy8vjo48+YsuWLWzdupU1X355Yb2WDRvSPTGRri1a0D4hgbb2o11CAvENGtRspw4duNUlrxVCRCKBV4FU4CiwRUQWG2P2uKx2M9DNflwP/NX+6ihjzIVHRUUFlZ9GvC4rLr5oeVlFBaXl5ZTYX0vLyykpL6e0cnl5OefLyigsKeHs3LkUFhZeeBQUFPD1119feHzj4SKLVq1a0TE6mnE9enB169Zc3bo1yc2bEx0ZGbC2UspRLsW1GTAeGN+hA3TowOmiIj4/dYp9ubnszc1lX24u8z/9lPNuN6uOi4qiaVwczeLiaBobS7O4OJqdOkV8fDxxcXEXHrGxsTRs2JDYw4eJi44mOiKCqIgIIu2vURERRJ44QWRkJNHR0URGRhIZGUlUVNSF81Qi4vHhNK9dLiIyCPiVMWaM/fynAMaYmS7rvA6sMcbMs5/vBYYbY05Utd3adrmsWLGCZ5555qICbOe5ZJmTGjRoQKNGjWjcuDEtW7akRYsWNG/e/ML3iYmJdOzYkfbt29OwYUMdPqhUDRhj+LqoiONnznC8oIDjBQXkFhWRd+4ceefO8U1xsfW1ooKCgoKAZquq0Ls+f/DBB3niiSdqu/3L6nJpBxxxeX6US4++Pa3TDriooIvINKDys8x5Ednlw/6d1hLIdTqEDzSnf2lO/9KcLmbMmMGMGTNq+/ZOVb3gS0H39DnC/bDel3UwxswGZgOISFZVf2WCieb0L83pX5rTv0IlZ1V8GRN0FOjg8rw9cLwW6yillKpDvhT0LUA3EeksIjHA3cBit3UWA/eLZSCQX13/uVJKKf/z2uVijCkTkceBdKxhi3OMMbtFZLr9+ixgKdaQxWysYYvf82HfoXIWUHP6l+b0L83pX6GS0yPHLixSSinlX3pdrVJKhQkt6EopFSb8XtBFZI6I5LiOMReR5iKSISL77a/NqnjvWBHZKyLZIvKMv7P5MedBEdkpIttFpE4npKki5xQR2S0iFSJS5RCrIGhPX3M63Z4vicjnIrJDRBaKSNMq3ut0e/qa0+n2fN7OuF1EVohI2yreG5D2vMyMAWtLv3C9wtIfD+BGIAXY5bLs98Az9vfPAC96eF8k8AXQBYgBPgV6+Tvf5ea0XzsItKyrbD7k7Al0B9YA/ap4XzC0p9ecQdKeo4Eo+/sXg/j302vOIGnPBJfv/wuY5WR71jZjoNvSHw+/H6EbY9YBp90WTwDm2t/PBSZ6eOsAINsYc8AYUwLMt99XJy4jZ0B5ymmM+cwYs9fLWx1vTx9zBlQVOVcYY8rspxuxrqNwFwzt6UvOgKoip+u19o3wcJEhAWzPy8gYcgLVh97a2OPS7a+tPKxT1fQBgeRLTrD+8VeIyFZ7OoNgFAzt6atgas+pwDIPy4OtPavKCUHQniLyWxE5AtwL/NLDKo63pw8ZIQjasiaC6aSoT9MHBIkhxpgUrFkmHxORG50O5IG2Zw2JyM+AMuBtTy97WOZIe3rJCUHQnsaYnxljOmBlfNzDKo63pw8ZIQjasiYCVdC/EpE2APbXHA/rBMP0Ab7kxBhz3P6aAyzE+vgYbIKhPX0SDO0pIg8AtwL3Grvz1E1QtKcPOYOiPV38HzDZw/KgaE9bVRmDrS29ClRBXww8YH//ALDIwzq+TDFQ17zmFJFGIhJf+T3WiapgnDUyGNrTq2BoT7Fu4PI0MN4YU1TFao63py85g6Q9u7k8HQ987mE1R9vTl4zB0JY1VgdnlOdhTZtbivVX+CGgBbAS2G9/bW6v2xZY6vLeW4B9WGe/f1aXZ4NrmxPrrPyn9mO3Qzkn2d+fB74C0oO0Pb3mDJL2zMbqz91uP2YFaXt6zRkk7fk+VuHbAfwbaOdke9Y2Y6Db0h8PvfRfKaXCRDCdFFVKKXUZtKArpVSY0IKulFJhQgu6UkqFCS3oSikVJrSgBxER+ZWIOHZndBF5SkSGO7X/6ohIGxFZKiL5ImKCNWd1RKSxnf1BL+t1EpF/ishhESkWkSMisqimVymKyIP2/hrX8H1r7PcZESkTkS9E5BURSfDhvQdF5A812Z/yH6+3oFP1ylPAX7BmRww2PwOuBe7Bmmhpj7Nx6oZYUzZvxBo3/VOsqyeTsC5+GQSsC1CU1cB/Y9WI/sDzWFd23uHlfZOAr+s2mqqKFnRVYyISZ4w5F+Dd9gA2GWOWXu6GRCTWGFPsh0x14Q6gNXCtsS43r/Q3EfE0/0ldOW2M2Wh/v96+UvJ5EUk0xpxyX7nyd8IY80kAMyo32uUSYkTkJhHZZH8U/0pEXnP/SC0ivUVkg73ObhG5RUSyROTv1Wz3INaVss+6fNwebr9mRORHIvJHETkF7LSXjxPrRiA5IlIgIhtFZLTbdn8lIrkicp39epGIfCIiQ93WG2/PaFcoInn2zziscv/ASGCSneWgy/vuFOsGBOftronfikiUy+uV3Q4D7K6Ec8CTLstT7OVFYt3EIMW+5PtvdvfOARG5x0N7TbDbtFhETorI70Uk2m2dySKyT0TOicg6rD9K3jQFSrh0ameMy1WAIjJIRBaLyHG7zbaLyL3eNi4isXbWI3abfSoit/iQa6v9NUlEkuy2u1dE/iEi32Bdbemxy0VEbhSR1SJy1m7TNSJyncvrHUVkvoictv8d0kWkuw+ZlBst6CFERHoBy4FcrMmEngW+A7znsk5DIB2Iw+qe+A3wCtDRy+YnAfnAW1gf7QcB21xefxJoA3wX64YAAJ2x/iN/186zAVgmIkPctt0Qa3751+31zgML7ayISLL9M6wCbsOazjQNaG6/fxDwCVY3wCA7K/Yfj3fsnBOAPwM/weo2cjfP3uYt9tdKc+3XJmPNAPie3QbHsY6WNwH/EJELc4+LyJ3AB8BmrK6Q54BpwEyXdVLsbJ8Ct2PNU7LAQy5324AGwD9FpK+IVPV/tBPwMfB9rDZ7H+so/pI/Pm7eAx4EXrDftwVYLCJ9vLwvyf560mXZH4AzwBR7e5ewDwpWYl12/wBwF/AR9lS5ItIcWI91I5TpwJ1Y85Nnikicl0zKndNzD+jj2wfwKyC3mtfnY80zE+my7E6saUcH2c8fwzrCa+eyzgB7nb972X8u8CsPyw3wiZf3RmB14aUDc9x+JgPc5LKsj71srP38DuBrL9tfA7zntmwjsNpt2VNAOdDefv6gva8n3NarXP6Ay7Jb7GWu+ZtgFaNH7OcCHAL+5ra9qcA5oIX9fAFWP7+4rPMze/sPevlZXwYq7HULsIr1qGrWF7vtXwdWefgZG9vPR9rPh7m9fx3wrltbv29vswEwFGsOlC32vpLs7Sz0kOUg8AeX5/8BslzbwW3957H63Ju7LGuGdXDxWCD+34XTQ4/QQ8sArP9E5S7L3seaG/sG+3l/YKsx5ljlCsaYzViTY12OJe4LRKS9iMwVkWN2hlKsGemudFu1lItPtFae0Kw86t0JNLG3NVqs/tpqiUgk1m3F3nV76R2sPy6DvOW3rXT5Ptv+uqpygTEmHzjFtzdfuBLr084CEYmqfNjviQWuttcbACw2doWyfeDt57L3+SN7P09itdtYrJssTK9cR0SaicifROQQVvuWYn1KcG97V6OwjrA/dsu+EnC/5+vt9jaLsQr+QS6dsreqNq3M2Ai4Hpjr9j73TBlAgUueM1hdPFXeh1Z5pgU9tLTBrTDbxf1rvu2euAKrALnztKwmLtqv3RWwGBiMdbeXEVh/TJZhFTZXBcaYCpfMJfa3sfbzvVhdJl2ApUCuiPyfiCRWk6clEO2ey+V58yqWu/vG5fsSD8sql1f+TC3tr0v5tpCWAl/ayyvn+L6CS+fT9zi/vifGmGxjzB+MMeOxule2Ay+IXDgx+nes7ouXsP6I9gfmcGnbu2pp5yp1e/yKi+cmB+sPVH/gOqyj5xuMMfvc1vF2kNAM64j+hJdMd3nINMJDJuWFjnIJLZfcFs8+Um3BtyfRTmL1R7qrrjj6wv0IqyvWf/abjTHLXfLUqt/TGLMEWCIiTYBxwB+x+sTvruItuVj/8d1vE9ja/up+UtFf04pWbncaVr++u8rCfpJLs1V1S8NqGWNyReRvwJ+AViKSj9VGjxtjZlWuV01/e6XTwDF8u1dunjHG213uvbVpHlbXURsvmRZjdb24O+Nl+8qNHqGHlk1YIz0iXZbdjvWHeb39fAvQT0Qu3J9RRAbwbaGrjuuRqDeVhfu8y346Ae4nRGvEGJNvjPk/rLvD9KpmvXKsj+VT3F66E6uI/OdyclRjL1ZRTDLGZHl4VI7B3gKMdzmiBuvfqlrVfCrphtXW+Vj92pFc3PbxWCdoq7MS6wj9rKfs3rLVlDGmEOt39n63dnDPdBWw20OmoLrBeCjQI/TgEyMini7eWIs1YuUT4EMR+StWH/SLWDeOqCxgfwN+DqSJyHNYhfc5rC6Xiku2erHPgXEishw4C+w1xlR1lPQ51omy/xGRXwDx9n6OVbF+lUTkB1h93suxRpd0wyrU//Dy1meBdPvodT5wDdaR3hvGmKM1zeELY0yFiPwYaxRKAlYXUwlWd9FE4A5j3U3oRaxitkBE3sLqW3/Ih108YA8//AfWCJlorJOZjwJ/Ndb4+WIR2QL8UkQKsP5dn8Eq9tVdzZmBddI6Q0RexLppQwLWSepYY8xPfW4I3z0DZGKNfpoNFGL9W2cZY9KwTgDfB6wSkT9j/f60BoYB640x8+ogU/hy+qysPr598O2IEE+P4fY6I7EKRTFWn+xr2KMYXLZzLdYQwvNYR5QTse4M80cv+++LNXKk0G2fBuvjvfv6/bGG7p3DGn3zIFbfbpbbz3TJyB3XbWL9B1+CVcyLsbotXgQauKy/BrdRLvbyu7BOqpZg/YH5LRDl8vqDuIz0qG45347euNVt3YO4jNywl92MNfyuEGskynasP7iu+56CdaK1GOsTVH+8jHLB+lTyKtaJ4zNY/flbgUfctt0Vq5+7EDiMNbrnorau4mdsgPWHN9tus5NYf0jHeWtrb+1UTVsNwzqxWmT/PKuBPi6vt8U6EPkK63f2IPAv4Cqn/0+G2kPvWFQPiEhnrII+zRjzN6fzKKXqhhb0MCQilXOAHMIaYvdTrPHUPYwxBU5mU0rVHe1DD08Gq3+5LdZH2I+An2gxVyq86RG6UkqFCR22qJRSYUILulJKhQkt6EopFSa0oCulVJjQgq6UUmHi/wO2j8qD4aj1qwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "SP_Log_Trnsfmd = train_df[\"SalePrice\"].copy()\n",
    "SP_Log_Trnsfmd = SP_Log_Trnsfmd.apply(lambda x: np.log1p(x))\n",
    "sns.distplot(SP_Log_Trnsfmd, kde=False, fit=stats.norm, color = \"red\")\n",
    "plt.xlabel(\"Log transformed SalePrice\", size = 15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log transformation demonstration performed on \"SalePrice\". As the distribution of the dependent variable looks normal, a Linear model can be fit on the variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 important features as per correlation values and SalePrice:\n",
      "OverallQual     0.790982\n",
      "GrLivArea       0.708624\n",
      "GarageCars      0.640409\n",
      "GarageArea      0.623431\n",
      "TotalBsmtSF     0.613581\n",
      "1stFlrSF        0.605852\n",
      "FullBath        0.560664\n",
      "TotRmsAbvGrd    0.533723\n",
      "YearBuilt       0.522897\n",
      "YearRemodAdd    0.507101\n",
      "GarageYrBlt     0.486362\n",
      "MasVnrArea      0.477493\n",
      "Fireplaces      0.466929\n",
      "BsmtFinSF1      0.386420\n",
      "LotFrontage     0.351799\n",
      "WoodDeckSF      0.324413\n",
      "2ndFlrSF        0.319334\n",
      "OpenPorchSF     0.315856\n",
      "HalfBath        0.284108\n",
      "LotArea         0.263843\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Top 20 important features as per correlation values and SalePrice:\")\n",
    "corr = abs(train_df.corr())\n",
    "corr.sort_values(['SalePrice'], ascending=False, inplace=True)\n",
    "print(corr.SalePrice[1:21])\n",
    "Imp_feat_df = pd.DataFrame(corr.SalePrice[1:11])\n",
    "Imp_feat_df.rename(columns = {\"SalePrice\": \"Importance\"}, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = list(Imp_feat_df.index) \n",
    "y = list(Imp_feat_df['Importance'])  \n",
    "fig = plt.figure(figsize = (12, 5)) \n",
    "plt.bar(x, y, color ='blue',  \n",
    "        width = 0.4)\n",
    "plt.xlabel(\"Features\", size=15) \n",
    "plt.ylabel(\"Importance\", size=15) \n",
    "plt.title(\"Top 10 features with correlation sorted by SalePrice\", size=15) \n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above plot is demonstrating the top 10 important features w.r.t their correlation factors against the y-variable SalePrice.\n",
    "This is a correlation sorted by SalePrice values, which is our dependent variable. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 important features and their distributions:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x900 with 30 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(\"Top 5 important features and their distributions:\")\n",
    "Imp_feat_df[\"Feature\"] = Imp_feat_df.index\n",
    "Imp_feat_df.set_index(np.arange(1,11))\n",
    "important_feats = [i for i in Imp_feat_df[\"Feature\"]]\n",
    "sns.set()\n",
    "cols = important_feats[:5]\n",
    "sns.pairplot(train_df[cols], height = 2.5)\n",
    "plt.show();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* OverallQual and GrLivArea are strongly correlated with the target variable with 0.82 and 0.7.\n",
    "* GarageCars, GarageArea, TotalBsmtSF, etc. follows the top 2 important variables w.r.t SalePrice.\n",
    "* GarageCars and GarageArea follow an almost similar correlation w.r.t SalePrice. More number of cars one would grab, much larger space that individual would need. That would iventually play an important role deciding the price of his/her plot.\n",
    "* Higher the quality of a house overall OverallQual, higher is the price of a house. The OverallQual has a high correlation with the target variable.\n",
    "* The presence of outliers are prominent in every features. Some of them are not showing a linear relationship as well. The ouliers are making those features important.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 20 important Discrete variables as per correlation values and SalePrice:\n",
      "OverallQual     0.790982\n",
      "GarageCars      0.640409\n",
      "FullBath        0.560664\n",
      "TotRmsAbvGrd    0.533723\n",
      "Fireplaces      0.466929\n",
      "HalfBath        0.284108\n",
      "BsmtFullBath    0.227122\n",
      "BedroomAbvGr    0.168213\n",
      "KitchenAbvGr    0.135907\n",
      "PoolArea        0.092404\n",
      "MSSubClass      0.084284\n",
      "OverallCond     0.077856\n",
      "MoSold          0.046432\n",
      "3SsnPorch       0.044584\n",
      "YrSold          0.028923\n",
      "LowQualFinSF    0.025606\n",
      "MiscVal         0.021190\n",
      "BsmtHalfBath    0.016844\n",
      "Name: SalePrice, dtype: float64\n",
      "\n",
      "Top 20 important Mumeric variables as per correlation values and SalePrice:\n",
      "GrLivArea        0.708624\n",
      "GarageArea       0.623431\n",
      "TotalBsmtSF      0.613581\n",
      "1stFlrSF         0.605852\n",
      "YearBuilt        0.522897\n",
      "YearRemodAdd     0.507101\n",
      "GarageYrBlt      0.486362\n",
      "MasVnrArea       0.477493\n",
      "BsmtFinSF1       0.386420\n",
      "LotFrontage      0.351799\n",
      "WoodDeckSF       0.324413\n",
      "2ndFlrSF         0.319334\n",
      "OpenPorchSF      0.315856\n",
      "LotArea          0.263843\n",
      "BsmtUnfSF        0.214479\n",
      "EnclosedPorch    0.128578\n",
      "ScreenPorch      0.111447\n",
      "BsmtFinSF2       0.011378\n",
      "Name: SalePrice, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "def imp_feat(name, vars_ls):\n",
    "    print(f\"Top 20 important {name} as per correlation values and SalePrice:\")\n",
    "    cols = vars_ls\n",
    "    cols.append('SalePrice')\n",
    "    for c in cols:\n",
    "        if c == 'SalePrice' and cols.count(c) > 1:\n",
    "            cols.pop()\n",
    "    corr = abs(train_df[cols].corr())\n",
    "    corr.sort_values(['SalePrice'], ascending=False, inplace=True)\n",
    "    print(corr.SalePrice[1:21])\n",
    "imp_feat(\"Discrete variables\", numeric_discrete_vars)\n",
    "print()\n",
    "imp_feat(\"Mumeric variables\", numeric_vars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Top 20 highly correlated features w.r.t SalePrice demonstrated seperately as per column data types. We can estimate the important features from the values above shown seperately. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Data cleaning and pre-processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df[categorical_vars] = train_df[categorical_vars].fillna('None')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NULL values in the categorical variables are replaced with None in order to fill the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in numeric_vars:\n",
    "    train_df[col].fillna(train_df[col].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NULL values in the numeric variables are replaced with Median of each variables in order to fill the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in numeric_discrete_vars:\n",
    "    train_df[col].fillna(train_df[col].median(),inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The NULL values in the discrete variables are replaced with Median of each variables in order to fill the missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.12134661989685333"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df[\"SalePrice\"] = train_df[\"SalePrice\"].apply(lambda x: np.log1p(x))\n",
    "train_df[\"SalePrice\"].skew()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dependent variable SalePrice has been Log transformed in order to remove skewness from it.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in ['YearBuilt', 'YearRemodAdd', 'GarageYrBlt']:  \n",
    "    train_df[col] = train_df['YrSold'] - train_df[col]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Replacing year related variables with related age in values. The data of getting sold YrSold column is marked as the end date. This is done to gain some coefficient values from those columns. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in numeric_vars:\n",
    "    train_df[col] = train_df[col].apply(lambda x: np.log1p(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Log transform on the numeric features as well in order to remove skewness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique values in all the categorical variables:\n",
      "MSZoning -> [0, 1, 2, 3, 4]\n",
      "Street -> [0, 1]\n",
      "Alley -> [0, 1, 2]\n",
      "LotShape -> [0, 1, 2, 3]\n",
      "LandContour -> [0, 1, 2, 3]\n",
      "Utilities -> [0, 1]\n",
      "LotConfig -> [0, 1, 2, 3, 4]\n",
      "LandSlope -> [0, 1, 2]\n",
      "Neighborhood -> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24]\n",
      "Condition1 -> [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
      "Condition2 -> [0, 1, 2, 3, 4, 5, 6, 7]\n",
      "BldgType -> [0, 1, 2, 3, 4]\n",
      "HouseStyle -> [0, 1, 2, 3, 4, 5, 6, 7]\n",
      "RoofStyle -> [0, 1, 2, 3, 4, 5]\n",
      "RoofMatl -> [0, 1, 2, 3, 4, 5, 6, 7]\n",
      "Exterior1st -> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14]\n",
      "Exterior2nd -> [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]\n",
      "MasVnrType -> [0, 1, 2, 3]\n",
      "ExterQual -> [0, 1, 2, 3]\n",
      "ExterCond -> [0, 1, 2, 3, 4]\n",
      "Foundation -> [0, 1, 2, 3, 4, 5]\n",
      "BsmtQual -> [0, 1, 2, 3, 4]\n",
      "BsmtCond -> [0, 1, 2, 3, 4]\n",
      "BsmtExposure -> [0, 1, 2, 3, 4]\n",
      "BsmtFinType1 -> [0, 1, 2, 3, 4, 5, 6]\n",
      "BsmtFinType2 -> [0, 1, 2, 3, 4, 5, 6]\n",
      "Heating -> [0, 1, 2, 3, 4, 5]\n",
      "HeatingQC -> [0, 1, 2, 3, 4]\n",
      "CentralAir -> [0, 1]\n",
      "Electrical -> [0, 1, 2, 3, 4, 5]\n",
      "KitchenQual -> [0, 1, 2, 3]\n",
      "Functional -> [0, 1, 2, 3, 4, 5, 6]\n",
      "FireplaceQu -> [0, 1, 2, 3, 4, 5]\n",
      "GarageType -> [0, 1, 2, 3, 4, 5, 6]\n",
      "GarageFinish -> [0, 1, 2, 3]\n",
      "GarageQual -> [0, 1, 2, 3, 4, 5]\n",
      "GarageCond -> [0, 1, 2, 3, 4, 5]\n",
      "PavedDrive -> [0, 1, 2]\n",
      "PoolQC -> [0, 1, 2, 3]\n",
      "Fence -> [0, 1, 2, 3, 4]\n",
      "MiscFeature -> [0, 1, 2, 3, 4]\n",
      "SaleType -> [0, 1, 2, 3, 4, 5, 6, 7, 8]\n",
      "SaleCondition -> [0, 1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "print(\"Unique values in all the categorical variables:\")\n",
    "_encoder = preprocessing.LabelEncoder() \n",
    "for col in categorical_vars:  \n",
    "    train_df[col] = _encoder.fit_transform(train_df[col])\n",
    "    unique = list(set(train_df[col]))\n",
    "    print(f\"{col} -> {unique}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Applying label encoding method on all the category features to convert each label into numeric discrete values. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.23529412, 0.75      , 0.41326841, ..., 0.5       , 1.        ,\n",
       "        0.8       ],\n",
       "       [0.        , 0.75      , 0.49030656, ..., 0.25      , 1.        ,\n",
       "        0.8       ],\n",
       "       [0.23529412, 0.75      , 0.42998996, ..., 0.5       , 1.        ,\n",
       "        0.8       ],\n",
       "       ...,\n",
       "       [0.29411765, 0.75      , 0.41892525, ..., 1.        , 1.        ,\n",
       "        0.8       ],\n",
       "       [0.        , 0.75      , 0.42998996, ..., 1.        , 1.        ,\n",
       "        0.8       ],\n",
       "       [0.        , 0.75      , 0.46633838, ..., 0.5       , 1.        ,\n",
       "        0.8       ]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "train_df.fillna(train_df.mean(), inplace=True)\n",
    "scale_feats = [cols for cols in train_df.columns if cols != 'SalePrice']\n",
    "scale = MinMaxScaler()\n",
    "scale.fit(train_df[scale_feats])\n",
    "scale.transform(train_df[scale_feats])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Min-Max Scaling is applied on the data set to scale all the independent variables under an unified standard. This is to avoid variation in values non-uniformity in units.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tranformed and prepared data set:\n"
     ]
    },
    {
     "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>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>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</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>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>0.235294</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.413268</td>\n",
       "      <td>0.366271</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.364180</td>\n",
       "      <td>0.435859</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.716038</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.759508</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.646838</td>\n",
       "      <td>0.774668</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.355839</td>\n",
       "      <td>0.884420</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.577436</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.382680</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.869159</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.654449</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.583824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.490307</td>\n",
       "      <td>0.391245</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.704421</td>\n",
       "      <td>0.843065</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.797188</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.728731</td>\n",
       "      <td>0.819153</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.502753</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.469935</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.740205</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.845088</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.843935</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.573300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.429990</td>\n",
       "      <td>0.422289</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.422652</td>\n",
       "      <td>0.473357</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.690361</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.716357</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.783246</td>\n",
       "      <td>0.782930</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.383123</td>\n",
       "      <td>0.886245</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.592825</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.444123</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.883450</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.596422</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.589054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.294118</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.383633</td>\n",
       "      <td>0.390223</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.919066</td>\n",
       "      <td>0.878382</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.622780</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.811361</td>\n",
       "      <td>0.760436</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.399623</td>\n",
       "      <td>0.868471</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.578882</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.469279</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.890935</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.568247</td>\n",
       "      <td>0.888227</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.553297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.508439</td>\n",
       "      <td>0.468694</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.625000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.446592</td>\n",
       "      <td>0.534491</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.794318</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.750841</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.798858</td>\n",
       "      <td>0.808002</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.465926</td>\n",
       "      <td>0.911832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666289</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.469279</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.927266</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.779126</td>\n",
       "      <td>0.704481</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.597433</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.176471</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.512839</td>\n",
       "      <td>0.466694</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.458333</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.575859</td>\n",
       "      <td>0.658753</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.763689</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.538171</td>\n",
       "      <td>0.766342</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.328342</td>\n",
       "      <td>0.830611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.496917</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.605112</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.850939</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.549784</td>\n",
       "      <td>0.544535</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.629921</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.045161</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.554946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.466338</td>\n",
       "      <td>0.400872</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.875000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.777778</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.281768</td>\n",
       "      <td>0.267245</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.708978</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.836088</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.742856</td>\n",
       "      <td>0.852356</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.614202</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.574109</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.296082</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.889643</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.820948</td>\n",
       "      <td>0.643873</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.612611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.435403</td>\n",
       "      <td>0.406573</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.583333</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.733929</td>\n",
       "      <td>0.878382</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.743360</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.782186</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.479209</td>\n",
       "      <td>0.693588</td>\n",
       "      <td>0.804134</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.453151</td>\n",
       "      <td>0.902829</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.648456</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.771212</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.852080</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.808905</td>\n",
       "      <td>0.844081</td>\n",
       "      <td>0.860398</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.022581</td>\n",
       "      <td>0.909091</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.580677</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.176471</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.323585</td>\n",
       "      <td>0.303132</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.708333</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.885513</td>\n",
       "      <td>0.991891</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.884356</td>\n",
       "      <td>0.786847</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.422914</td>\n",
       "      <td>0.867777</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.590440</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.930497</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.847458</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.667820</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.843638</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.272727</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.547453</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.316280</td>\n",
       "      <td>0.340830</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.863519</td>\n",
       "      <td>0.991891</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.781104</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.638004</td>\n",
       "      <td>0.791448</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.442752</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.413851</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.907385</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.734099</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.255212</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.539904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.440738</td>\n",
       "      <td>0.421417</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.769147</td>\n",
       "      <td>0.920532</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.788345</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.632398</td>\n",
       "      <td>0.796979</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.429522</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.401484</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.808219</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.820265</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.090909</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.547212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.235294</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.512839</td>\n",
       "      <td>0.433677</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.714286</td>\n",
       "      <td>0.888889</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.140884</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.767035</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.799529</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.668046</td>\n",
       "      <td>0.810966</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.477963</td>\n",
       "      <td>0.922451</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.686019</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.148041</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.909735</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.739823</td>\n",
       "      <td>0.490153</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.621134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.435403</td>\n",
       "      <td>0.450105</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.782553</td>\n",
       "      <td>0.936576</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.428571</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.764476</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666590</td>\n",
       "      <td>0.781929</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.379818</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.355025</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.822307</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.808309</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.732650</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.838125</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.727273</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.555487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.538208</td>\n",
       "      <td>0.411598</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.208333</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.140884</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.776166</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.942405</td>\n",
       "      <td>0.838496</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.566637</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.529649</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.416667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.148041</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.927923</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.752288</td>\n",
       "      <td>0.559183</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.636364</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.605704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.435403</td>\n",
       "      <td>0.416461</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.791023</td>\n",
       "      <td>0.946714</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.726621</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.763847</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.806504</td>\n",
       "      <td>0.818332</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.500043</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.467402</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.831207</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.808309</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.850895</td>\n",
       "      <td>0.819613</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.562177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.147059</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.323585</td>\n",
       "      <td>0.303132</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.125000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.875</td>\n",
       "      <td>0.888103</td>\n",
       "      <td>0.473357</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.867005</td>\n",
       "      <td>0.771410</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.354954</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.331784</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.605112</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.876012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.576173</td>\n",
       "      <td>0.749632</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.545455</td>\n",
       "      <td>0.25</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.548708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.435403</td>\n",
       "      <td>0.422132</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.555556</td>\n",
       "      <td>0.750</td>\n",
       "      <td>0.754794</td>\n",
       "      <td>0.903353</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.928571</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.704558</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.736388</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.780853</td>\n",
       "      <td>0.792942</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.416189</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.389021</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.793137</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.850939</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.045161</td>\n",
       "      <td>0.181818</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.558135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.411765</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.451188</td>\n",
       "      <td>0.414135</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.791667</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.749775</td>\n",
       "      <td>0.897347</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.571429</td>\n",
       "      <td>0.533333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.512816</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.479341</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.787863</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.860884</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.032258</td>\n",
       "      <td>0.818182</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.518307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.418925</td>\n",
       "      <td>0.460781</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.500</td>\n",
       "      <td>0.327123</td>\n",
       "      <td>0.391507</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.857143</td>\n",
       "      <td>0.866667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.749242</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.792948</td>\n",
       "      <td>0.804856</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.455537</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.425801</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.343740</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.876012</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.734939</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.454545</td>\n",
       "      <td>0.50</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.8</td>\n",
       "      <td>2.563153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.440738</td>\n",
       "      <td>0.344488</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.250</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.285714</td>\n",
       "      <td>0.444444</td>\n",
       "      <td>0.625</td>\n",
       "      <td>0.803101</td>\n",
       "      <td>0.925998</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.142857</td>\n",
       "      <td>0.214286</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.720558</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.807736</td>\n",
       "      <td>0.795760</td>\n",
       "      <td>0.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.525172</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.490890</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.375</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.6</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>0.843899</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.783577</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</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>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.25</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.363636</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.552739</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    MSSubClass  MSZoning  LotFrontage   LotArea  Street  Alley  LotShape  \\\n",
       "0     0.235294      0.75     0.413268  0.366271     1.0    0.5  1.000000   \n",
       "1     0.000000      0.75     0.490307  0.391245     1.0    0.5  1.000000   \n",
       "2     0.235294      0.75     0.429990  0.422289     1.0    0.5  0.000000   \n",
       "3     0.294118      0.75     0.383633  0.390223     1.0    0.5  0.000000   \n",
       "4     0.235294      0.75     0.508439  0.468694     1.0    0.5  0.000000   \n",
       "5     0.176471      0.75     0.512839  0.466694     1.0    0.5  0.000000   \n",
       "6     0.000000      0.75     0.466338  0.400872     1.0    0.5  1.000000   \n",
       "7     0.235294      0.75     0.435403  0.406573     1.0    0.5  0.000000   \n",
       "8     0.176471      1.00     0.323585  0.303132     1.0    0.5  1.000000   \n",
       "9     1.000000      0.75     0.316280  0.340830     1.0    0.5  1.000000   \n",
       "10    0.000000      0.75     0.440738  0.421417     1.0    0.5  1.000000   \n",
       "11    0.235294      0.75     0.512839  0.433677     1.0    0.5  0.000000   \n",
       "12    0.000000      0.75     0.435403  0.450105     1.0    0.5  0.333333   \n",
       "13    0.000000      0.75     0.538208  0.411598     1.0    0.5  0.000000   \n",
       "14    0.000000      0.75     0.435403  0.416461     1.0    0.5  0.000000   \n",
       "15    0.147059      1.00     0.323585  0.303132     1.0    0.5  1.000000   \n",
       "16    0.000000      0.75     0.435403  0.422132     1.0    0.5  0.000000   \n",
       "17    0.411765      0.75     0.451188  0.414135     1.0    0.5  1.000000   \n",
       "18    0.000000      0.75     0.418925  0.460781     1.0    0.5  1.000000   \n",
       "19    0.000000      0.75     0.440738  0.344488     1.0    0.5  1.000000   \n",
       "\n",
       "    LandContour  Utilities  LotConfig  LandSlope  Neighborhood  Condition1  \\\n",
       "0           1.0        0.0       1.00        0.0      0.208333       0.250   \n",
       "1           1.0        0.0       0.50        0.0      1.000000       0.125   \n",
       "2           1.0        0.0       1.00        0.0      0.208333       0.250   \n",
       "3           1.0        0.0       0.00        0.0      0.250000       0.250   \n",
       "4           1.0        0.0       0.50        0.0      0.625000       0.250   \n",
       "5           1.0        0.0       1.00        0.0      0.458333       0.250   \n",
       "6           1.0        0.0       1.00        0.0      0.875000       0.250   \n",
       "7           1.0        0.0       0.00        0.0      0.583333       0.500   \n",
       "8           1.0        0.0       1.00        0.0      0.708333       0.000   \n",
       "9           1.0        0.0       0.00        0.0      0.125000       0.000   \n",
       "10          1.0        0.0       1.00        0.0      0.791667       0.250   \n",
       "11          1.0        0.0       1.00        0.0      0.666667       0.250   \n",
       "12          1.0        0.0       1.00        0.0      0.791667       0.250   \n",
       "13          1.0        0.0       1.00        0.0      0.208333       0.250   \n",
       "14          1.0        0.0       0.00        0.0      0.500000       0.250   \n",
       "15          1.0        0.0       0.00        0.0      0.125000       0.250   \n",
       "16          1.0        0.0       0.25        0.0      0.500000       0.250   \n",
       "17          1.0        0.0       1.00        0.0      0.791667       0.250   \n",
       "18          1.0        0.0       1.00        0.0      0.833333       0.625   \n",
       "19          1.0        0.0       1.00        0.0      0.500000       0.250   \n",
       "\n",
       "    Condition2  BldgType  HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0     0.285714      0.00    0.714286     0.666667        0.500   0.364180   \n",
       "1     0.285714      0.00    0.285714     0.555556        0.875   0.704421   \n",
       "2     0.285714      0.00    0.714286     0.666667        0.500   0.422652   \n",
       "3     0.285714      0.00    0.714286     0.666667        0.500   0.919066   \n",
       "4     0.285714      0.00    0.714286     0.777778        0.500   0.446592   \n",
       "5     0.285714      0.00    0.000000     0.444444        0.500   0.575859   \n",
       "6     0.285714      0.00    0.285714     0.777778        0.500   0.281768   \n",
       "7     0.285714      0.00    0.714286     0.666667        0.625   0.733929   \n",
       "8     0.285714      0.00    0.000000     0.666667        0.500   0.885513   \n",
       "9     0.000000      0.25    0.142857     0.444444        0.625   0.863519   \n",
       "10    0.285714      0.00    0.285714     0.444444        0.500   0.769147   \n",
       "11    0.285714      0.00    0.714286     0.888889        0.500   0.140884   \n",
       "12    0.285714      0.00    0.285714     0.444444        0.625   0.782553   \n",
       "13    0.285714      0.00    0.285714     0.666667        0.500   0.140884   \n",
       "14    0.285714      0.00    0.285714     0.555556        0.500   0.791023   \n",
       "15    0.285714      0.00    0.142857     0.666667        0.875   0.888103   \n",
       "16    0.285714      0.00    0.285714     0.555556        0.750   0.754794   \n",
       "17    0.285714      0.50    0.285714     0.333333        0.500   0.749775   \n",
       "18    0.285714      0.00    0.285714     0.444444        0.500   0.327123   \n",
       "19    0.285714      0.00    0.285714     0.444444        0.625   0.803101   \n",
       "\n",
       "    YearRemodAdd  RoofStyle  RoofMatl  Exterior1st  Exterior2nd  MasVnrType  \\\n",
       "0       0.435859        0.2  0.142857     0.857143     0.866667    0.333333   \n",
       "1       0.843065        0.2  0.142857     0.571429     0.533333    0.666667   \n",
       "2       0.473357        0.2  0.142857     0.857143     0.866667    0.333333   \n",
       "3       0.878382        0.2  0.142857     0.928571     1.000000    0.666667   \n",
       "4       0.534491        0.2  0.142857     0.857143     0.866667    0.333333   \n",
       "5       0.658753        0.2  0.142857     0.857143     0.866667    0.666667   \n",
       "6       0.267245        0.2  0.142857     0.857143     0.866667    1.000000   \n",
       "7       0.878382        0.2  0.142857     0.428571     0.400000    1.000000   \n",
       "8       0.991891        0.2  0.142857     0.214286     1.000000    0.666667   \n",
       "9       0.991891        0.2  0.142857     0.571429     0.533333    0.666667   \n",
       "10      0.920532        0.6  0.142857     0.428571     0.400000    0.666667   \n",
       "11      0.000000        0.6  0.142857     1.000000     1.000000    1.000000   \n",
       "12      0.936576        0.6  0.142857     0.428571     0.666667    0.666667   \n",
       "13      0.000000        0.2  0.142857     0.857143     0.866667    1.000000   \n",
       "14      0.946714        0.6  0.142857     0.571429     0.533333    0.333333   \n",
       "15      0.473357        0.2  0.142857     0.928571     0.933333    0.666667   \n",
       "16      0.903353        0.2  0.142857     0.928571     0.933333    0.333333   \n",
       "17      0.897347        0.2  0.142857     0.571429     0.533333    0.666667   \n",
       "18      0.391507        0.2  0.142857     0.857143     0.866667    0.666667   \n",
       "19      0.925998        0.6  0.142857     0.214286     0.666667    0.666667   \n",
       "\n",
       "    MasVnrArea  ExterQual  ExterCond  Foundation  BsmtQual  BsmtCond  \\\n",
       "0     0.716038   0.666667        1.0         0.4      0.50      1.00   \n",
       "1     0.000000   1.000000        1.0         0.2      0.50      1.00   \n",
       "2     0.690361   0.666667        1.0         0.4      0.50      1.00   \n",
       "3     0.000000   1.000000        1.0         0.0      1.00      0.25   \n",
       "4     0.794318   0.666667        1.0         0.4      0.50      1.00   \n",
       "5     0.000000   1.000000        1.0         1.0      0.50      1.00   \n",
       "6     0.708978   0.666667        1.0         0.4      0.00      1.00   \n",
       "7     0.743360   1.000000        1.0         0.2      0.50      1.00   \n",
       "8     0.000000   1.000000        1.0         0.0      1.00      1.00   \n",
       "9     0.000000   1.000000        1.0         0.0      1.00      1.00   \n",
       "10    0.000000   1.000000        1.0         0.2      1.00      1.00   \n",
       "11    0.767035   0.000000        1.0         0.4      0.00      1.00   \n",
       "12    0.000000   1.000000        1.0         0.2      1.00      1.00   \n",
       "13    0.776166   0.666667        1.0         0.4      0.50      1.00   \n",
       "14    0.726621   1.000000        1.0         0.2      1.00      1.00   \n",
       "15    0.000000   1.000000        1.0         0.0      1.00      1.00   \n",
       "16    0.704558   1.000000        1.0         0.2      1.00      1.00   \n",
       "17    0.000000   1.000000        1.0         0.6      0.75      0.50   \n",
       "18    0.000000   1.000000        1.0         0.4      1.00      1.00   \n",
       "19    0.000000   1.000000        1.0         0.2      1.00      1.00   \n",
       "\n",
       "    BsmtExposure  BsmtFinType1  BsmtFinSF1  BsmtFinType2  BsmtFinSF2  \\\n",
       "0           0.75      0.333333    0.759508      1.000000    0.000000   \n",
       "1           0.25      0.000000    0.797188      1.000000    0.000000   \n",
       "2           0.50      0.333333    0.716357      1.000000    0.000000   \n",
       "3           0.75      0.000000    0.622780      1.000000    0.000000   \n",
       "4           0.00      0.333333    0.750841      1.000000    0.000000   \n",
       "5           0.75      0.333333    0.763689      1.000000    0.000000   \n",
       "6           0.00      0.333333    0.836088      1.000000    0.000000   \n",
       "7           0.50      0.000000    0.782186      0.166667    0.479209   \n",
       "8           0.75      1.000000    0.000000      1.000000    0.000000   \n",
       "9           0.75      0.333333    0.781104      1.000000    0.000000   \n",
       "10          0.75      0.833333    0.788345      1.000000    0.000000   \n",
       "11          0.75      0.333333    0.799529      1.000000    0.000000   \n",
       "12          0.75      0.000000    0.764476      1.000000    0.000000   \n",
       "13          0.00      1.000000    0.000000      1.000000    0.000000   \n",
       "14          0.75      0.166667    0.763847      1.000000    0.000000   \n",
       "15          0.75      1.000000    0.000000      1.000000    0.000000   \n",
       "16          0.75      0.000000    0.736388      1.000000    0.000000   \n",
       "17          1.00      0.666667    0.000000      0.666667    0.000000   \n",
       "18          0.75      0.333333    0.749242      1.000000    0.000000   \n",
       "19          0.75      0.500000    0.720558      1.000000    0.000000   \n",
       "\n",
       "    BsmtUnfSF  TotalBsmtSF  Heating  HeatingQC  CentralAir  Electrical  \\\n",
       "0    0.646838     0.774668      0.2        0.0         1.0         1.0   \n",
       "1    0.728731     0.819153      0.2        0.0         1.0         1.0   \n",
       "2    0.783246     0.782930      0.2        0.0         1.0         1.0   \n",
       "3    0.811361     0.760436      0.2        0.5         1.0         1.0   \n",
       "4    0.798858     0.808002      0.2        0.0         1.0         1.0   \n",
       "5    0.538171     0.766342      0.2        0.0         1.0         1.0   \n",
       "6    0.742856     0.852356      0.2        0.0         1.0         1.0   \n",
       "7    0.693588     0.804134      0.2        0.0         1.0         1.0   \n",
       "8    0.884356     0.786847      0.2        0.5         1.0         0.2   \n",
       "9    0.638004     0.791448      0.2        0.0         1.0         1.0   \n",
       "10   0.632398     0.796979      0.2        0.0         1.0         1.0   \n",
       "11   0.668046     0.810966      0.2        0.0         1.0         1.0   \n",
       "12   0.666590     0.781929      0.2        1.0         1.0         1.0   \n",
       "13   0.942405     0.838496      0.2        0.0         1.0         1.0   \n",
       "14   0.806504     0.818332      0.2        1.0         1.0         1.0   \n",
       "15   0.867005     0.771410      0.2        0.0         1.0         0.0   \n",
       "16   0.780853     0.792942      0.2        0.0         1.0         1.0   \n",
       "17   0.000000     0.000000      0.2        1.0         1.0         1.0   \n",
       "18   0.792948     0.804856      0.2        0.0         1.0         1.0   \n",
       "19   0.807736     0.795760      0.2        1.0         1.0         1.0   \n",
       "\n",
       "    1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  \\\n",
       "0   0.355839  0.884420           0.0   0.577436      0.333333           0.0   \n",
       "1   0.502753  0.000000           0.0   0.469935      0.000000           0.5   \n",
       "2   0.383123  0.886245           0.0   0.592825      0.333333           0.0   \n",
       "3   0.399623  0.868471           0.0   0.578882      0.333333           0.0   \n",
       "4   0.465926  0.911832           0.0   0.666289      0.333333           0.0   \n",
       "5   0.328342  0.830611           0.0   0.496917      0.333333           0.0   \n",
       "6   0.614202  0.000000           0.0   0.574109      0.333333           0.0   \n",
       "7   0.453151  0.902829           0.0   0.648456      0.333333           0.0   \n",
       "8   0.422914  0.867777           0.0   0.590440      0.000000           0.0   \n",
       "9   0.442752  0.000000           0.0   0.413851      0.333333           0.0   \n",
       "10  0.429522  0.000000           0.0   0.401484      0.333333           0.0   \n",
       "11  0.477963  0.922451           0.0   0.686019      0.333333           0.0   \n",
       "12  0.379818  0.000000           0.0   0.355025      0.333333           0.0   \n",
       "13  0.566637  0.000000           0.0   0.529649      0.000000           0.0   \n",
       "14  0.500043  0.000000           0.0   0.467402      0.333333           0.0   \n",
       "15  0.354954  0.000000           0.0   0.331784      0.000000           0.0   \n",
       "16  0.416189  0.000000           0.0   0.389021      0.333333           0.0   \n",
       "17  0.512816  0.000000           0.0   0.479341      0.000000           0.0   \n",
       "18  0.455537  0.000000           0.0   0.425801      0.333333           0.0   \n",
       "19  0.525172  0.000000           0.0   0.490890      0.000000           0.0   \n",
       "\n",
       "    FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  KitchenQual  TotRmsAbvGrd  \\\n",
       "0   0.666667       0.5         0.375      0.333333     0.666667      0.500000   \n",
       "1   0.666667       0.0         0.375      0.333333     1.000000      0.333333   \n",
       "2   0.666667       0.5         0.375      0.333333     0.666667      0.333333   \n",
       "3   0.333333       0.0         0.375      0.333333     0.666667      0.416667   \n",
       "4   0.666667       0.5         0.500      0.333333     0.666667      0.583333   \n",
       "5   0.333333       0.5         0.125      0.333333     1.000000      0.250000   \n",
       "6   0.666667       0.0         0.375      0.333333     0.666667      0.416667   \n",
       "7   0.666667       0.5         0.375      0.333333     1.000000      0.416667   \n",
       "8   0.666667       0.0         0.250      0.666667     1.000000      0.500000   \n",
       "9   0.333333       0.0         0.250      0.666667     1.000000      0.250000   \n",
       "10  0.333333       0.0         0.375      0.333333     1.000000      0.250000   \n",
       "11  1.000000       0.0         0.500      0.333333     0.000000      0.750000   \n",
       "12  0.333333       0.0         0.250      0.333333     1.000000      0.166667   \n",
       "13  0.666667       0.0         0.375      0.333333     0.666667      0.416667   \n",
       "14  0.333333       0.5         0.250      0.333333     1.000000      0.250000   \n",
       "15  0.333333       0.0         0.250      0.333333     1.000000      0.250000   \n",
       "16  0.333333       0.0         0.250      0.333333     1.000000      0.250000   \n",
       "17  0.666667       0.0         0.250      0.666667     1.000000      0.333333   \n",
       "18  0.333333       0.5         0.375      0.333333     0.666667      0.333333   \n",
       "19  0.333333       0.0         0.375      0.333333     1.000000      0.333333   \n",
       "\n",
       "    Functional  Fireplaces  FireplaceQu  GarageType  GarageYrBlt  \\\n",
       "0     1.000000    0.000000          0.6    0.166667     0.382680   \n",
       "1     1.000000    0.333333          1.0    0.166667     0.740205   \n",
       "2     1.000000    0.333333          1.0    0.166667     0.444123   \n",
       "3     1.000000    0.333333          0.4    0.833333     0.469279   \n",
       "4     1.000000    0.333333          1.0    0.166667     0.469279   \n",
       "5     1.000000    0.000000          0.6    0.166667     0.605112   \n",
       "6     1.000000    0.333333          0.4    0.166667     0.296082   \n",
       "7     1.000000    0.666667          1.0    0.166667     0.771212   \n",
       "8     0.333333    0.666667          1.0    0.833333     0.930497   \n",
       "9     1.000000    0.666667          1.0    0.166667     0.907385   \n",
       "10    1.000000    0.000000          0.6    0.833333     0.808219   \n",
       "11    1.000000    0.666667          0.4    0.500000     0.148041   \n",
       "12    1.000000    0.000000          0.6    0.833333     0.822307   \n",
       "13    1.000000    0.333333          0.4    0.166667     0.148041   \n",
       "14    1.000000    0.333333          0.2    0.166667     0.831207   \n",
       "15    1.000000    0.000000          0.6    0.833333     0.605112   \n",
       "16    1.000000    0.333333          1.0    0.166667     0.793137   \n",
       "17    1.000000    0.000000          0.6    0.666667     0.787863   \n",
       "18    1.000000    0.000000          0.6    0.833333     0.343740   \n",
       "19    0.333333    0.000000          0.6    0.166667     0.843899   \n",
       "\n",
       "    GarageFinish  GarageCars  GarageArea  GarageQual  GarageCond  PavedDrive  \\\n",
       "0       0.666667        0.50    0.869159         1.0         1.0         1.0   \n",
       "1       0.666667        0.50    0.845088         1.0         1.0         1.0   \n",
       "2       0.666667        0.50    0.883450         1.0         1.0         1.0   \n",
       "3       1.000000        0.75    0.890935         1.0         1.0         1.0   \n",
       "4       0.666667        0.75    0.927266         1.0         1.0         1.0   \n",
       "5       1.000000        0.50    0.850939         1.0         1.0         1.0   \n",
       "6       0.666667        0.50    0.889643         1.0         1.0         1.0   \n",
       "7       0.666667        0.50    0.852080         1.0         1.0         1.0   \n",
       "8       1.000000        0.50    0.847458         0.2         1.0         1.0   \n",
       "9       0.666667        0.25    0.734099         0.4         1.0         1.0   \n",
       "10      1.000000        0.25    0.820265         1.0         1.0         1.0   \n",
       "11      0.000000        0.75    0.909735         1.0         1.0         1.0   \n",
       "12      1.000000        0.25    0.808309         1.0         1.0         1.0   \n",
       "13      0.666667        0.75    0.927923         1.0         1.0         1.0   \n",
       "14      0.666667        0.25    0.808309         1.0         1.0         1.0   \n",
       "15      1.000000        0.50    0.876012         1.0         1.0         1.0   \n",
       "16      0.000000        0.50    0.850939         1.0         1.0         1.0   \n",
       "17      1.000000        0.50    0.860884         1.0         1.0         1.0   \n",
       "18      1.000000        0.50    0.876012         1.0         1.0         1.0   \n",
       "19      1.000000        0.25    0.783577         1.0         1.0         1.0   \n",
       "\n",
       "    WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  \\\n",
       "0     0.000000     0.654449       0.000000   0.000000     0.000000       0.0   \n",
       "1     0.843935     0.000000       0.000000   0.000000     0.000000       0.0   \n",
       "2     0.000000     0.596422       0.000000   0.000000     0.000000       0.0   \n",
       "3     0.000000     0.568247       0.888227   0.000000     0.000000       0.0   \n",
       "4     0.779126     0.704481       0.000000   0.000000     0.000000       0.0   \n",
       "5     0.549784     0.544535       0.000000   0.629921     0.000000       0.0   \n",
       "6     0.820948     0.643873       0.000000   0.000000     0.000000       0.0   \n",
       "7     0.808905     0.844081       0.860398   0.000000     0.000000       0.0   \n",
       "8     0.667820     0.000000       0.843638   0.000000     0.000000       0.0   \n",
       "9     0.000000     0.255212       0.000000   0.000000     0.000000       0.0   \n",
       "10    0.000000     0.000000       0.000000   0.000000     0.000000       0.0   \n",
       "11    0.739823     0.490153       0.000000   0.000000     0.000000       0.0   \n",
       "12    0.732650     0.000000       0.000000   0.000000     0.838125       0.0   \n",
       "13    0.752288     0.559183       0.000000   0.000000     0.000000       0.0   \n",
       "14    0.000000     0.850895       0.819613   0.000000     0.000000       0.0   \n",
       "15    0.576173     0.749632       0.000000   0.000000     0.000000       0.0   \n",
       "16    0.000000     0.000000       0.000000   0.000000     0.000000       0.0   \n",
       "17    0.000000     0.000000       0.000000   0.000000     0.000000       0.0   \n",
       "18    0.000000     0.734939       0.000000   0.000000     0.000000       0.0   \n",
       "19    0.000000     0.000000       0.000000   0.000000     0.000000       0.0   \n",
       "\n",
       "    PoolQC  Fence  MiscFeature   MiscVal    MoSold  YrSold  SaleType  \\\n",
       "0      1.0   1.00         0.25  0.000000  0.090909    0.50      1.00   \n",
       "1      1.0   1.00         0.25  0.000000  0.363636    0.25      1.00   \n",
       "2      1.0   1.00         0.25  0.000000  0.727273    0.50      1.00   \n",
       "3      1.0   1.00         0.25  0.000000  0.090909    0.00      1.00   \n",
       "4      1.0   1.00         0.25  0.000000  1.000000    0.50      1.00   \n",
       "5      1.0   0.50         0.75  0.045161  0.818182    0.75      1.00   \n",
       "6      1.0   1.00         0.25  0.000000  0.636364    0.25      1.00   \n",
       "7      1.0   1.00         0.75  0.022581  0.909091    0.75      1.00   \n",
       "8      1.0   1.00         0.25  0.000000  0.272727    0.50      1.00   \n",
       "9      1.0   1.00         0.25  0.000000  0.000000    0.50      1.00   \n",
       "10     1.0   1.00         0.25  0.000000  0.090909    0.50      1.00   \n",
       "11     1.0   1.00         0.25  0.000000  0.545455    0.00      0.75   \n",
       "12     1.0   1.00         0.25  0.000000  0.727273    0.50      1.00   \n",
       "13     1.0   1.00         0.25  0.000000  0.636364    0.25      0.75   \n",
       "14     1.0   0.25         0.25  0.000000  0.363636    0.50      1.00   \n",
       "15     1.0   0.00         0.25  0.000000  0.545455    0.25      1.00   \n",
       "16     1.0   1.00         0.75  0.045161  0.181818    1.00      1.00   \n",
       "17     1.0   1.00         0.75  0.032258  0.818182    0.00      1.00   \n",
       "18     1.0   1.00         0.25  0.000000  0.454545    0.50      1.00   \n",
       "19     1.0   0.50         0.25  0.000000  0.363636    0.75      0.00   \n",
       "\n",
       "    SaleCondition  SalePrice  \n",
       "0             0.8   2.583824  \n",
       "1             0.8   2.573300  \n",
       "2             0.8   2.589054  \n",
       "3             0.0   2.553297  \n",
       "4             0.8   2.597433  \n",
       "5             0.8   2.554946  \n",
       "6             0.8   2.612611  \n",
       "7             0.8   2.580677  \n",
       "8             0.0   2.547453  \n",
       "9             0.8   2.539904  \n",
       "10            0.8   2.547212  \n",
       "11            1.0   2.621134  \n",
       "12            0.8   2.555487  \n",
       "13            1.0   2.605704  \n",
       "14            0.8   2.562177  \n",
       "15            0.8   2.548708  \n",
       "16            0.8   2.558135  \n",
       "17            0.8   2.518307  \n",
       "18            0.8   2.563153  \n",
       "19            0.0   2.552739  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_df_prepared = pd.DataFrame(scale.transform(train_df[scale_feats]), columns = scale_feats)\n",
    "train_df_prepared = pd.concat([train_df_prepared, train_df['SalePrice']], axis = 1)\n",
    "print(\"Tranformed and prepared data set:\")\n",
    "train_df_prepared.head(20)"
   ]
  }
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