House_Price-PCA_Imputation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "542a6a56",
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c58570a",
   "metadata": {},
   "source": [
    "# Load Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d5cf16ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train = pd.read_csv('Dataset/train.csv')\n",
    "df_test = pd.read_csv('Dataset/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "149700cb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Id', 'MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
       "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
       "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
       "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
       "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
       "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
       "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
       "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
       "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
       "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
       "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
       "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
       "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
       "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
       "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
       "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
       "       'SaleCondition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ab30ad14",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_train.drop(['Id'], axis=1, inplace=True)\n",
    "df_test.drop(['Id'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bff5bc8",
   "metadata": {},
   "source": [
    "#### Merge Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d939ee96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2919, 79)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main = pd.concat([df_train.drop(['SalePrice'], axis=1), df_test], ignore_index=True)\n",
    "df_main.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8225777e",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.index.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47da4271",
   "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>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.137718</td>\n",
       "      <td>42.517628</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>2915</td>\n",
       "      <td>5</td>\n",
       "      <td>RL</td>\n",
       "      <td>2265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>2433.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.305795</td>\n",
       "      <td>23.344905</td>\n",
       "      <td>21.0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10168.11408</td>\n",
       "      <td>7886.996359</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>7478.0</td>\n",
       "      <td>9453.0</td>\n",
       "      <td>11570.0</td>\n",
       "      <td>215245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Street</th>\n",
       "      <td>2919</td>\n",
       "      <td>2</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2907</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>198</td>\n",
       "      <td>2</td>\n",
       "      <td>Grvl</td>\n",
       "      <td>120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotShape</th>\n",
       "      <td>2919</td>\n",
       "      <td>4</td>\n",
       "      <td>Reg</td>\n",
       "      <td>1859</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandContour</th>\n",
       "      <td>2919</td>\n",
       "      <td>4</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>2622</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>2917</td>\n",
       "      <td>2</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2916</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotConfig</th>\n",
       "      <td>2919</td>\n",
       "      <td>5</td>\n",
       "      <td>Inside</td>\n",
       "      <td>2133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope</th>\n",
       "      <td>2919</td>\n",
       "      <td>3</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>2778</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <td>2919</td>\n",
       "      <td>25</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>443</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1</th>\n",
       "      <td>2919</td>\n",
       "      <td>9</td>\n",
       "      <td>Norm</td>\n",
       "      <td>2511</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2</th>\n",
       "      <td>2919</td>\n",
       "      <td>8</td>\n",
       "      <td>Norm</td>\n",
       "      <td>2889</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType</th>\n",
       "      <td>2919</td>\n",
       "      <td>5</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2425</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle</th>\n",
       "      <td>2919</td>\n",
       "      <td>8</td>\n",
       "      <td>1Story</td>\n",
       "      <td>1471</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.089072</td>\n",
       "      <td>1.409947</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.564577</td>\n",
       "      <td>1.113131</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1971.312778</td>\n",
       "      <td>30.291442</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1953.5</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1984.264474</td>\n",
       "      <td>20.894344</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>1993.0</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle</th>\n",
       "      <td>2919</td>\n",
       "      <td>6</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2310</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl</th>\n",
       "      <td>2919</td>\n",
       "      <td>8</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>2876</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st</th>\n",
       "      <td>2918</td>\n",
       "      <td>15</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>1025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd</th>\n",
       "      <td>2918</td>\n",
       "      <td>16</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>1014</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>1153</td>\n",
       "      <td>3</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>879</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>2896.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.201312</td>\n",
       "      <td>179.334253</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>164.0</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual</th>\n",
       "      <td>2919</td>\n",
       "      <td>4</td>\n",
       "      <td>TA</td>\n",
       "      <td>1798</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond</th>\n",
       "      <td>2919</td>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2538</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation</th>\n",
       "      <td>2919</td>\n",
       "      <td>6</td>\n",
       "      <td>PConc</td>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>2838</td>\n",
       "      <td>4</td>\n",
       "      <td>TA</td>\n",
       "      <td>1283</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>2837</td>\n",
       "      <td>4</td>\n",
       "      <td>TA</td>\n",
       "      <td>2606</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>2837</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1904</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>2840</td>\n",
       "      <td>6</td>\n",
       "      <td>Unf</td>\n",
       "      <td>851</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>441.423235</td>\n",
       "      <td>455.610826</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>368.5</td>\n",
       "      <td>733.0</td>\n",
       "      <td>5644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>2839</td>\n",
       "      <td>6</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.582248</td>\n",
       "      <td>169.205611</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>560.772104</td>\n",
       "      <td>439.543659</td>\n",
       "      <td>0.0</td>\n",
       "      <td>220.0</td>\n",
       "      <td>467.0</td>\n",
       "      <td>805.5</td>\n",
       "      <td>2336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1051.777587</td>\n",
       "      <td>440.766258</td>\n",
       "      <td>0.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>989.5</td>\n",
       "      <td>1302.0</td>\n",
       "      <td>6110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating</th>\n",
       "      <td>2919</td>\n",
       "      <td>6</td>\n",
       "      <td>GasA</td>\n",
       "      <td>2874</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeatingQC</th>\n",
       "      <td>2919</td>\n",
       "      <td>5</td>\n",
       "      <td>Ex</td>\n",
       "      <td>1493</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CentralAir</th>\n",
       "      <td>2919</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>2723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>2918</td>\n",
       "      <td>5</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>2671</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1159.581706</td>\n",
       "      <td>392.362079</td>\n",
       "      <td>334.0</td>\n",
       "      <td>876.0</td>\n",
       "      <td>1082.0</td>\n",
       "      <td>1387.5</td>\n",
       "      <td>5095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>336.483727</td>\n",
       "      <td>428.701456</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>704.0</td>\n",
       "      <td>2065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.694416</td>\n",
       "      <td>46.396825</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1500.759849</td>\n",
       "      <td>506.051045</td>\n",
       "      <td>334.0</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>1444.0</td>\n",
       "      <td>1743.5</td>\n",
       "      <td>5642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.429894</td>\n",
       "      <td>0.524736</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061364</td>\n",
       "      <td>0.245687</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullBath</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.568003</td>\n",
       "      <td>0.552969</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.380267</td>\n",
       "      <td>0.502872</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.860226</td>\n",
       "      <td>0.822693</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.044536</td>\n",
       "      <td>0.214462</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual</th>\n",
       "      <td>2918</td>\n",
       "      <td>4</td>\n",
       "      <td>TA</td>\n",
       "      <td>1492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.451524</td>\n",
       "      <td>1.569379</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional</th>\n",
       "      <td>2917</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2717</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.597122</td>\n",
       "      <td>0.646129</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>1499</td>\n",
       "      <td>5</td>\n",
       "      <td>Gd</td>\n",
       "      <td>744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>2762</td>\n",
       "      <td>6</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1723</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>2760.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1978.113406</td>\n",
       "      <td>25.574285</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>1960.0</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>2760</td>\n",
       "      <td>3</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1230</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.766621</td>\n",
       "      <td>0.761624</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>2918.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>472.874572</td>\n",
       "      <td>215.394815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>480.0</td>\n",
       "      <td>576.0</td>\n",
       "      <td>1488.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>2760</td>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2604</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>2760</td>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2654</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PavedDrive</th>\n",
       "      <td>2919</td>\n",
       "      <td>3</td>\n",
       "      <td>Y</td>\n",
       "      <td>2641</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.709832</td>\n",
       "      <td>126.526589</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>168.0</td>\n",
       "      <td>1424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.486811</td>\n",
       "      <td>67.575493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.098321</td>\n",
       "      <td>64.244246</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.602261</td>\n",
       "      <td>25.188169</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.06235</td>\n",
       "      <td>56.184365</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.251799</td>\n",
       "      <td>35.663946</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>Ex</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>571</td>\n",
       "      <td>4</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>329</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>105</td>\n",
       "      <td>4</td>\n",
       "      <td>Shed</td>\n",
       "      <td>95</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.825968</td>\n",
       "      <td>567.402211</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.213087</td>\n",
       "      <td>2.714762</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>2919.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.792737</td>\n",
       "      <td>1.314964</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType</th>\n",
       "      <td>2918</td>\n",
       "      <td>9</td>\n",
       "      <td>WD</td>\n",
       "      <td>2525</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition</th>\n",
       "      <td>2919</td>\n",
       "      <td>6</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2402</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count unique      top  freq         mean          std     min   \n",
       "MSSubClass     2919.0    NaN      NaN   NaN    57.137718    42.517628    20.0  \\\n",
       "MSZoning         2915      5       RL  2265          NaN          NaN     NaN   \n",
       "LotFrontage    2433.0    NaN      NaN   NaN    69.305795    23.344905    21.0   \n",
       "LotArea        2919.0    NaN      NaN   NaN  10168.11408  7886.996359  1300.0   \n",
       "Street           2919      2     Pave  2907          NaN          NaN     NaN   \n",
       "Alley             198      2     Grvl   120          NaN          NaN     NaN   \n",
       "LotShape         2919      4      Reg  1859          NaN          NaN     NaN   \n",
       "LandContour      2919      4      Lvl  2622          NaN          NaN     NaN   \n",
       "Utilities        2917      2   AllPub  2916          NaN          NaN     NaN   \n",
       "LotConfig        2919      5   Inside  2133          NaN          NaN     NaN   \n",
       "LandSlope        2919      3      Gtl  2778          NaN          NaN     NaN   \n",
       "Neighborhood     2919     25    NAmes   443          NaN          NaN     NaN   \n",
       "Condition1       2919      9     Norm  2511          NaN          NaN     NaN   \n",
       "Condition2       2919      8     Norm  2889          NaN          NaN     NaN   \n",
       "BldgType         2919      5     1Fam  2425          NaN          NaN     NaN   \n",
       "HouseStyle       2919      8   1Story  1471          NaN          NaN     NaN   \n",
       "OverallQual    2919.0    NaN      NaN   NaN     6.089072     1.409947     1.0   \n",
       "OverallCond    2919.0    NaN      NaN   NaN     5.564577     1.113131     1.0   \n",
       "YearBuilt      2919.0    NaN      NaN   NaN  1971.312778    30.291442  1872.0   \n",
       "YearRemodAdd   2919.0    NaN      NaN   NaN  1984.264474    20.894344  1950.0   \n",
       "RoofStyle        2919      6    Gable  2310          NaN          NaN     NaN   \n",
       "RoofMatl         2919      8  CompShg  2876          NaN          NaN     NaN   \n",
       "Exterior1st      2918     15  VinylSd  1025          NaN          NaN     NaN   \n",
       "Exterior2nd      2918     16  VinylSd  1014          NaN          NaN     NaN   \n",
       "MasVnrType       1153      3  BrkFace   879          NaN          NaN     NaN   \n",
       "MasVnrArea     2896.0    NaN      NaN   NaN   102.201312   179.334253     0.0   \n",
       "ExterQual        2919      4       TA  1798          NaN          NaN     NaN   \n",
       "ExterCond        2919      5       TA  2538          NaN          NaN     NaN   \n",
       "Foundation       2919      6    PConc  1308          NaN          NaN     NaN   \n",
       "BsmtQual         2838      4       TA  1283          NaN          NaN     NaN   \n",
       "BsmtCond         2837      4       TA  2606          NaN          NaN     NaN   \n",
       "BsmtExposure     2837      4       No  1904          NaN          NaN     NaN   \n",
       "BsmtFinType1     2840      6      Unf   851          NaN          NaN     NaN   \n",
       "BsmtFinSF1     2918.0    NaN      NaN   NaN   441.423235   455.610826     0.0   \n",
       "BsmtFinType2     2839      6      Unf  2493          NaN          NaN     NaN   \n",
       "BsmtFinSF2     2918.0    NaN      NaN   NaN    49.582248   169.205611     0.0   \n",
       "BsmtUnfSF      2918.0    NaN      NaN   NaN   560.772104   439.543659     0.0   \n",
       "TotalBsmtSF    2918.0    NaN      NaN   NaN  1051.777587   440.766258     0.0   \n",
       "Heating          2919      6     GasA  2874          NaN          NaN     NaN   \n",
       "HeatingQC        2919      5       Ex  1493          NaN          NaN     NaN   \n",
       "CentralAir       2919      2        Y  2723          NaN          NaN     NaN   \n",
       "Electrical       2918      5    SBrkr  2671          NaN          NaN     NaN   \n",
       "1stFlrSF       2919.0    NaN      NaN   NaN  1159.581706   392.362079   334.0   \n",
       "2ndFlrSF       2919.0    NaN      NaN   NaN   336.483727   428.701456     0.0   \n",
       "LowQualFinSF   2919.0    NaN      NaN   NaN     4.694416    46.396825     0.0   \n",
       "GrLivArea      2919.0    NaN      NaN   NaN  1500.759849   506.051045   334.0   \n",
       "BsmtFullBath   2917.0    NaN      NaN   NaN     0.429894     0.524736     0.0   \n",
       "BsmtHalfBath   2917.0    NaN      NaN   NaN     0.061364     0.245687     0.0   \n",
       "FullBath       2919.0    NaN      NaN   NaN     1.568003     0.552969     0.0   \n",
       "HalfBath       2919.0    NaN      NaN   NaN     0.380267     0.502872     0.0   \n",
       "BedroomAbvGr   2919.0    NaN      NaN   NaN     2.860226     0.822693     0.0   \n",
       "KitchenAbvGr   2919.0    NaN      NaN   NaN     1.044536     0.214462     0.0   \n",
       "KitchenQual      2918      4       TA  1492          NaN          NaN     NaN   \n",
       "TotRmsAbvGrd   2919.0    NaN      NaN   NaN     6.451524     1.569379     2.0   \n",
       "Functional       2917      7      Typ  2717          NaN          NaN     NaN   \n",
       "Fireplaces     2919.0    NaN      NaN   NaN     0.597122     0.646129     0.0   \n",
       "FireplaceQu      1499      5       Gd   744          NaN          NaN     NaN   \n",
       "GarageType       2762      6   Attchd  1723          NaN          NaN     NaN   \n",
       "GarageYrBlt    2760.0    NaN      NaN   NaN  1978.113406    25.574285  1895.0   \n",
       "GarageFinish     2760      3      Unf  1230          NaN          NaN     NaN   \n",
       "GarageCars     2918.0    NaN      NaN   NaN     1.766621     0.761624     0.0   \n",
       "GarageArea     2918.0    NaN      NaN   NaN   472.874572   215.394815     0.0   \n",
       "GarageQual       2760      5       TA  2604          NaN          NaN     NaN   \n",
       "GarageCond       2760      5       TA  2654          NaN          NaN     NaN   \n",
       "PavedDrive       2919      3        Y  2641          NaN          NaN     NaN   \n",
       "WoodDeckSF     2919.0    NaN      NaN   NaN    93.709832   126.526589     0.0   \n",
       "OpenPorchSF    2919.0    NaN      NaN   NaN    47.486811    67.575493     0.0   \n",
       "EnclosedPorch  2919.0    NaN      NaN   NaN    23.098321    64.244246     0.0   \n",
       "3SsnPorch      2919.0    NaN      NaN   NaN     2.602261    25.188169     0.0   \n",
       "ScreenPorch    2919.0    NaN      NaN   NaN     16.06235    56.184365     0.0   \n",
       "PoolArea       2919.0    NaN      NaN   NaN     2.251799    35.663946     0.0   \n",
       "PoolQC             10      3       Ex     4          NaN          NaN     NaN   \n",
       "Fence             571      4    MnPrv   329          NaN          NaN     NaN   \n",
       "MiscFeature       105      4     Shed    95          NaN          NaN     NaN   \n",
       "MiscVal        2919.0    NaN      NaN   NaN    50.825968   567.402211     0.0   \n",
       "MoSold         2919.0    NaN      NaN   NaN     6.213087     2.714762     1.0   \n",
       "YrSold         2919.0    NaN      NaN   NaN  2007.792737     1.314964  2006.0   \n",
       "SaleType         2918      9       WD  2525          NaN          NaN     NaN   \n",
       "SaleCondition    2919      6   Normal  2402          NaN          NaN     NaN   \n",
       "\n",
       "                  25%     50%      75%       max  \n",
       "MSSubClass       20.0    50.0     70.0     190.0  \n",
       "MSZoning          NaN     NaN      NaN       NaN  \n",
       "LotFrontage      59.0    68.0     80.0     313.0  \n",
       "LotArea        7478.0  9453.0  11570.0  215245.0  \n",
       "Street            NaN     NaN      NaN       NaN  \n",
       "Alley             NaN     NaN      NaN       NaN  \n",
       "LotShape          NaN     NaN      NaN       NaN  \n",
       "LandContour       NaN     NaN      NaN       NaN  \n",
       "Utilities         NaN     NaN      NaN       NaN  \n",
       "LotConfig         NaN     NaN      NaN       NaN  \n",
       "LandSlope         NaN     NaN      NaN       NaN  \n",
       "Neighborhood      NaN     NaN      NaN       NaN  \n",
       "Condition1        NaN     NaN      NaN       NaN  \n",
       "Condition2        NaN     NaN      NaN       NaN  \n",
       "BldgType          NaN     NaN      NaN       NaN  \n",
       "HouseStyle        NaN     NaN      NaN       NaN  \n",
       "OverallQual       5.0     6.0      7.0      10.0  \n",
       "OverallCond       5.0     5.0      6.0       9.0  \n",
       "YearBuilt      1953.5  1973.0   2001.0    2010.0  \n",
       "YearRemodAdd   1965.0  1993.0   2004.0    2010.0  \n",
       "RoofStyle         NaN     NaN      NaN       NaN  \n",
       "RoofMatl          NaN     NaN      NaN       NaN  \n",
       "Exterior1st       NaN     NaN      NaN       NaN  \n",
       "Exterior2nd       NaN     NaN      NaN       NaN  \n",
       "MasVnrType        NaN     NaN      NaN       NaN  \n",
       "MasVnrArea        0.0     0.0    164.0    1600.0  \n",
       "ExterQual         NaN     NaN      NaN       NaN  \n",
       "ExterCond         NaN     NaN      NaN       NaN  \n",
       "Foundation        NaN     NaN      NaN       NaN  \n",
       "BsmtQual          NaN     NaN      NaN       NaN  \n",
       "BsmtCond          NaN     NaN      NaN       NaN  \n",
       "BsmtExposure      NaN     NaN      NaN       NaN  \n",
       "BsmtFinType1      NaN     NaN      NaN       NaN  \n",
       "BsmtFinSF1        0.0   368.5    733.0    5644.0  \n",
       "BsmtFinType2      NaN     NaN      NaN       NaN  \n",
       "BsmtFinSF2        0.0     0.0      0.0    1526.0  \n",
       "BsmtUnfSF       220.0   467.0    805.5    2336.0  \n",
       "TotalBsmtSF     793.0   989.5   1302.0    6110.0  \n",
       "Heating           NaN     NaN      NaN       NaN  \n",
       "HeatingQC         NaN     NaN      NaN       NaN  \n",
       "CentralAir        NaN     NaN      NaN       NaN  \n",
       "Electrical        NaN     NaN      NaN       NaN  \n",
       "1stFlrSF        876.0  1082.0   1387.5    5095.0  \n",
       "2ndFlrSF          0.0     0.0    704.0    2065.0  \n",
       "LowQualFinSF      0.0     0.0      0.0    1064.0  \n",
       "GrLivArea      1126.0  1444.0   1743.5    5642.0  \n",
       "BsmtFullBath      0.0     0.0      1.0       3.0  \n",
       "BsmtHalfBath      0.0     0.0      0.0       2.0  \n",
       "FullBath          1.0     2.0      2.0       4.0  \n",
       "HalfBath          0.0     0.0      1.0       2.0  \n",
       "BedroomAbvGr      2.0     3.0      3.0       8.0  \n",
       "KitchenAbvGr      1.0     1.0      1.0       3.0  \n",
       "KitchenQual       NaN     NaN      NaN       NaN  \n",
       "TotRmsAbvGrd      5.0     6.0      7.0      15.0  \n",
       "Functional        NaN     NaN      NaN       NaN  \n",
       "Fireplaces        0.0     1.0      1.0       4.0  \n",
       "FireplaceQu       NaN     NaN      NaN       NaN  \n",
       "GarageType        NaN     NaN      NaN       NaN  \n",
       "GarageYrBlt    1960.0  1979.0   2002.0    2207.0  \n",
       "GarageFinish      NaN     NaN      NaN       NaN  \n",
       "GarageCars        1.0     2.0      2.0       5.0  \n",
       "GarageArea      320.0   480.0    576.0    1488.0  \n",
       "GarageQual        NaN     NaN      NaN       NaN  \n",
       "GarageCond        NaN     NaN      NaN       NaN  \n",
       "PavedDrive        NaN     NaN      NaN       NaN  \n",
       "WoodDeckSF        0.0     0.0    168.0    1424.0  \n",
       "OpenPorchSF       0.0    26.0     70.0     742.0  \n",
       "EnclosedPorch     0.0     0.0      0.0    1012.0  \n",
       "3SsnPorch         0.0     0.0      0.0     508.0  \n",
       "ScreenPorch       0.0     0.0      0.0     576.0  \n",
       "PoolArea          0.0     0.0      0.0     800.0  \n",
       "PoolQC            NaN     NaN      NaN       NaN  \n",
       "Fence             NaN     NaN      NaN       NaN  \n",
       "MiscFeature       NaN     NaN      NaN       NaN  \n",
       "MiscVal           0.0     0.0      0.0   17000.0  \n",
       "MoSold            4.0     6.0      8.0      12.0  \n",
       "YrSold         2007.0  2008.0   2009.0    2010.0  \n",
       "SaleType          NaN     NaN      NaN       NaN  \n",
       "SaleCondition     NaN     NaN      NaN       NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 200)\n",
    "df_main.describe(include='all').T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e32626c6",
   "metadata": {},
   "source": [
    "**DataFrame.describe()** generates a descriptive statistics of the dataframe. <br>\n",
    "***For Numeric Data*** <br>\n",
    "**count** - The number of not-empty values. <br>\n",
    "**mean** - The average (mean) value. <br>\n",
    "**std** - The standard deviation. <br>\n",
    "**min** - the minimum value. <br>\n",
    "**25%** - The 25% percentile. <br>\n",
    "**50%** - The 50% percentile. <br>\n",
    "**75%** - The 75% percentile. <br>\n",
    "**max** - the maximum value. <br>\n",
    "\n",
    "***For Object Data*** <br>\n",
    "**top** - most common value <br>\n",
    "**freq** - top value's frequency <br>\n",
    "**unique** - unique values of the feature/column"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "18cd575c",
   "metadata": {},
   "source": [
    "# Preprocessing "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67f6592f",
   "metadata": {},
   "source": [
    "#### Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e0f4393c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1f34b555",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_main.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fab6ada4",
   "metadata": {},
   "source": [
    "#### Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ee30b3c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_main.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "48498cc6",
   "metadata": {},
   "source": [
    "#### Missing Value Percentage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "81e691b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GarageCars      0.000343\n",
       "GarageArea      0.000343\n",
       "KitchenQual     0.000343\n",
       "BsmtFinSF1      0.000343\n",
       "Electrical      0.000343\n",
       "SaleType        0.000343\n",
       "Exterior1st     0.000343\n",
       "BsmtFinSF2      0.000343\n",
       "TotalBsmtSF     0.000343\n",
       "Exterior2nd     0.000343\n",
       "BsmtUnfSF       0.000343\n",
       "Utilities       0.000686\n",
       "BsmtFullBath    0.000686\n",
       "Functional      0.000686\n",
       "BsmtHalfBath    0.000686\n",
       "MSZoning        0.001371\n",
       "MasVnrArea      0.007885\n",
       "BsmtFinType1    0.027083\n",
       "BsmtFinType2    0.027425\n",
       "BsmtQual        0.027768\n",
       "BsmtExposure    0.028111\n",
       "BsmtCond        0.028111\n",
       "GarageType      0.053822\n",
       "GarageCond      0.054508\n",
       "GarageFinish    0.054508\n",
       "GarageYrBlt     0.054508\n",
       "GarageQual      0.054508\n",
       "LotFrontage     0.166610\n",
       "FireplaceQu     0.486116\n",
       "MasVnrType      0.605074\n",
       "Fence           0.804251\n",
       "Alley           0.932465\n",
       "MiscFeature     0.964004\n",
       "PoolQC          0.996572\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_by_cols = df_main.isna().sum().sort_values()/len(df_main)\n",
    "missing_by_cols = missing_by_cols[missing_by_cols>0]\n",
    "missing_by_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7442f0ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_by_col_first = missing_by_cols.head(len(missing_by_cols)//2+1)\n",
    "missing_by_col_second = missing_by_cols.tail(len(missing_by_cols)//2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "fd841ff9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'CountMissing')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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JiaMjp02bNnr55Zf16KOPSjrwArbf79eoUaOKfEg5FM2ZZ56pfv36afr06cFF9a1bt+r+++9Xy5YtHZ4ufowZM0YdOnRQw4YNtX//fl1zzTX6+eefdeyxx+r11193ery407NnT91000369ddfg4/jb7/9ViNHjlTPnj0dni72/fO8VJI0bNiwAttcLhcvXh+h//znP0W+bv4nqHHkHnjgAc2bN08TJ07U9ddfrwkTJmjjxo2aPHmyRo4c6fR4cWfw4MHq0aOHNm7cKL/frxkzZmjNmjV6+eWX+fejkUWLFqlMmTJOjxFXeFzb6tevn+644w7t379fgUBAixcv1uuvv64RI0ZoypQpTo8XFy699FJ98sknOuWUU9SrVy91795dlSpVCl5+zDHH6N577+UT6RHUuXNnDRs2TG+99ZakA79bb9iwQf3799fll1/u8HTx46yzztL06dN17rnnFnr5yy+/zGsjB+GcMqVQz549VbZsWU2YMKHQy1etWqVOnTrxLr8I6NKli5o0aaKhQ4cWuGz+/Pm65JJLlJ2dzQshEfbGG2+oe/fu6tChg+bMmaMLL7xQa9eu1datW9W1a1dNmzbN6RHjxo8//qjzzz9fzZs319y5c9W5c2f973//065du/T111/rpJNOcnrEuPHLL7+oa9euWrt2rWrVqiVJ+uOPP1SvXj29//77hR52ASXj9Xr1xhtvaMWKFcrKylLz5s117bXX8q6bKPD7/Ro9erSefvppbd68WZJUo0YN3X333br33ntDDmsGlGYHvwEnHJfLpd9++y3K0xw9ateurZdffllt27ZVamqqvv/+e5188smaPn26Xn/9dc2ePdvpEePOl19+qWHDhumHH34I/owcPHiwLrzwQqdHiyv/POR4IBDQ5s2btWTJEg0aNEhDhgxxaLL4xOPa1quvvqpHHnlEv/76qyTpuOOO09ChQznEe4TcdNNNuvnmm4PnIS1MIBDQhg0bCnxiCSWTkZGhK664QkuWLNGePXt03HHHacuWLWrVqpVmz56tY445xukR48K8efN0wQUXqG/fvrr//vtD3qw6atQoPf3005ozZ47atWvn8KSlA4sypVBOTo58Pl+hhxxCZC1YsEALFy7UwIEDC7183rx5evnll1kkiLDGjRvr1ltv1R133BE8ydqJJ56oW2+9VTVq1Ch0kQwll5GRofHjx4f8I+aOO+5QjRo1nB4t7gQCAX366adavXq1pAOffGzfvj3nAUNcyP/IP+eAsLN79+5CD4sIxIpy5crpp59+Uu3atXX88cdrxowZatmypdatW6fTTz+dc28gZv3z06Jut1tVqlRRu3btWChA3Ni3b5+ysrJUtWpVp0eJW/v37+fTdYa+/vrrkNdF2rdv7/RIcWfy5Mm6++67lZeXp9TUVLlcLmVkZCgxMVFjx47V7bff7vSIpQaLMgDMHXPMMfrf//6nOnXqqHLlypo/f75OP/10rVq1Su3atQu+GxtHJi8vTxdddJEmTZqkevXqOT0OEDFr1qzRM888o1WrVkk6sPjVp08fNWjQwOHJgJJ74oknVKdOHXXr1k2SdOWVV+rdd99VjRo1NHv2bDVp0sThCYHia9y4sZ555hmdd955at++vZo2barRo0frP//5j0aNGsWhggEU2ZIlS4K/+zVs2FBnnHGGwxPFp3Xr1snr9Rb49+PPP/+sxMRE1alTx5nB4ojf79djjz2mSZMmaevWrVq7dq3q1q2rQYMGqU6dOnwiKcLy8vKUkpKi5cuXq1GjRk6Pc1TYuHGj3nrrLf3yyy8KBAI65ZRTdMUVV+j44493erRShXPKlHK//vqrpk2bpl9//VVPP/20qlatqv/+97+qXbu2TjvtNKfHiyu0tlOxYkXt2bNHklSzZk39+OOPOv3007V7927t27fP4eniR2JiolasWOH0GEeVzz//XJ9//rm2bdtW4CToL7zwgkNTxZd3331X//73v9WiRYvgR/6/+eYbnX766XrjjTc4JnCEbd26Vffdd1/wcf3P9/JweM/ImTRpkl599VVJ0qeffqrPPvtMH3/8sd566y3df//9mjNnjsMTxoeff/5ZK1asUPPmzXXiiSdq1qxZeuKJJ5Sdna0uXbrowQcf5NONEdSzZ0/98MMPOu+88zRgwABdeumlGj9+vPLy8vTUU085PV7cqVixYqGPX5fLpTJlyujkk0/WDTfcwDnBIiA7O1uffvqp1q5dq6SkJNWvX1/t27fnsJ5R8Oeff+rqq6/W119/Hfz06O7du9W6dWu98cYbvMgXYTfccINuvPHGAosy3377raZMmaL58+c7M1gcGT58uF566SWNGjVKvXr1Cm5v1KiRxo0bx6JMhCUmJqp27dr8u8VQzZo1dc899zg9RqnHJ2VKsQULFqhjx44655xz9MUXX2jVqlWqW7euRo4cqSVLluidd95xesS4QWtb11xzjVq0aKF+/frp0Ucf1TPPPKN//etf+vTTT9W8eXPNmDHD6RHjxj333KPk5GROpmtg6NChGjZsmFq0aKEaNWoUeFHkvffec2iy+HLSSSfp2muvLXAy9CFDhuiVV14JHvsakdGxY0dt2LBBffr0KfRx/a9//cuhyeJPSkpK8JxUd999t/bv36/Jkydr7dq1Ouuss/TXX385PWLMe++993TVVVfJ7XbL5XLpueee06233qq2bdvK4/Hok08+0fDhw9W/f3+nR41b69ev19KlS3XyySercePGTo8Td8aOHavHHntMHTt2DJ5Id/Hixfr44491zz33aN26dZo+fbqeeeaZkBcCUTwffPCBbr75Zu3YsSNke82aNfXqq68GT3C8bt26Ip/PCod20UUXaffu3XrppZdUv359SQc+Nd2zZ0+lpqbq448/dnjC+HLw+b8O9ssvv6hFixbavXu3M4PFkZNPPlmTJ0/W+eefHzyce926dbV69Wq1atWK3/miYOrUqZoxY4amT5+uSpUqOT1O3Hv77bf1+uuvB9+4cMopp6hnz57q0KGD06OVKizKlGKtWrXSlVdeqX79+oU8US9evFiXXXYZH/ePIFrb2rVrl/bv36/jjjtOfr9fo0aN0sKFC1WvXj09/PDDqlixotMjxo0777xTL7/8surVq6czzjijwAnseJdq5NSoUUOjRo3S9ddf7/Qoca1s2bJasWJFgX8o/vzzz2rSpAmftouw8uXL68svv1TTpk2dHiXuHXfccXrnnXfUunVr1a9fX8OHD9eVV16pNWvW6Mwzzwye0wcl16JFC3Xo0EHDhw/Xiy++qDvuuEOPP/64+vbtK0l67rnnNHbs2ODhcYBYc/nll+uCCy7QbbfdFrJ98uTJmjNnjt59910988wzeu6557Ry5UqHpoxtCxcuVNu2bdW5c2fde++9OvXUUyVJP/30k8aMGaPZs2dr2bJlmjZtmo455hgNHjzY4YljX0pKihYuXKhmzZqFbF+6dKnatGnD734RlpaWpvnz5xfau23btsEjXqDkUlJStHr1ap1wwgkhrz/99NNPatmyJedbi4JmzZrpl19+UV5enk444YQCr4t8//33Dk0WX/x+v66++mq9/fbbOuWUU4KHF1+1apV++eUX3XLLLZo4caJ27typL774Ql27dnV4Ymdx+LJSbOXKlXrttdcKbK9atWqBd+XgyNDa1sHvTHC73RowYICD08S3H3/8Uc2bN5ckrV27NuQyDs8SWbm5uWrdurXTY8S9tm3b6ssvvyywKPPVV1+pTZs2Dk0Vv2rVqlXgkGWIjssuu0zXXHON6tWrp507d6pjx46SpGXLlhV4vKNk1qxZozfffFMul0s9evRQr169Qk7weuGFFwYXaBAZ//xU4z/xgnVkffLJJ3riiScKbD///PN17733SpI6derE795HYPjw4erZs6cmT54csr1169Zq3bq1br31VrVp00aBQECff/65Q1PGl1q1aikvL6/Adp/Pp+OOO86BieLbueeeqxEjRuj1118PHo7P5/NpxIgR+r//+z+Hp4sPDRs21JdffqkTTjghZPs777xTYDEMkdGlSxenRzgqPP300/rss8/0wQcf6JJLLgm57IMPPlDPnj110kkn6cUXX1T37t0dmrL0YFGmFKtQoYI2b95c4CPPy5YtU82aNR2aKj7R2taGDRvCXl67dm2jSeLfvHnznB7hqHHzzTfrtdde06BBg5weJa517txZ/fv319KlS3X22WdLOnBOmbfffltDhw7VBx98EHJdHJlx48ZpwIABmjx5Mid2jbKxY8eqTp06+uOPPzRq1CiVK1dOkrR582b17t3b4eniw969e1W+fHlJB94UkpKSorJlywYvT0lJUU5OjlPjxaV/HrozLy9P69atU0JCgk466SQWZSKsUqVK+vDDDwscx/3DDz8Mvinq4L8HKL5vvvmm0IWvfHfccYeef/55ff/992rSpInhZPHrySef1J133qkJEyaoRYsWkqQlS5bo7rvv1ujRox2eLv488cQTOvfcc1W/fv3gG56+/PJLZWZmau7cuQ5PFx8GDx6sHj16aOPGjfL7/ZoxY4bWrFmjl19+WR999JHT48WlIUOGOD3CUWHatGl68sknCyzISAf+bT5q1CjdcsstvBHq/+PwZaXYfffdp2+//Tb4sa/vv/9eW7duVffu3dW9e3eeVCKI1rbyj+V+KJyADbHo7rvv1ssvv6zGjRurcePGSkxMDLmcQ8VFhtvtLtL1XC4XzyURULFiRe3bt09er1dly5Yt8LjetWuXQ5MBxefxeLRlyxZVqVJF0oHj5v/www/BN+Vs3bpVxx13HM8dUZaZmakbbrhBXbt25ZCfEfb888/r9ttvV6dOnYLnlPnuu+80e/ZsTZo0STfddJPGjBmjxYsX680333R42th08GGHCrN+/Xo1aNBA2dnZxpPFr4N/F0lIOPC+4vz//89DEPF7SWRs2rRJ48eP1w8//KCUlBQ1btxYffr04VwcEfTll19q2LBh+uGHH5SVlaXmzZtr8ODBuvDCC50eDSixlJQUrVmz5pBvtF6/fr3q1q2r7OxsJSUlGU9X+rAoU4rl5ubqjjvu0Isvviifz6eEhAT5fD5dc801evHFF4MfJcWRo7WtH374IeTPeXl5WrZsmZ566ik99thjuuyyyxyaLP6kp6eHXQDj3U6Rk56efsjLXC4XrRGTXnrppbCX9+jRw2iS+PTBBx+oY8eOSkxMDPmUV2H45NeRc7vdSktLC/5c3L17t1JTU4OLvYFAQJmZmSzKGFi5cqUuvfRS/f77706PEne+/vprjR8/XmvWrJEk1a9fX3feeSeHWI2Qxo0b65577lHPnj0LvfyFF17QuHHjtGLFCuPJ4teLL75Y5MMu83sJgMLwxmAblSpV0vz589W4ceNCL1+5cqXOPfdc/fXXX8aTlU4sysSADRs26Mcff1RWVpaaNWumevXqOT1S3KK1s2bNmqUnn3xS8+fPd3qUuPHPw1fk5eVp+fLl+vHHH9WjRw89/fTTDk0GlMxvv/2munXrOj0GEBFut1tbtmxR1apVw34KjE9+RcbhFhnz8aJe9H311Ve69NJL+Uc5Ys7YsWM1fPhwTZ8+XZ06dQq5bNasWerRo4cefPBB9evXz6EJgSO3e/duLV68WNu2bZPf7w+5jPNAIBbNnDkz5M/5bwx+6aWXNHToUN10000OTRZfLr74YtWuXVsTJ04s9PLbbrtNGzZs0OzZs40nK51YlIkR+XcTJ+aOPlo755dfflGTJk20d+9ep0eJe4888oiysrI4DnOU/Pnnn5Kk448/3uFJ4o/b7dZ5552nm266SVdccYXKlCnj9Ehxz+fz6f3339eqVaskSaeddpo6d+7Mp0gRk3w+n77++ms1btxYFSpUcHqcuPef//wn5M+BQECbN2/W9OnTdd555+m1115zaLL4t3//fuXm5oZsS01NdWia+OH3+9WtWze9++67ql+/vk499VQFAgGtWrVKP//8s/71r3/pnXfeKfLhVnF4+b/3XXnllUpJSXF6nLj34Ycf6tprr1VWVpZSU1NDXhdxuVwcIq6EKlasWOTXmGhs57XXXtObb75ZYNEGJbNw4UK1bdtWXbp00X333acGDRoEf0aOGTNGM2fO1Lx583TOOec4PWqpwKJMKTd16lSNHTtWP//8sySpXr166tu3r26++WaHJ4s/tLaTmZkZ8uf8f6A/8sgjWr16tZYvX+7MYEeRX375RS1btuQXvgjy+/0aPny4xowZo6ysLElS+fLlde+99+qhhx7iH+cRsnz5ck2bNk2vv/66cnNz1a1bN910003BY+cjsn755Rd16tRJGzduVP369SVJa9asUa1atTRr1iyddNJJDk8IFF+ZMmW0atWq4LlkED3/bOx2u1WlShW1a9dOAwcO5ITzEbZv3z498MADeuutt7Rz584Cl/OJu8h588039frrr2vt2rWSDvzb8eqrr9a///1vhyeLP3379tVrr72mnJwcXXXVVbrpppt09tlnOz1W3DrllFPUqVMnPf744ypbtqzT48SNon5aV+ITu5Z+++03NW7cOPjvdxy59957T7fcckuB15oqVqyoyf+vvTuPqzHv/wf+OqdFSSqUZdo0kSJEZN8jkiWjhkkrxt7cljD2sTcIg7GlMEaSfezZGpHdkSEqUbZmxl4iLb8//DrfObLMcLqu0+X1fDx63HfXdWbu1+Nze3ycc96f6/1esQK9evUSKZnmYVFGg02ePBkLFizA8OHD0bRpUwDAyZMnsWTJEvzvf//DDz/8IHJC6eBaC+tt/TwLCwthYWGBqKgo5f8HVHLWr1+PsWPH4u7du2JHkYzx48cjPDwc06ZNU578OH78OKZOnYoBAwZg5syZIieUlry8POzcuRORkZHYt28fatasicDAQPTr1085xJs+XZcuXVBYWIgNGzYoh7s+ePAAPj4+kMvl2L17t8gJpeXQoUM4dOjQW9uFrFmzRqRU0uPs7Iy5c+eiffv2YkchUquhQ4fiyJEjmD59Ovr164elS5fizp07WLFiBebMmYNvvvlG7IhEH6Xofd/atWuxd+9e2NraKt/3Va5cWex4kmJgYIDExES2CybJy8nJwfjx47F3717lHDZSj+fPn2P//v0qh947derEQu8bWJTRYKampli8eDH69Omjcn3jxo0YPnw4/v77b5GSSQ/XWljHjh1T+b3o1KStrS20tbVFSiVNnp6eKr8XPZV09uxZTJo0CVOmTBEpmfRUq1YNy5cvLzaMe8eOHRgyZAju3LkjUjJpe/nyJZYtW4bx48cjNzcXurq68PLywty5c1G1alWx45V6BgYGSEhIgKOjo8p1hUKB5s2b81SZGk2bNg0//PADnJ2dUbVq1WKHF7Zt2yZSMunZt28fxo8fj+nTp6Nhw4YwMDBQuc8WT+r1+PFjpKSkQFdXF9WrV+fTMSXI0tIS69atQ5s2bVC+fHmcP38etra2WL9+PTZu3Mge7mqWmpqKiIgI3LhxAwsXLoSZmRn27t0LS0tL1K5dW+x4kvXnn39i5cqVmDlzJvLz89GlSxeMGDEC7dq1EzuaJHh6euLrr7+Gl5eX2FEk5c1uIe/D9yHq92b7uMLCQjx79gxly5bFL7/8UuwzPJEQ+O2nBnv16hWcnZ2LXW/YsCHy8vJESCRdXGthtW7dWuwInw0jIyOV3+VyOezs7PDDDz+gY8eOIqWSpocPH6JWrVrFrteqVYtt4krA2bNnsWbNGkRFRcHAwACjR49GUFAQbt++jWnTpqF79+44ffq02DFLvTJlyuDZs2fFrmdlZUFXV1eERNK1fPlyREZGol+/fmJHkbyi4dzdunUr9gFdJpOxxZOa3Lx5E0OHDsX+/fuVMxu1tbXh6emJhQsXKk+3v3z5EmXKlBEzqmQ8fPhQebq9fPnyyvcfLVq0wODBg8WMJjnHjh1D586d0bx5c8TFxWHGjBkwMzODQqFAeHg4YmJixI4oSadPn0ZERASioqJgZmYGf39/3LlzB127dsWQIUM4L1MN3N3dMWbMGFy5cgWOjo7Q0dFRuc8vrz+OsbHxB2fK8H1IyQkLC1NZ/6KDwS4uLjAxMRExmXS8OUfwfUaMGFGCSUoPPimjwYYPHw4dHR0sWLBA5fro0aORk5ODpUuXipRMerjWwtq8ebOyB7Ouri5q1qyJgIAAdOrUSexoRB/NxcUFLi4uxd6MDB8+HGfOnEFCQoJIyaQhMDAQixYtwqpVqxAREYFr166hS5cu6N+/P7p06aIys+f27duwtrZmUV0NfH19cf78eYSHhyvn9pw6dQoDBgxAw4YNERkZKW5ACalYsSJOnz7NOT0CePOJ3Tfx8Miny8jIQKNGjaCjo4MhQ4bA3t4eAHDlyhX8/PPP0NbWxoULFxAXF4erV69i7NixIieWhrp16+Knn35C69at0aFDB9SvXx/z5s3D4sWLERoaitu3b4sdUTKaNm2K3r17Y+TIkTA0NIRCoYCNjQ1Onz4NT09PrrUaxMXFoVmzZnj48CHWr1+PiIgIJCcnw8PDA/3790enTp2UX7IeP34cbm5ufIJXDd43B5MFg4/3ofceRRITEzFs2LASTvP5SU9Ph4WFxVsLY+np6bC0tBQhlbT821mNMpkMN27cKOE0pQOLMhps+PDhWLduHSwsLJSD7E6dOoX09HT4+vqqnFh4s5hA/w3XWhgFBQXo06cPNm/ejJo1ayqfKrh69SpSUlIwcOBA/Pzzz3jw4AHi4uLQs2dPkROXfhkZGZDJZDA3Nwfw+nTZr7/+CgcHBwwcOFDkdNJy7NgxuLu7w9LSUmU2VUZGBvbs2YOWLVuKnLB009LSwr1799C8eXMEBgbC39//ne3JcnNzsXHjRg7JVIPHjx/Dz88Pu3btUv5dmJeXh27duiEyMrLY03j08caOHYty5cph0qRJYkeRrIsXL6J+/fpix/gsBAUFISUlBfv374eenp7KvZycHLi5uaGgoABnz55FVFQUunfvLlJSaQkLC4OWlhZGjBiB2NhYeHh4oLCwEK9evcKCBQsQHBwsdkTJKFeuHBITE5Ut+YqKMjdv3kStWrXw4sULsSOWekXv/czNzfHll18q3/+9bXbg06dP0b17dxw5ckSEpESf5tmzZ9i4cSNWr16Nc+fOsfBVAor2EzMzM5XrDx48gJmZGdecRMGijAZr27btv3qdTCbD4cOHSziNtHGthREWFoYZM2Zg7dq16Nq1q8q9nTt3IiAgAOPHj0dkZCR8fX0REhIiUlLpaNmyJQYOHIh+/frh/v37qFmzJurUqYPk5GQMHz4ckydPFjuipNy9exdLly5FUlISAMDe3h5DhgxBtWrVRE5W+snlcty/f7/YG2kSRkpKCq5evQrg9Z9rW1tbkRNJT3BwMNatW4e6deuibt26xdqF8FDIpytTpgymTJmCcePGvfckMH26L774Aps2bUKLFi3eej8uLg5t2rTB6tWrERgYKHC6z8etW7dw7tw52Nraom7dumLHkRRzc3NER0ejWbNmKkWZbdu2YfTo0UhNTRU7YqlX9N7v2rVrPNwkkhcvXhQrrJP6xMXFITw8HFu2bEG1atXg6emJXr16oVGjRmJHk5x3fZa8desWHBwckJ2dLVIy6SsqO3yofd/niEUZIhJM3bp18d13373zw3d4eDgGDhyIjh07YseOHZxXoAYmJiZISEiAnZ0dFi9ejE2bNiE+Ph4HDhzAoEGD+NgolRpyuRzJyclvPR35TxyMqT5Pnz5FuXLlin15XVBQgKysLK61mr3vgAgPhajHnj17MHDgQJibm2P9+vWoUaOG2JEkq0yZMkhNTVU+qfum27dvw8bGBrm5uQInk7Z169bB29u72Iye3NxcREVFwdfXV6Rk0jN69GicOnVK2QHg/PnzyMzMhK+vL3x9fTFlyhSxI5Z6crkcmZmZH3zvR+qVn5+PWbNmYfny5cjMzMT169dhY2ODSZMmwdraGkFBQWJHLNXu37+PyMhIhIeH4+nTp/Dy8sLy5cuhUCjg4OAgdjzJGTlyJABg0aJFGDBgAMqWLau8l5+fj1OnTkFLSwvx8fFiRZSsdevW4ccff0RycjIAoGbNmhgzZgznZ/4Dj4hpsIiICOTk5Igd47PAtRZGcnIyOnTo8M77RfdYkFGfV69eKT+Yx8bGKgcz1qpVC/fu3RMzmmQkJyejT58+ePr0abF7T548Qd++fVn8UpOaNWvCxMTkrT/GxsYc0qhG27Ztg7Oz81vbr+Tk5KBRo0bYtWuXCMmk68iRI+/8YUFGPbp06YI//vgDtWrVgpOTE3766SexI0lW1apVceXKlXfev3z5Mp8iLQEBAQF48uRJsevPnj1DQECACImka9asWahVqxYsLCyQlZUFBwcHtGrVCs2aNcPEiRPFjicZ/v7+8PT0fO8PqdfMmTMRGRmJ0NBQlc/kderUwerVq0VMVvp5eHjAzs4Oly5dwsKFC3H37l2+FylhFy5cwIULF1BYWIjExETl7xcuXEBSUhLq1avHGZklYMGCBRg8eDC6dOmC6OhoREdHw83NDYMGDUJYWJjY8TQGn5TRYJUrV0ZOTg569+6NoKAgNGvWTOxIksW1FkaFChVw9OjRd7ZPSExMRKtWrfDo0SOBk0mXi4sL2rZtC3d3d3Ts2BEJCQmoV68eEhIS8NVXX3EIqRoMHDgQxsbGCA0Nfev9sWPH4unTp/j5558FTiYtcrkcW7ZsQYUKFd77Og7oVo+OHTvCy8sL/fv3f+v9NWvWYNOmTdi/f7/AyYjUIyYmBl9//TUMDAygpaWlcu/hw4cipZKO7777DocPH8ahQ4eKnXL/888/4erqirZt22LhwoXiBJSodz1ZoFAo0LZtW/7ZLgEZGRlITExEVlYWnJyc+ASeGsnlcnh5eUFfX/+9r4uIiBAo0efB1tYWK1asQPv27VVa8yUlJaFp06b8rP4JtLW1MWLECAwePFhlr9DR0eGTMiUsICAAixYt4pP+AqlevTqmTZtW7AndtWvXYurUqUhLSxMpmWZhUUaD5eXlYdeuXYiMjMTevXthY2ODgIAA+Pn5oUqVKmLHkxSutTCKhqC/68vpQYMGIT09HXv27BE4mXQdPXoUPXv2xNOnT+Hn54c1a9YAAL7//nskJSVh69atIics/ezs7PDLL7+8s/fvuXPn0LdvX1y7dk3gZNLCmTLCqlatGuLi4t45OyYlJQWtWrXC3bt3BU4mLZ6enoiMjET58uU/eNqX+7X6nDlzRvkhcfTo0dDW1la57+fnJ0YsSXn06BFcXFxw//59+Pj4oFatWigsLMTVq1fx66+/okqVKkhISPhgoZ3+HScnJ8hkMigUCtSuXVvlz3R+fj7S0tLg5uaG6OhoEVMS/Td87ycOfX19JCUlwcrKSqUoc+XKFTRu3BhZWVliRyy1EhISEB4ejk2bNsHe3h79+vXD119/japVq7IoQ5Kip6eHy5cvF/ssmZycDEdHx7d2Y/gcaX/4JSQWbW1t9OzZEz179kRmZiZ++eUXrF27FpMmTYKbmxuCgoLg4eHBQaVqwLUWxoQJE9CmTRs8ePAAo0ePVvmAPn/+fOzYsQNHjhwRO6aktGnTBn///TeePn2q0tpp4MCBKv1U6eOlp6e/98NipUqVkJGRIWAiok/36NEj5OXlvfP+q1eveFJSDYyMjJRDL42MjEROI315eXmYMmUK5s2bh6FDh2LWrFkcYFxCTExMcOrUKXz//feIiorC48ePAQDGxsbo27cvZs6cyYKMGvXo0QMAcPHiRXTq1AnlypVT3tPV1YW1tTV69eolUjpp6tWrFxo3boyxY8eqXA8NDcWZM2ewefNmkZJJB4dCi8PBwQG///47rKysVK7HxMTAyclJpFTS0KRJEzRp0gQLFy7Epk2bsGbNGowcORIFBQU4ePAgLCwsYGhoKHZMyTp79iyio6ORnp5ebKYdDz+pl62tLaKjo/H999+rXN+0aROfKP0HFmVKicqVK6NFixa4fv06rl+/jsTERPj5+cHExAQRERFo06aN2BElg2tdcpo1a4ZNmzZh4MCB2LJli8o9ExMTbNy4Ec2bNxcpnXRpaWkhLy8Px48fB/D6yQ5ra2txQ0mIkZERUlNTi31wKZKSksLHpNXAysqqWIshKjnW1tY4e/YsatWq9db7Z8+efeefefr3IiIicPjwYbRq1YrtVwTQoEEDZGVlYf/+/Xw/JwATExP8/PPPWLZsGf766y8AgKmpKb9oLQFFQ+Wtra3h7e3NYqMA4uLiMHXq1GLXO3fujPnz5wsfSILY1EUckydPhp+fH+7cuYOCggJs3boV165dw7p16/Dbb7+JHU8SDAwMEBgYiMDAQFy7dg3h4eGYM2cOxo0bB1dXV+zcuVPsiJITFRUFX19fdOrUCQcOHEDHjh1x/fp1ZGZmomfPnmLHk5xp06bB29sbcXFxyu/44uPjcejQIT61+w889q/hMjMzMW/ePNSuXRtt2rTB06dP8dtvvyEtLQ137tyBl5cXWyyoCddaGD179sStW7ewZcsWzJ49G7Nnz0ZMTAzS09N5gq8EZGdnIzAwEFWrVkWrVq3QqlUrVKtWDUFBQXj+/LnY8SShVatW7x3QuHjxYrRs2VLARNKUlpaGihUrAnh92j02NhYrVqzAs2fPAAB3795lOwU18vT0xIQJE5CZmVns3v379zFx4kTu2Wri6uqqMuehSZMmuHPnjoiJpKtx48ZQKBQsyAgsPz8fly5dwrZt25T7NPfskuHn5wc9PT3k5ubi9u3bSE9PV/kh9cnKylIZgl5ER0cHT58+FSGR9Bw5cgQmJiZo3749kpOTxY4jeTdu3EBhYSG6d++OXbt2ITY2FgYGBpg8eTKuXr2KXbt2wdXVVeyYkmNnZ4fQ0FDcvn0bGzduFDuOZM2aNQthYWHYtWsXdHV1sWjRIiQlJcHLywuWlpZix5OcXr164dSpU6hUqRK2b9+O7du3o1KlSjh9+jSLYP/AmTIayMbGBmfOnIG/vz/279+PmjVron///vD19S32mP+ff/6JKlWqoKCgQKS0pRvXWhzr1q2Dt7c3ypQpo3I9NzdXeYKB1OPbb79FbGwslixZojyhcPz4cYwYMQKurq4cPq8GFy5cQNOmTdG1a1eEhITAzs4OAJCUlITQ0FDs3r0bJ06cQIMGDUROKg23bt2Cm5sb0tPT8fLlS1y/fh02NjYIDg7Gy5cvsXz5crEjSsKzZ8/QtGlTpKenw8fHR+XP9YYNG2BhYYGEhAS2WFCDN3vm/7N/O5WcvLw8HD16FKmpqejbty8MDQ1x9+5dlC9fXqX1E30a7tnCSk5ORmBgIE6cOKFyvbCwEDKZDPn5+SIlk57GjRuja9eumDx5ssr1qVOnYteuXTh37pxIyaTH1NQUJ06cYMubEqalpYV79+4p3494e3tj8eLFqFy5ssjJiD6dgYEB/vjjD1hbW6NixYo4evQoHB0dcfXqVbRr1w737t0TOyJ9hti+TAPdunUL+fn5MDMzw7Fjx9C0adN3vtbU1BRpaWkCppMWrrU4AgIC4ObmVmwOx7NnzxAQEMCijBpt2bIFMTExKqeCu3TpAn19fXh5ebEoowZOTk6IiYlBYGAgtm3bpnKvYsWKiI6OZkFGjYKDg+Hs7AyFQqF8cgZ4/RTegAEDREwmLYaGhoiPj8f48eOxadMm5fwYY2Nj+Pj4YObMmSzIUKn1ZqHA1dUVhoaGmDt3LgsFasY9W1j+/v7Q1tbGb7/9hqpVq7JVXAmaNGkSPD09kZqainbt2gEADh06hI0bN3KejJr5+Pgo2ztRyXnzvPbevXuRnZ0tUhoi9TIxMVF2WPjiiy9w+fJlODo64vHjx+wgUkLy8/Oxfft2XL16FQBQu3ZtdOvWjS3J/4FFGQ1U9JdheHj4B18rk8nY0/0TcK3FUXRa7023b9/moGM1e/78+VtPN5mZmfHNhxp17doVt27dwv79+5GcnIzCwkLUrFkTHTt2RNmyZcWOJym///47Tpw4UaxliLW1NVs+qZmRkRGWLVuGpUuX4u+//0ZhYSHnQZQAmUymsqZv/k7qx0KBcLhnC+vixYs4d+7cO+eBkfp4eHhg+/btmDVrFmJiYqCvr4+6desiNjYWrVu3FjuepOTl5WHNmjWIjY1Fw4YNYWBgoHJ/wYIFIiWTNjbVISlp1aoVDh48CEdHR/Tu3RvBwcE4fPgwDh48iPbt24sdT3JSUlLg7u6O27dvKzsuzJ49GxYWFti9eze+/PJLkRNqBhZlNNT+/fs/+OV0t27dBEojbVxr4Tg5OSm/bGrfvj20tf9vC8rPz0daWhrc3NxETCg9TZs2xZQpU7Bu3Trl0NecnBxMmzbtvU+G0X+3efNmeHt7o0ePHirX2ZZPvQoKCt7afuX27dt8cqOE5OfnQ6FQsM1TCSksLFT5O/H58+fw8PAo9iX2+fPnxYgnSSwUCId7trAcHBzw999/ix3js+Hu7g53d3exY0je5cuXlU+dX79+XeUeDzGoz9sOhXB9SSqWLFmCFy9eAAAmTJgAHR0dnDhxAr169cLEiRNFTic9I0aMgI2NDU6ePKkcDfHgwQP4+PhgxIgR2L17t8gJNQNnymgguVz+wdewJ7B6cK2FNW3aNOV/jho1SuXLPF1dXVhbW6NXr15vHZpJH+fy5cvo1KkTXr58iXr16gEAFAoF9PT0sH//ftSuXVvkhNLxZh/mIg8ePICZmRn3ETXx9vaGkZERVq5cCUNDQ1y6dAmmpqbo3r07LC0tERERIXZESeE8iJJX9Hfjh0yZMqWEk3w+TExMEB8fDwcHB5UZPsePH0evXr2QmZkpdkTJ4J4trMOHD2PixImYNWsWHB0doaOjo3K/fPnyIiUjIk0nl8vRuXNn5dzXXbt2oV27dsWeTNq6dasY8YioFDEwMEBCQgIcHR1VrisUCjRv3hxZWVkiJdMsLMpooDcHvlLJ4VqLY+3atfD29lY+uUEl6/nz59iwYQOSkpIAAPb29vjmm2+gr68vcjJpkcvlyMzMhKmpqcp1hUKBtm3b4uHDhyIlk5bbt2+jU6dOKCwsRHJyMpydnZGcnIxKlSohLi6O+7ma9ejRA4aGhggPD0fFihWVX14fPXoUAwYMQHJystgRif4zFgqEwz1bWEUHzt483V7UOpgHRNQnPz8fYWFhiI6ORnp6OnJzc1Xu830flTYBAQH/6nX8O5JKq9TUVERERCA1NRWLFi2CmZkZ9u7dC0tLSx5WVbMKFSrgt99+Q7NmzVSux8fHw8PDg39H/n8symigd522JvXjWosvKysLBQUFKtd4io9Kk6K2fAqFArVr135nW77o6GgRU0pLXl4eoqKicOnSJWRlZaFBgwYsNJaQihUr4sSJE7Czs1N5ouDmzZtwcHDgbCo1y8vLw9GjR9kqroSxUCAs7tnCOXbs2Hvvc9aJ+kyePBmrV6/GqFGjMHHiREyYMAE3b97E9u3bMXnyZIwYMULsiJKRnZ2NOXPm4NChQ/jzzz+LfXa8ceOGSMmIqLQ4duwYOnfujObNmyMuLg5Xr16FjY0N5syZg7NnzyImJkbsiJLi6+uL8+fPIzw8HI0bNwYAnDp1CgMGDEDDhg0RGRkpbkANwZkyGoh1MuFwrcWRlpaGYcOG4ejRo8q+ngBP8ZWUa9eu4aeffsLVq1cBvH5SZtiwYRwCqyZFM2QuXryITp06vbMtH6mPtrY2fHx8xI7xWeA8COG82SrO1dUVhoaGmDt3LlvFqZm5uTkUCoVKoSAoKIiFghLCPVs4LLoIZ8OGDVi1ahXc3d0xdepU9OnTB19++SXq1q2LhIQEFmXUqH///jh27Bj69euHqlWrcs4JEf1n48aNw4wZMzBy5EiVzzDt2rXDkiVLREwmTYsXL4a/vz+aNWumPLSal5eHbt26YdGiRSKn0xwsymggPz8/fiAUCNdaHD4+PigsLMSaNWtQuXJlvrEuQVu2bMHXX38NZ2dnNG3aFACUvT2joqJYLFCDojkP1tbWbMsngJ07d771ukwmg56eHmxtbVG9enWBU0lXx44dsXDhQqxcuRLA63XOysrClClT0KVLF5HTSUtwcDCcnZ2hUChQsWJF5fWePXtiwIABIiaTJhYKhME9W3iPHz9GeHi48jBO7dq1ERgYCCMjI5GTScv9+/eVvfLLlSuHJ0+eAAC6du2KSZMmiRlNcvbu3Yvdu3ejefPmYkcholIqMTERv/76a7HrZmZm+Pvvv0VIJE0FBQX48ccfsXPnTuTm5qJHjx7w8/ODTCaDvb09bG1txY6oUViU0UD/7NFZUFCAlJSUtz6m26pVK6GjSQ7XWhwKhQLnzp2DnZ2d2FEkLyQkBOPHj8cPP/ygcn3KlCkICQlhUUaN/Pz8lP+dbflKTo8ePSCTyYo96Vh0TSaToUWLFti+fTtMTExESikd8+fPR6dOneDg4IAXL16gb9++yjZPGzduFDuepPz+++84ceIEdHV1Va5bW1vjzp07IqWSJhYKhMM9W1hnz55Fp06doK+vr2wXsmDBAsycORMHDhxAgwYNRE4oHebm5rh37x4sLS3x5ZdfKtf3zJkzykHppB4mJiaoUKGC2DGIqBQzNjbGvXv3ir2/u3DhAr744guRUknPzJkzMXXqVHTo0AH6+vrYs2cPjIyMsGbNGrGjaSTOlNFgCQkJ6Nu3L27duvXWDzJs8aQ+XGthtW3bFhMmTECHDh3EjiJ5ZcuWxaVLl4qdSEhOTka9evU4D0KN2JZPGIcOHcKECRMwc+ZM5RdOp0+fxqRJkzBx4kQYGRnh22+/hYuLC8LDw0VOKw2cByEMExMTxMfHw8HBQWV+z/Hjx9GrVy9kZmaKHVEy5HI5CwUC4Z4trJYtW8LW1harVq1SaRfSv39/3LhxA3FxcSInlI5x48ahfPny+P7777Fp0yb4+PjA2toa6enp+N///oc5c+aIHVEyfvnlF+zYsQNr165F2bJlxY5DRKXQ6NGjcerUKWzevBk1a9bE+fPnkZmZCV9fX/j6+iq7X9CnqVGjBkaPHo1vv/0WABAbGwt3d3fk5ORALpeLnE7zsCijwerXr4+aNWti2rRpb+2dykfQ1YdrLazU1FQMGjQIPj4+qFOnDnR0dFTu161bV6Rk0tOlSxf07t0bAQEBKtcjIiIQFRWF/fv3i5RMepo3b47CwkIEBwe/tS0f+7yrR506dbBy5Uo0a9ZM5Xp8fDwGDhyIP/74A7GxsQgMDER6erpIKYn+O29vbxgZGWHlypUwNDTEpUuXYGpqiu7du8PS0lLl6V76NCwUCId7trD09fVx4cKFYnMDr1y5AmdnZx7GKUEnT57EyZMnUaNGDXh4eIgdR1KcnJyQmpqKwsJCWFtbF/vseP78eZGSEVFpkZubi6FDhyIyMhL5+fnQ1tZGfn4++vbti8jISGhpaYkdURLKlCmDlJQUWFhYKK/p6ekhJSUF5ubmIibTTGxfpsGSk5MRExPDnnsC4FoL66+//kJqaqpKoeCfp1P5RIH6dOvWDWPHjsW5c+fQpEkTAK+fDNu8eTOmTZum0sKlW7duYsWUBLblE0ZqaupbW8GVL18eN27cAPD6hA57A6sH2zwJh63ihBMcHFysUNC+fXvo6ekpCwULFy5EYGCgiCmlgXu2sMqXL4/09PRiRZmMjAyVwcakfk2bNlXObyT16tGjh9gRiKiU09XVxapVqzB58mQkJiYiKysLTk5OqFGjhtjRJCUvL6/YjF0dHR28evVKpESajUUZDebi4oKUlBQWCgTAtRZWYGAgnJycsHHjxrc+UUDqM2TIEADAsmXLsGzZsrfeA9imTx0aNWqEjIwMFmVKWMOGDTFmzBisW7cOpqamAF4XekNCQtCoUSMArwvt/zydQx+P8yCEY25uDoVCodIqLigoiK3iSgALBcLhni0sb29vBAUFYd68ecqiY3x8PMaMGYM+ffqInK70e9dBhbfhYadPl5+fDy0tLbYVIqKP1qpVK+zcuRPGxsYAXs+QcXV15XvrElJYWAh/f3+V2WovXrzAoEGDYGBgoLy2detWMeJpHLYv02Dbtm3DxIkTMWbMGDg6OrLFUwniWgvLwMAACoWCRTCSFLblE8a1a9fQvXt3pKWlKb/Ey8jIgI2NDXbs2IGaNWti+/btePbsGfr16ydy2tKPbZ5Iilq0aAFDQ8NihQJfX19kZ2cjLi4OsbGxGDp0KK5duyZy2tKNe7awcnNzMWbMGCxfvhx5eXkAXp9QHTx4MObMmcMB9J/o3/bD52En9ahSpQr8/f0RFBTE0+xE9FHkcjnu378PMzMzAK8P4Fy8eBE2NjYiJ5OmN9vmvwvbMr/GoowGe9ubPrZ4Khlca2F5eHjA398fvXr1EjvKZ+358+cclqlGCQkJ6Nu3L27evKm8xn2kZBQUFODAgQO4fv06AMDOzg6urq4cHlgCOA9COGwVJxwWCoTFPVsY+fn5iI+Ph6OjI8qUKYPU1FQAwJdffsn3e1QqTZ8+HWvXrkVaWhqaNWuGoKAgeHl58c8zEf1rbxZlDA0NoVAoWJQhjcCijAa7devWe+9bWVkJlET6uNbCWrlyJWbMmIHAwMC3PpnEx/3Vp3379li3bh2++OILleunTp1Cv379lF+Q0KdzcHCAvb09QkJC3tqWj/sIlUb6+vo4c+YM6tSpo3I9MTERjRs3Rk5ODm7dugV7e3sOkP5EcrmcreIExEIBSZGenh6uXr3KAi5JytGjRxEREYEtW7ZAS0sLXl5e6N+/P1xcXMSORkQajkUZ0mQsyhCR4N73hQefKFAvd3d3JCQkYNmyZfD29kZBQQF++OEHzJo1C0OGDMHChQvFjigZbMsnnOzsbBw7dgzp6enIzc1VuTdixAiRUkkT2zwJh63iSKq4ZwvH2dkZc+fORfv27cWO8lk4dOgQwsLCcPXqVQCAvb09vvvuO3To0EHkZNKUlZWFqKgoREZG4sSJE7C3t0dQUBBGjhwpdjQi0lByuRxr166FkZERAKBPnz5YuHAhKleurPI6HgwmMbAoo2F27tyJzp07Q0dH54ODBLlpfBquNX0uli5dipCQEHTv3h03b97ErVu3EBERgY4dO4odTVLYlk8YFy5cQJcuXfD8+XNkZ2ejQoUK+Pvvv1G2bFmYmZkph3STerDNk3DYKk5YLBQIg3u2sPbt24fx48dj+vTpaNiwocpQXeB1L31Sj2XLliE4OBhfffUVmjZtCuB1K9uYmBiEhYVh6NChIieUtt27d8PX1xePHz/mgT4ieqd/8wQ0DwaTWFiU0TD/fLSOTxOULK61Znn8+DGMjY3FjiFZ48ePx9y5c6GtrY2jR48W+9KPPh3b8gmjTZs2qFmzJpYvXw4jIyMoFAro6OjAx8cHwcHB8PT0FDui5LDNkzDYKk44LBQIh3u2sP65L/+zjSrn26mfubk5xo0bh2HDhqlcX7p0KWbNmoU7d+6IlEy6nj9/jujoaEREROD48eP48ssvERgYiHHjxokdjYiI6D9jUYaIBDd37lxYW1vD29sbANC7d29s2bIFVatWxZ49e1CvXj2RE0rHo0eP0L9/fxw6dAg//vgjjh07hu3btyM0NBRDhgwRO56ksLgrDGNjY5w6dQp2dnYwNjbGyZMnYW9vj1OnTsHPzw9JSUliRyT6KGwVJxwWCoTDPVtYx44de+/91q1bC5RE+sqVK4eLFy8Wa1ubnJwMJycnZGVliZRMek6cOIE1a9Zg8+bNyMvLw1dffYWgoCC0atVK7GhEREQfjUccNZClpSUePHig/H3JkiV4+vSpiImki2stjuXLlyvb4Bw8eBCxsbHYt28fOnfujDFjxoicTlrq1KmDzMxMXLhwAQMGDMAvv/yC8PBwTJo0Ce7u7mLHk5SCgoJ3/rAgoz46OjrKApiZmZmyjZORkREyMjLEjCZZ2dnZ2LNnD5YvX47Fixer/JD6hIeHIy0tDebm5rC1tYWtrS3Mzc1x8+ZNrF69GsDrfvoTJ04UOWnpd/HiRYwaNQpyuRxaWlp4+fIlLCwsEBoaiu+//17seJLCPVsYvr6+ePbsGVq3bo3WrVvD2NgYzZo1U/5e9EPq061bN2zbtq3Y9R07dqBr164iJJKe0NBQ2Nvbo2XLlkhMTMSPP/6I+/fvY+3atSzIENF/Mnv2bKxZs6bY9TVr1mDu3LkiJCLikzIa6Z9ttYDXvX8vXrwIGxsbkZNJD9daHPr6+rh+/TosLCwQHByMFy9eYMWKFbh+/TpcXFzw6NEjsSNKxvTp0zFhwoRiT3Hcvn0bAQEBOHjwoEjJPg9sy6d+HTt2hL+/P/r27YsBAwbg0qVLGDFiBNavX49Hjx7h1KlTYkeUFLZ5EhZbxQnD1NQUJ06cQI0aNVCzZk389NNP6NSpE5KSktCwYUNkZ2eLHVEyuGcLQ0tLC/fu3eNnGgHNmDED8+bNQ/PmzVVmysTHx2PUqFEq83s4p+rjmJqawsfHB0FBQcVaexIR/RfW1tb49ddfi7VxP3XqFL7++mukpaWJlIw+ZyzKaKA3CwWGhoZQKBR8U10CuNbiqFatGmJiYtCsWTPY2dlhxowZ6N27N65du4ZGjRrxaSUqldiWTxhnz57Fs2fP0LZtW/z555/w9fVVfrm6Zs0arrOasc0TSRELBcLhni0MfqYRXvXq1f/V62QyGQ8wfKRXr14Vm9FYhAefiOi/0NPTw9WrV4vt3Tdu3ICDgwNevHghUjL6nGmLHYCIPj+enp7o27cvatSogQcPHqBz584AXp/IfrMvM32c0NBQDB8+HPr6+gCA+Ph4ODs7o0yZMgCAZ8+eYezYsVi2bJmYMSVl+fLl2LBhAwDVtnzR0dEYM2YMDhw4IHLC0q+wsBBmZmbK05JmZmbYt2+fyKmk7eLFi1ixYoVKmycbGxuEhobCz8+PRRk1y87OxrFjx5Ceno7c3FyVezxprT6zZs3Cs2fPAAAzZ86Er68vBg8erCwUkHpwzyYp46nqkldUkHnz4JOXlxe2bNmCKlWq8OATEf0rFhYWiI+PL1aUiY+PR7Vq1URKRZ87FmU01OrVq1GuXDkAQF5eHiIjI1GpUiWV1/DDuXpwrYUXFhYGa2trZGRkIDQ0VLn+9+7d4/B5NRk/fjz8/f2VRZnOnTurtLF4/vw5VqxYwaKMGt2/f185K+m3336Dl5cXOnbsCGtra7i4uIicThoKCwtha2uLP/74AzVq1BA7zmfhbfMg7O3tOQ+iBHyoVRzfi6gHCwXC4Z4trCtXruD+/fsAXq99UlJSsWHzdevWFSOapOXm5iItLQ1ffvkltLX59UpJePPg08GDB7F3714efCKif23AgAH47rvv8OrVK7Rr1w4AcOjQIYSEhGDUqFEip6PPFduXaSBra2vIZLL3voaPQasH11ocDx48QMWKFQEAGRkZWLVqFXJycuDh4cGhjWryoTYWmZmZqFatGgfQqxHb8gmjdu3aCA8PR5MmTcSO8llgmyfhsFWcMAoKCqCnp8dCgUC4ZwtDLpdDJpPhbR/ti67LZDK+71Oj58+fY/jw4Vi7di0A4Pr167CxscHw4cPxxRdfYNy4cSInlA7OIyWiT1VYWIhx48Zh8eLFyqfR9fT0MHbsWEyePFnkdPS54lEODXTz5k2xI3w2uNbCSkxMhIeHBzIyMlCjRg1ERUXBzc0N2dnZkMvlCAsLQ0xMDHr06CF2VKL/jG35hDFnzhyMGTMGP//8M4e+CoBtnoTDVnHCkMvlyn2aRZmSxz1bGGylJbzx48dDoVDg6NGjcHNzU17v0KEDpk6dyqKMGpmYmCAjIwMWFhbYt28fZsyYAeD1l6wsNBLRvyGTyTB37lxMmjQJV69ehb6+PmrUqKFs704kBrnYAejd1q1bh5cvXxa7npubi3Xr1omQSLq41sIICQmBo6Mj4uLi0KZNG3Tt2hXu7u548uQJHj16hG+//RZz5swROybRRwkLC8OwYcPg4OCAgwcPsi1fCfH19cXp06dRr1496Ovro0KFCio/pD5FbZ6aNm0K4P/aPD19+hTnzp1jD3c1e1urOABsFVcCigoFly9fFjuK5HHPFoaVlRWsrKwgk8lgaWmp/P2fPx/qDkD/zfbt27FkyRK0aNFCZW1r166N1NRUEZNJT9HBJ1dXVx58IqJPUq5cOTRq1AiWlpbYu3cvrl69KnYk+oyxfZkG09LSwr1795Tth4o8ePAAZmZmPBWiRlxrYVSqVAmHDx9G3bp1kZWVhfLly+PMmTNo2LAhACApKQlNmjTB48ePxQ0qAXK5HDNmzFAWBsaOHYsxY8Yo5yU9e/YMkydP5p9tNWJbPmEUtQl5Fz8/P4GSSB/bPAmLreKEY2JigufPnyMvLw+6urrK+WtFHj58KFIy6eGeLSx+phFO2bJlcfnyZdjY2Ki0CVYoFGjVqhWePHkidkTJePXqFRYtWoSMjAz4+/vDyckJwOsDUYaGhujfv7/ICYlI03l5eaFVq1YYNmwYcnJyUK9ePdy8eROFhYWIiopCr169xI5InyG2L9NgRb1/33T79m0YGRmJkEi6uNbCePjwIapUqQLg9QkFAwMDmJiYKO+bmJgo2+TQp7G0tMSqVauUv1epUgXr168v9hr6dGzLJyx+gScctnkSFlvFCWfhwoViR/hscM8W1rs+02RlZUFPT0+ERNLl7OyM3bt3Y/jw4QCgXPfVq1crnzAl9dDR0cHo0aOLXf/f//4nQhoiKo3i4uIwYcIEAMC2bdtQWFiIx48fY+3atZgxYwaLMiQKFmU0kJOTE2QyGWQyGdq3bw9t7f/7vyk/Px9paWkqfWvp43GthffmB0W2UigZnJcknKK2fBs2bMD69euVbfmKimLDhw/HnDlzWJQpAS9evFAOaixSvnx5kdJIE+dBCKOoVVzRGhe1iqOSwUKBOLhnl5yRI0cCeP2+etKkSShbtqzyXn5+Pk6dOoX69euLlE6aZs2ahc6dO+PKlSvIy8vDokWLcOXKFZw4cQLHjh0TO54kXblyBenp6cX2kW7duomUiIhKiydPnijbpu7btw+9evVC2bJl4e7ujjFjxoicjj5XLMpooKIv7i5evIhOnTop2w8BgK6uLqytrVnFVROutfD8/f2Vw9RevHiBQYMGwcDAAADeOteHPs26devg7e1dbIBdbm4uoqKi4OvrK1Iy6Thz5oyyLV+9evWwcuVKDBkyRDkbYvjw4WjSpInIKaUjOzsbY8eORXR0NB48eFDsPluzqJevry+eP3+OevXqsc1TCSosLIStrS1bxYmAhYKSxT1bGBcuXADwei9JTEyErq6u8p6uri7q1av31icN6OO1aNECCoUCs2fPhqOjIw4cOIAGDRrg5MmTcHR0FDuepNy4cQM9e/ZEYmIiZDIZijrwFx3u4z5CRB9iYWGBkydPokKFCti3bx+ioqIAAI8ePeKTpCQazpTRYGvXroW3tzc3CAFwrYUREBDwr14XERFRwkk+H+wtXvLkcjnu37+vXON/9hUHgMzMTFSrVo1rrSZDhw7FkSNHMH36dPTr1w9Lly7FnTt3sGLFCsyZMwfffPON2BElhfMghFO7dm2Eh4eziCsAFgqEwz1bWAEBAVi0aBELiyXs1atX+PbbbzFp0iRUr15d7DiS5+HhAS0tLaxevRrVq1fH6dOn8eDBA4waNQrz5s1Dy5YtxY5IRBpu2bJlCA4ORrly5WBlZYXz589DLpfjp59+wtatW3HkyBGxI9JniEWZUiIrKwsFBQUq1/hmu2RwrUlK5HI5MjMzYWpqqnJdoVCgbdu2POWuBm+usaGhIS5duqT8kM6ijHpZWlpi3bp1aNOmDcqXL4/z58/D1tYW69evx8aNG7Fnzx6xIxJ9lF27diE0NJSt4gTAQoFwuGeL5/bt2wAAc3NzkZNIk5GRES5evMiijAAqVaqkfCrdyMgIp0+fhp2dHQ4fPoxRo0YpnxQjInqfs2fPIiMjA66ursouObt374axsTGaN28ucjr6HLF9mQZLS0vDsGHDcPToUbx48UJ5vWiAI7/gUx+uNUkN5yUJi235hPPw4UPlU0jly5dXFhZbtGiBwYMHixlN8tjmqWSxVZxwdu3apSwUBAQEoGXLlrC1tYWVlRU2bNjAoowacc8WVkFBAWbMmIH58+cjKysLwOvDIqNGjcKECROUrVXp0/Xo0QPbt2/nsHkB5Ofnw9DQEMDrAs3du3dhZ2cHKysrXLt2TeR0RFRaODs7w9nZWeWau7u7SGmIWJTRaD4+PigsLMSaNWtQuXJlDkQvQVxrkhrOSxLOm+2bfHx8ir2Gs3vUx8bGBmlpabC0tEStWrUQHR2Nxo0bY9euXTA2NhY7nuSwzZNwFi5cKHaEzwYLBcLhni2sCRMmIDw8HHPmzFGe+j1+/DimTp2KFy9eYObMmSInlI4aNWrghx9+QHx8PBo2bKg8jFNkxIgRIiWTnjp16kChUKB69epwcXFBaGgodHV1sXLlSuVeTkT0ppEjR2L69OkwMDDAyJEj3/vaBQsWCJSK6P+wfZkGK1euHM6dOwc7Ozuxo0ge15qkivOSSGrCwsKgpaWFESNGIDY2Fh4eHigsLMSrV6+wYMECBAcHix1RUtjmiaSobt26+Omnn9C6dWt06NAB9evXx7x587B48WKEhoYq2z7Rp+OeLaxq1aph+fLl6Natm8r1HTt2YMiQIbhz545IyaTnfW3LZDIZbty4IWAaadu/fz+ys7Ph6emJ5ORkeHh44Pr166hYsSKioqLQvn17sSMSkQZq27Yttm3bBmNjY7Rt2/a9r+VMGRIDizIarG3btpgwYQI6dOggdhTJ41rT54DzkkiKbt26hXPnzsHW1hZ169YVO47kcB6EONgqrmSxUCAe7tklS09PD5cuXULNmjVVrl+7dg3169dHTk6OSMmI1Ovhw4cwMTFhhwsiIiq1WJTRYKmpqRg0aBB8fHxQp04d6OjoqNznBxn14VqTVHFeEklFTk4ODh06hK5duwIAxo8frzKvR1tbGz/88AOfClOzcuXK4cqVK7C0tIS5uTm2bt2Kxo0bIy0tDY6OjsqZBfTp2CpOPCwUqB/3bHG4uLjAxcUFixcvVrk+fPhwnD59GqdOnRIpGdF/FxgY+K9et2bNmhJOQkSl1b/ZR2QyGcLDwwVIQ6SKM2U02F9//YXU1FQEBAQor8lkMn6ZWgK41iRVnJdEUrF27Vrs3r1b+QXfkiVLULt2beUw9KSkJFStWpUDd9WM8yCEExISgiNHjuDnn39+a6s4+nQfKhQkJCSwUKAm3LPFERoaCnd3d8TGxqJp06YAgJMnTyIjI4NPNqoB5xMIKzIyElZWVnBycgLPEhPRx+A+QpqMT8poMAcHB9jb2yMkJOStX6ZaWVmJlEx6uNYkVZyXRFLRsmVLhISEwMPDAwBgaGgIhUKhHPD6yy+/YOnSpTh58qSYMSWHbZ6Ew1ZxJW/58uXYvXs3du3aBeD1PvJmoSAkJISFAjXgni2eu3fvYunSpUhKSgIA2NvbY+DAgZgxYwZWrlwpcrrSrW3btpg3bx6cnJzeO8dEJpPh8OHDAiaTpqFDh2Ljxo2wsrJCQEAAfHx8UKFCBbFjEVEpwn2ENBmLMhrMwMAACoUCtra2YkeRPK41SRXnJZFUVK1aFSdPnoS1tTUAwNTUFGfOnFH+fv36dTRq1AhPnjwRL+RngG2eSg5bxZU8FgqEwz1bsygUCjRo0IBP/6uBlpYW7t27BzMzMwCAt7c3Fi9ejMqVK4ucTJpevnyJrVu3Ys2aNThx4gTc3d0RFBSEjh07sgMAEf0r3EdIU7F9mQZr164dCwUC4VqTVK1evRqDBg3CnTt3OC+JSrXHjx+rtBn666+/VO4XFBSo3KdPwzZPwmOruJKXkpICR0dH5e96enqQy+XK3xs3boyhQ4eKEU1yuGeTVL15pnXv3r3Izs4WKY30lSlTBn369EGfPn1w69YtREZGYsiQIcjLy8Mff/yBcuXKiR2RiDQc9xHSVCzKaDAPDw/873//Q2JiIhwdHYt9mdqtWzeRkkkP15qkivOSSCrMzc1x+fLld7biu3TpEszNzQVOJV2cByG8gIAAKBQKtG7dGuPGjYOHhweWLFmibBVHn46FAuFwz6bPBRuPCEculys/x/AzDBF9DO4jpEnYvkyD/fPk3pv4Zap6ca1JqjgviaQiODgYsbGxOHfuXLGnM3JycuDs7IwOHTpg0aJFIiWUFrZ5Eh9bxalfjRo1MGfOHPTq1eut96Ojo/H9998jJSVF4GTSwz1bs7B9mfpoaWnh/v37MDU1BfD678dLly6hevXqIieTpn+2HTp+/Di6du2KgIAAuLm5vfczPBFREe4jpKlYlCEikjDOSyKpyMzMRP369aGrq4thw4ahZs2aAIBr165hyZIlyMvLw4ULF9jTXU04D0I4H2oVp62tzVZxasJCgXC4ZwvL09PzvfcfP36MY8eOsSijBnK5HJ07d0aZMmUAALt27UK7du1gYGCg8rqtW7eKEU9ShgwZgqioKFhYWCAwMBDffPMNKlWqJHYsIipFuI+QJmNRppR5/Pgx+4oLhGtNUuDh4QF/f/93ngomKk3S0tIwePBgHDx4UNkuRCaTwdXVFcuWLVM+xUGfTl9fHxcvXnxn66GkpCTUr18fL168EDiZ9Cxfvhy7d+/Grl27ALw+df1mq7iQkBC2ilMDFgqExT1bOP9sU/s+ERERJZxE+rjWwpHL5bC0tISTk9N7h3GzAEZE78J9hDQZZ8posLlz58La2hre3t4AgN69e2PLli2oWrUq9uzZg3r16omcUDq41iRVnJdEUlK9enXs27cPDx8+VLYXsrW1RYUKFUROJj2cByGcDRs2ICQkROXar7/+WqxVHIsyn65y5co4ceIEBg8ejHHjxr21UMCCjPpwzxYOCwDC4VoLx9fX971fohIRfQj3EdJkfFJGg1WvXh0bNmxAs2bNcPDgQXh5eWHTpk2Ijo5Geno6Dhw4IHZEyeBak1RxXhIRfQy2eRIOW8WJg4UCIiIiIiISC4syGkxfXx/Xr1+HhYUFgoOD8eLFC6xYsQLXr1+Hi4sLHj16JHZEyeBaExER/R+2eRIOW8URERERERF9Xt59hJpEZ2JigoyMDADAvn370KFDBwBAYWEhT7erGdeaPiePHz8WOwIRabiiNk/29vYYN24cevbsiZ49e2L8+PFwcHDA8ePHWZBRk6JWce/CVnFERERERETSwqKMBvP09ETfvn3h6uqKBw8eoHPnzgCACxcuwNbWVuR00sK1JqmaO3cuNm3apPy9d+/eqFChAr744gsoFAoRkxGRpiuaB/HXX38hISEBCQkJ+Ouvv7Bv3z4O6FajLl26YPLkyW99EiYnJwfTpk2Du7u7CMmIiIiIiIioJLB9mQZ79eoVFi1ahIyMDPj7+8PJyQkAEBYWBkNDQ/Tv31/khNLBtSap4rwkIiLNxlZxREREREREnxcWZTTYgwcPULFiRQBARkYGVq1ahZycHHh4eKBVq1Yip5MWrjVJFeclERFpvrS0NAwePBgHDx5E0VtzmUwGV1dXLFu2jE8mERERERERSQiLMhooMTERHh4eyMjIQI0aNRAVFQU3NzdkZ2dDLpcjOzsbMTEx6NGjh9hRSz2uNUldtWrVEBMTg2bNmsHOzg4zZsxA7969ce3aNTRq1AhPnz4VOyIREf1/Dx8+REpKCgDA1tYWFSpUEDkRERERERERqRtnymigkJAQODo6Ii4uDm3atEHXrl3h7u6OJ0+e4NGjR/j2228xZ84csWNKAteapI7zkoiISo8KFSqgcePGaNy4MQsyREREREREEsUnZTRQpUqVcPjwYdStWxdZWVkoX748zpw5g4YNGwIAkpKS0KRJEzx+/FjcoBLAtSap47wkIiIiIiIiIiIizaEtdgAq7uHDh6hSpQoAoFy5cjAwMICJiYnyvomJCZ49eyZWPEnhWpPUPX36FKNHjwbwel7S5MmTOS+JiIiIiIiIiIhIJGxfpqFkMtl7fyf14VqTFCUmJsLa2hpmZmaoVasWLl68iEaNGiEsLAwrV65Eu3btsH37drFjEhERERERERERfVb4pIyG8vf3R5kyZQAAL168wKBBg2BgYAAAePnypZjRJIdrTVJUNC9pw4YNWL9+vXJe0qpVqwAAw4cPx5w5c9CjRw9xgxIREREREREREX1GOFNGAwUEBPyr10VERJRwEunjWpNUcV4SERERERERERGR5mFRhohIguRyOe7fvw8zMzMAgKGhIRQKBWxsbAAAmZmZqFatGvLz88WMSURERERERERE9FnhTBkiIonivCQiIiIiIiIiIiLNwpkyREQSxXlJREREREREREREmoXty4iIJIjzkoiIiIiIiIiIiDQPizJEREREREREREREREQC4EwZIiIiIiIiIiIiIiIiAbAoQ0REREREREREREREJAAWZYiIiIiIiIiIiIiIiATAogwREREREREREREREZEAWJQhIiIiIiKN4e/vD5lMVuwnJSXlk//dkZGRMDY2/vSQREREREREH0lb7ABERERERET/5ObmhoiICJVrpqamIqV5u1evXkFHR0fsGEREREREVMrwSRkiIiIiItIoZcqUQZUqVVR+tLS0sGPHDjRo0AB6enqwsbHBtGnTkJeXp/znFixYAEdHRxgYGMDCwgJDhgxBVlYWAODo0aMICAjAkydPlE/fTJ06FQAgk8mwfft2lQzGxsaIjIwEANy8eRMymQybNm1C69atoaenhw0bNgAAVq9eDXt7e+jp6aFWrVpYtmyZ8t+Rm5uLYcOGoWrVqtDT04OVlRVmz55dcgtHREREREQaj0/KEBERERGRxvv999/h6+uLxYsXo2XLlkhNTcXAgQMBAFOmTAEAyOVyLF68GNWrV8eNGzcwZMgQhISEYNmyZWjWrBkWLlyIyZMn49q1awCAcuXK/acM48aNw/z58+Hk5KQszEyePBlLliyBk5MTLly4gAEDBsDAwAB+fn5YvHgxdu7ciejoaFhaWiIjIwMZGRnqXRgiIiIiIipVWJQhIiIiIiKN8ttvv6kUTDp37oxHjx5h3Lhx8PPzAwDY2Nhg+vTpCAkJURZlvvvuO+U/Y21tjRkzZmDQoEFYtmwZdHV1YWRkBJlMhipVqnxUru+++w6enp7K36dMmYL58+crr1WvXh1XrlzBihUr4Ofnh/T0dNSoUQMtWrSATCaDlZXVR/3vEhERERGRdLAoQ0REREREGqVt27b4+eeflb8bGBigbt26iI+Px8yZM5XX8/Pz8eLFCzx//hxly5ZFbGwsZs+ejaSkJDx9+hR5eXkq9z+Vs7Oz8r9nZ2cjNTUVQUFBGDBggPJ6Xl4ejIyMAAD+/v5wdXWFnZ0d3Nzc0LVrV3Ts2PGTcxARERERUenFogwREREREWkUAwMD2NraqlzLysrCtGnTVJ5UKaKnp4ebN2+ia9euGDx4MGbOnIkKFSrg+PHjCAoKQm5u7nuLMjKZDIWFhSrXXr169dZc/8wDAKtWrYKLi4vK67S0tAAADRo0QFpaGvbu3YvY2Fh4eXmhQ4cOiImJ+cAKEBERERGRVLEoQ0REREREGq9Bgwa4du1asWJNkXPnzqGgoADz58+HXC4HAERHR6u8RldXF/n5+cX+WVNTU9y7d0/5e3JyMp4/f/7ePJUrV0a1atVw48YNfPPNN+98Xfny5eHt7Q1vb2989dVXcHNzw8OHD1GhQoX3/vuJiIiIiEiaWJQhIiIiIiKNN3nyZHTt2hWWlpb46quvIJfLoVAocPnyZcyYMQO2trZ49eoVfvrpJ3h4eCA+Ph7Lly9X+XdYW1sjKysLhw4dQr169VC2bFmULVsW7dq1w5IlS9C0aVPk5+dj7Nix0NHR+WCmadOmYcSIETAyMoKbmxtevnyJs2fP4tGjRxg5ciQWLFiAqlWrwsnJCXK5HJs3b0aVKlVgbGxcQqtERERERESaTi52ACIiIiIiog/p1KkTfvvtNxw4cACNGjVCkyZNEBYWBisrKwBAvXr1sGDBAsydOxd16tTBhg0bMHv2bJV/R7NmzTBo0CB4e3vD1NQUoaGhAID58+fDwsICLVu2RN++fTF69Oh/NYOmf//+WL16NSIiIuDo6IjWrVsjMjIS1atXBwAYGhoiNDQUzs7OaNSoEW7evIk9e/Yon+QhIiIiIqLPj6zwzebJREREREREREREREREpHY8okVERERERERERERERCQAFmWIiIiIiIiIiIiIiIgEwKIMERERERERERERERGRAFiUISIiIiIiIiIiIiIiEgCLMkRERERERERERERERAJgUYaIiIiIiIiIiIiIiEgALMoQEREREREREREREREJgEUZIiIiIiIiIiIiIiIiAbAoQ0REREREREREREREJAAWZYiIiIiIiIiIiIiIiATAogwREREREREREREREZEAWJQhIiIiIiIiIiIiIiISwP8DVRYBQXzZ/MYAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8))\n",
    "plt.title('Missing values count', fontdict=dict(fontsize=30))\n",
    "missing_by_col_first.plot(kind = 'bar', color='xkcd:hunter green')\n",
    "plt.grid(True, which='both', linestyle='--')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('CountMissing')\n",
    "\n",
    "for index,data in enumerate(missing_by_col_first):\n",
    "    plt.text(x=index - 0.2, y = data + 0.001, s=f\"{data * 100:.{2}f}%\" , fontdict=dict(fontsize=15))\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(20, 8))\n",
    "missing_by_col_second.plot(kind = 'bar', color='xkcd:hunter green')\n",
    "plt.grid(True, which='both', linestyle='--')\n",
    "for index,data in enumerate(missing_by_col_second):\n",
    "    plt.text(x=index - 0.2, y = data + 0.01, s=f\"{data * 100:.{2}f}%\" , fontdict=dict(fontsize=15))\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('CountMissing')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1f9db932",
   "metadata": {},
   "source": [
    "## Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c0eb94cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8ea542c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df_main.columns:\n",
    "    if df_main[col].dtype == 'O':\n",
    "        series = df_main[col]\n",
    "        label_encoder = LabelEncoder()\n",
    "        df_main[col] = pd.Series(label_encoder.fit_transform(series[series.notnull()]), index=series[series.notnull()].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a8f70c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>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>...</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>60</td>\n",
       "      <td>3.0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</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>8.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</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>8.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>60</td>\n",
       "      <td>3.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>...</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>8.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>70</td>\n",
       "      <td>3.0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</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>8.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>60</td>\n",
       "      <td>3.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2</td>\n",
       "      <td>...</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>8.0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 79 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   MSSubClass  MSZoning  LotFrontage  LotArea  Street  Alley  LotShape   \n",
       "0          60       3.0         65.0     8450       1    NaN         3  \\\n",
       "1          20       3.0         80.0     9600       1    NaN         3   \n",
       "2          60       3.0         68.0    11250       1    NaN         0   \n",
       "3          70       3.0         60.0     9550       1    NaN         0   \n",
       "4          60       3.0         84.0    14260       1    NaN         0   \n",
       "\n",
       "   LandContour  Utilities  LotConfig  ...  ScreenPorch  PoolArea  PoolQC   \n",
       "0            3        0.0          4  ...            0         0     NaN  \\\n",
       "1            3        0.0          2  ...            0         0     NaN   \n",
       "2            3        0.0          4  ...            0         0     NaN   \n",
       "3            3        0.0          0  ...            0         0     NaN   \n",
       "4            3        0.0          2  ...            0         0     NaN   \n",
       "\n",
       "   Fence  MiscFeature  MiscVal  MoSold  YrSold  SaleType  SaleCondition  \n",
       "0    NaN          NaN        0       2    2008       8.0              4  \n",
       "1    NaN          NaN        0       5    2007       8.0              4  \n",
       "2    NaN          NaN        0       9    2008       8.0              4  \n",
       "3    NaN          NaN        0       2    2006       8.0              0  \n",
       "4    NaN          NaN        0      12    2008       8.0              4  \n",
       "\n",
       "[5 rows x 79 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93860d5d",
   "metadata": {},
   "source": [
    "## Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "18bbd186",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.impute import KNNImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a24187e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "imputer = KNNImputer(n_neighbors=10, weights='distance')\n",
    "imputed_wdis = imputer.fit_transform(df_main)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c186be20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MSSubClass</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>57.067192</td>\n",
       "      <td>42.446750</td>\n",
       "      <td>20.0</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>190.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MSZoning</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>3.028174</td>\n",
       "      <td>0.657264</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotFrontage</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>70.084261</td>\n",
       "      <td>22.464599</td>\n",
       "      <td>21.0</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotArea</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>10173.348989</td>\n",
       "      <td>7887.164614</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>7488.000000</td>\n",
       "      <td>9457.000000</td>\n",
       "      <td>11577.000000</td>\n",
       "      <td>215245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Street</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.995886</td>\n",
       "      <td>0.064018</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alley</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.312655</td>\n",
       "      <td>0.252868</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.168039</td>\n",
       "      <td>0.278083</td>\n",
       "      <td>0.396367</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotShape</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.946863</td>\n",
       "      <td>1.409935</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandContour</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.776826</td>\n",
       "      <td>0.704608</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utilities</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.000343</td>\n",
       "      <td>0.018515</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LotConfig</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>3.055194</td>\n",
       "      <td>1.604832</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LandSlope</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.053822</td>\n",
       "      <td>0.248831</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Neighborhood</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>12.436064</td>\n",
       "      <td>5.957075</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition1</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.040453</td>\n",
       "      <td>0.874346</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Condition2</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.002057</td>\n",
       "      <td>0.209503</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BldgType</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.503942</td>\n",
       "      <td>1.205156</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HouseStyle</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>3.025369</td>\n",
       "      <td>1.912894</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallQual</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>6.088447</td>\n",
       "      <td>1.410229</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OverallCond</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>5.564964</td>\n",
       "      <td>1.113414</td>\n",
       "      <td>1.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearBuilt</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1971.290024</td>\n",
       "      <td>30.289349</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1953.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1984.250600</td>\n",
       "      <td>20.894780</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>1965.000000</td>\n",
       "      <td>1993.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofStyle</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.396640</td>\n",
       "      <td>0.821122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RoofMatl</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.063079</td>\n",
       "      <td>0.539393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior1st</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>9.623051</td>\n",
       "      <td>3.199410</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>14.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exterior2nd</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>10.334950</td>\n",
       "      <td>3.551200</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrType</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.148650</td>\n",
       "      <td>0.313238</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.197289</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MasVnrArea</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>102.476450</td>\n",
       "      <td>178.890265</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>164.000000</td>\n",
       "      <td>1600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterQual</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.531025</td>\n",
       "      <td>0.702348</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ExterCond</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>3.708605</td>\n",
       "      <td>0.773869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Foundation</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.392869</td>\n",
       "      <td>0.727137</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtQual</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.242789</td>\n",
       "      <td>0.878122</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtCond</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.797680</td>\n",
       "      <td>0.673981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtExposure</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.281255</td>\n",
       "      <td>1.118733</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.773087</td>\n",
       "      <td>1.791975</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>441.627516</td>\n",
       "      <td>455.573226</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>369.000000</td>\n",
       "      <td>733.000000</td>\n",
       "      <td>5644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>4.681713</td>\n",
       "      <td>0.990061</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>49.611071</td>\n",
       "      <td>169.229868</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>560.664746</td>\n",
       "      <td>439.560224</td>\n",
       "      <td>0.0</td>\n",
       "      <td>220.000000</td>\n",
       "      <td>467.000000</td>\n",
       "      <td>804.000000</td>\n",
       "      <td>2336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1051.903333</td>\n",
       "      <td>440.770633</td>\n",
       "      <td>0.0</td>\n",
       "      <td>793.000000</td>\n",
       "      <td>990.000000</td>\n",
       "      <td>1302.000000</td>\n",
       "      <td>6110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.025369</td>\n",
       "      <td>0.245762</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HeatingQC</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.534796</td>\n",
       "      <td>1.742682</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CentralAir</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.932808</td>\n",
       "      <td>0.250398</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Electrical</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>3.684880</td>\n",
       "      <td>1.047792</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1stFlrSF</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1159.865958</td>\n",
       "      <td>392.302052</td>\n",
       "      <td>334.0</td>\n",
       "      <td>876.000000</td>\n",
       "      <td>1083.000000</td>\n",
       "      <td>1388.000000</td>\n",
       "      <td>5095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>336.247515</td>\n",
       "      <td>428.750767</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>704.000000</td>\n",
       "      <td>2065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>4.670209</td>\n",
       "      <td>46.391690</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1064.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GrLivArea</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1500.783682</td>\n",
       "      <td>506.183736</td>\n",
       "      <td>334.0</td>\n",
       "      <td>1126.000000</td>\n",
       "      <td>1444.000000</td>\n",
       "      <td>1744.000000</td>\n",
       "      <td>5642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.430194</td>\n",
       "      <td>0.524669</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.061364</td>\n",
       "      <td>0.245687</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FullBath</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.567707</td>\n",
       "      <td>0.553043</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HalfBath</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.380185</td>\n",
       "      <td>0.502864</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.860473</td>\n",
       "      <td>0.822817</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.044566</td>\n",
       "      <td>0.214532</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KitchenQual</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.347177</td>\n",
       "      <td>0.834550</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>6.452177</td>\n",
       "      <td>1.569228</td>\n",
       "      <td>2.0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>15.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Functional</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>5.761574</td>\n",
       "      <td>0.929493</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>6.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fireplaces</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>0.597532</td>\n",
       "      <td>0.646162</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FireplaceQu</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.703311</td>\n",
       "      <td>0.872979</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.577198</td>\n",
       "      <td>3.409296</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageType</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.327630</td>\n",
       "      <td>1.767203</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1977.231797</td>\n",
       "      <td>25.389082</td>\n",
       "      <td>1895.0</td>\n",
       "      <td>1959.641002</td>\n",
       "      <td>1978.000000</td>\n",
       "      <td>2001.000000</td>\n",
       "      <td>2207.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageFinish</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.206773</td>\n",
       "      <td>0.807004</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.921584</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCars</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.766541</td>\n",
       "      <td>0.761743</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageArea</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>472.922450</td>\n",
       "      <td>215.427147</td>\n",
       "      <td>0.0</td>\n",
       "      <td>320.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>1488.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageQual</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>3.829777</td>\n",
       "      <td>0.649709</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GarageCond</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>3.889900</td>\n",
       "      <td>0.516895</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PavedDrive</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.830648</td>\n",
       "      <td>0.537465</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>93.774083</td>\n",
       "      <td>126.546161</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>1424.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>47.448749</td>\n",
       "      <td>67.562874</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>23.114158</td>\n",
       "      <td>64.263424</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3SsnPorch</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.604045</td>\n",
       "      <td>25.196714</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>508.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ScreenPorch</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>16.073363</td>\n",
       "      <td>56.202054</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolArea</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2.253342</td>\n",
       "      <td>35.676126</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PoolQC</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.108389</td>\n",
       "      <td>0.115082</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.036675</td>\n",
       "      <td>1.113231</td>\n",
       "      <td>1.181251</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fence</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.355522</td>\n",
       "      <td>0.508361</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.007586</td>\n",
       "      <td>1.393241</td>\n",
       "      <td>1.732886</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscFeature</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>1.979319</td>\n",
       "      <td>0.103599</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MiscVal</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>50.860816</td>\n",
       "      <td>567.595198</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>17000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MoSold</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>6.213233</td>\n",
       "      <td>2.715561</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>YrSold</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>2007.793966</td>\n",
       "      <td>1.314577</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleType</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>7.490683</td>\n",
       "      <td>1.593968</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SaleCondition</th>\n",
       "      <td>2917.0</td>\n",
       "      <td>3.778882</td>\n",
       "      <td>1.078595</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                count          mean          std     min          25%   \n",
       "MSSubClass     2917.0     57.067192    42.446750    20.0    20.000000  \\\n",
       "MSZoning       2917.0      3.028174     0.657264     0.0     3.000000   \n",
       "LotFrontage    2917.0     70.084261    22.464599    21.0    60.000000   \n",
       "LotArea        2917.0  10173.348989  7887.164614  1300.0  7488.000000   \n",
       "Street         2917.0      0.995886     0.064018     0.0     1.000000   \n",
       "Alley          2917.0      0.312655     0.252868     0.0     0.168039   \n",
       "LotShape       2917.0      1.946863     1.409935     0.0     0.000000   \n",
       "LandContour    2917.0      2.776826     0.704608     0.0     3.000000   \n",
       "Utilities      2917.0      0.000343     0.018515     0.0     0.000000   \n",
       "LotConfig      2917.0      3.055194     1.604832     0.0     2.000000   \n",
       "LandSlope      2917.0      0.053822     0.248831     0.0     0.000000   \n",
       "Neighborhood   2917.0     12.436064     5.957075     0.0     7.000000   \n",
       "Condition1     2917.0      2.040453     0.874346     0.0     2.000000   \n",
       "Condition2     2917.0      2.002057     0.209503     0.0     2.000000   \n",
       "BldgType       2917.0      0.503942     1.205156     0.0     0.000000   \n",
       "HouseStyle     2917.0      3.025369     1.912894     0.0     2.000000   \n",
       "OverallQual    2917.0      6.088447     1.410229     1.0     5.000000   \n",
       "OverallCond    2917.0      5.564964     1.113414     1.0     5.000000   \n",
       "YearBuilt      2917.0   1971.290024    30.289349  1872.0  1953.000000   \n",
       "YearRemodAdd   2917.0   1984.250600    20.894780  1950.0  1965.000000   \n",
       "RoofStyle      2917.0      1.396640     0.821122     0.0     1.000000   \n",
       "RoofMatl       2917.0      1.063079     0.539393     0.0     1.000000   \n",
       "Exterior1st    2917.0      9.623051     3.199410     0.0     8.000000   \n",
       "Exterior2nd    2917.0     10.334950     3.551200     0.0     8.000000   \n",
       "MasVnrType     2917.0      1.148650     0.313238     0.0     1.000000   \n",
       "MasVnrArea     2917.0    102.476450   178.890265     0.0     0.000000   \n",
       "ExterQual      2917.0      2.531025     0.702348     0.0     2.000000   \n",
       "ExterCond      2917.0      3.708605     0.773869     0.0     4.000000   \n",
       "Foundation     2917.0      1.392869     0.727137     0.0     1.000000   \n",
       "BsmtQual       2917.0      2.242789     0.878122     0.0     2.000000   \n",
       "BsmtCond       2917.0      2.797680     0.673981     0.0     3.000000   \n",
       "BsmtExposure   2917.0      2.281255     1.118733     0.0     2.000000   \n",
       "BsmtFinType1   2917.0      2.773087     1.791975     0.0     2.000000   \n",
       "BsmtFinSF1     2917.0    441.627516   455.573226     0.0     0.000000   \n",
       "BsmtFinType2   2917.0      4.681713     0.990061     0.0     5.000000   \n",
       "BsmtFinSF2     2917.0     49.611071   169.229868     0.0     0.000000   \n",
       "BsmtUnfSF      2917.0    560.664746   439.560224     0.0   220.000000   \n",
       "TotalBsmtSF    2917.0   1051.903333   440.770633     0.0   793.000000   \n",
       "Heating        2917.0      1.025369     0.245762     0.0     1.000000   \n",
       "HeatingQC      2917.0      1.534796     1.742682     0.0     0.000000   \n",
       "CentralAir     2917.0      0.932808     0.250398     0.0     1.000000   \n",
       "Electrical     2917.0      3.684880     1.047792     0.0     4.000000   \n",
       "1stFlrSF       2917.0   1159.865958   392.302052   334.0   876.000000   \n",
       "2ndFlrSF       2917.0    336.247515   428.750767     0.0     0.000000   \n",
       "LowQualFinSF   2917.0      4.670209    46.391690     0.0     0.000000   \n",
       "GrLivArea      2917.0   1500.783682   506.183736   334.0  1126.000000   \n",
       "BsmtFullBath   2917.0      0.430194     0.524669     0.0     0.000000   \n",
       "BsmtHalfBath   2917.0      0.061364     0.245687     0.0     0.000000   \n",
       "FullBath       2917.0      1.567707     0.553043     0.0     1.000000   \n",
       "HalfBath       2917.0      0.380185     0.502864     0.0     0.000000   \n",
       "BedroomAbvGr   2917.0      2.860473     0.822817     0.0     2.000000   \n",
       "KitchenAbvGr   2917.0      1.044566     0.214532     0.0     1.000000   \n",
       "KitchenQual    2917.0      2.347177     0.834550     0.0     2.000000   \n",
       "TotRmsAbvGrd   2917.0      6.452177     1.569228     2.0     5.000000   \n",
       "Functional     2917.0      5.761574     0.929493     0.0     6.000000   \n",
       "Fireplaces     2917.0      0.597532     0.646162     0.0     0.000000   \n",
       "FireplaceQu    2917.0      2.703311     0.872979     0.0     2.000000   \n",
       "GarageType     2917.0      2.327630     1.767203     0.0     1.000000   \n",
       "GarageYrBlt    2917.0   1977.231797    25.389082  1895.0  1959.641002   \n",
       "GarageFinish   2917.0      1.206773     0.807004     0.0     0.921584   \n",
       "GarageCars     2917.0      1.766541     0.761743     0.0     1.000000   \n",
       "GarageArea     2917.0    472.922450   215.427147     0.0   320.000000   \n",
       "GarageQual     2917.0      3.829777     0.649709     0.0     4.000000   \n",
       "GarageCond     2917.0      3.889900     0.516895     0.0     4.000000   \n",
       "PavedDrive     2917.0      1.830648     0.537465     0.0     2.000000   \n",
       "WoodDeckSF     2917.0     93.774083   126.546161     0.0     0.000000   \n",
       "OpenPorchSF    2917.0     47.448749    67.562874     0.0     0.000000   \n",
       "EnclosedPorch  2917.0     23.114158    64.263424     0.0     0.000000   \n",
       "3SsnPorch      2917.0      2.604045    25.196714     0.0     0.000000   \n",
       "ScreenPorch    2917.0     16.073363    56.202054     0.0     0.000000   \n",
       "PoolArea       2917.0      2.253342    35.676126     0.0     0.000000   \n",
       "PoolQC         2917.0      1.108389     0.115082     0.0     1.036675   \n",
       "Fence          2917.0      1.355522     0.508361     0.0     1.007586   \n",
       "MiscFeature    2917.0      1.979319     0.103599     0.0     2.000000   \n",
       "MiscVal        2917.0     50.860816   567.595198     0.0     0.000000   \n",
       "MoSold         2917.0      6.213233     2.715561     1.0     4.000000   \n",
       "YrSold         2917.0   2007.793966     1.314577  2006.0  2007.000000   \n",
       "SaleType       2917.0      7.490683     1.593968     0.0     8.000000   \n",
       "SaleCondition  2917.0      3.778882     1.078595     0.0     4.000000   \n",
       "\n",
       "                       50%           75%       max  \n",
       "MSSubClass       50.000000     70.000000     190.0  \n",
       "MSZoning          3.000000      3.000000       4.0  \n",
       "LotFrontage      70.000000     80.000000     313.0  \n",
       "LotArea        9457.000000  11577.000000  215245.0  \n",
       "Street            1.000000      1.000000       1.0  \n",
       "Alley             0.278083      0.396367       1.0  \n",
       "LotShape          3.000000      3.000000       3.0  \n",
       "LandContour       3.000000      3.000000       3.0  \n",
       "Utilities         0.000000      0.000000       1.0  \n",
       "LotConfig         4.000000      4.000000       4.0  \n",
       "LandSlope         0.000000      0.000000       2.0  \n",
       "Neighborhood     12.000000     17.000000      24.0  \n",
       "Condition1        2.000000      2.000000       8.0  \n",
       "Condition2        2.000000      2.000000       7.0  \n",
       "BldgType          0.000000      0.000000       4.0  \n",
       "HouseStyle        2.000000      5.000000       7.0  \n",
       "OverallQual       6.000000      7.000000      10.0  \n",
       "OverallCond       5.000000      6.000000       9.0  \n",
       "YearBuilt      1973.000000   2001.000000    2010.0  \n",
       "YearRemodAdd   1993.000000   2004.000000    2010.0  \n",
       "RoofStyle         1.000000      1.000000       5.0  \n",
       "RoofMatl          1.000000      1.000000       7.0  \n",
       "Exterior1st      12.000000     12.000000      14.0  \n",
       "Exterior2nd      13.000000     13.000000      15.0  \n",
       "MasVnrType        1.000000      1.197289       2.0  \n",
       "MasVnrArea        0.000000    164.000000    1600.0  \n",
       "ExterQual         3.000000      3.000000       3.0  \n",
       "ExterCond         4.000000      4.000000       4.0  \n",
       "Foundation        1.000000      2.000000       5.0  \n",
       "BsmtQual          2.000000      3.000000       3.0  \n",
       "BsmtCond          3.000000      3.000000       3.0  \n",
       "BsmtExposure      3.000000      3.000000       3.0  \n",
       "BsmtFinType1      2.000000      5.000000       5.0  \n",
       "BsmtFinSF1      369.000000    733.000000    5644.0  \n",
       "BsmtFinType2      5.000000      5.000000       5.0  \n",
       "BsmtFinSF2        0.000000      0.000000    1526.0  \n",
       "BsmtUnfSF       467.000000    804.000000    2336.0  \n",
       "TotalBsmtSF     990.000000   1302.000000    6110.0  \n",
       "Heating           1.000000      1.000000       5.0  \n",
       "HeatingQC         0.000000      4.000000       4.0  \n",
       "CentralAir        1.000000      1.000000       1.0  \n",
       "Electrical        4.000000      4.000000       4.0  \n",
       "1stFlrSF       1083.000000   1388.000000    5095.0  \n",
       "2ndFlrSF          0.000000    704.000000    2065.0  \n",
       "LowQualFinSF      0.000000      0.000000    1064.0  \n",
       "GrLivArea      1444.000000   1744.000000    5642.0  \n",
       "BsmtFullBath      0.000000      1.000000       3.0  \n",
       "BsmtHalfBath      0.000000      0.000000       2.0  \n",
       "FullBath          2.000000      2.000000       4.0  \n",
       "HalfBath          0.000000      1.000000       2.0  \n",
       "BedroomAbvGr      3.000000      3.000000       8.0  \n",
       "KitchenAbvGr      1.000000      1.000000       3.0  \n",
       "KitchenQual       3.000000      3.000000       3.0  \n",
       "TotRmsAbvGrd      6.000000      7.000000      15.0  \n",
       "Functional        6.000000      6.000000       6.0  \n",
       "Fireplaces        1.000000      1.000000       4.0  \n",
       "FireplaceQu       2.577198      3.409296       4.0  \n",
       "GarageType        1.000000      5.000000       5.0  \n",
       "GarageYrBlt    1978.000000   2001.000000    2207.0  \n",
       "GarageFinish      1.000000      2.000000       2.0  \n",
       "GarageCars        2.000000      2.000000       5.0  \n",
       "GarageArea      480.000000    576.000000    1488.0  \n",
       "GarageQual        4.000000      4.000000       4.0  \n",
       "GarageCond        4.000000      4.000000       4.0  \n",
       "PavedDrive        2.000000      2.000000       2.0  \n",
       "WoodDeckSF        0.000000    168.000000    1424.0  \n",
       "OpenPorchSF      26.000000     70.000000     742.0  \n",
       "EnclosedPorch     0.000000      0.000000    1012.0  \n",
       "3SsnPorch         0.000000      0.000000     508.0  \n",
       "ScreenPorch       0.000000      0.000000     576.0  \n",
       "PoolArea          0.000000      0.000000     800.0  \n",
       "PoolQC            1.113231      1.181251       2.0  \n",
       "Fence             1.393241      1.732886       3.0  \n",
       "MiscFeature       2.000000      2.000000       3.0  \n",
       "MiscVal           0.000000      0.000000   17000.0  \n",
       "MoSold            6.000000      8.000000      12.0  \n",
       "YrSold         2008.000000   2009.000000    2010.0  \n",
       "SaleType          8.000000      8.000000       8.0  \n",
       "SaleCondition     4.000000      4.000000       5.0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_main = pd.DataFrame(imputed_wdis, columns = ['MSSubClass', 'MSZoning', 'LotFrontage', 'LotArea', 'Street',\n",
    "       'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig',\n",
    "       'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType',\n",
    "       'HouseStyle', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd',\n",
    "       'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType',\n",
    "       'MasVnrArea', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual',\n",
    "       'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinSF1',\n",
    "       'BsmtFinType2', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', 'Heating',\n",
    "       'HeatingQC', 'CentralAir', 'Electrical', '1stFlrSF', '2ndFlrSF',\n",
    "       'LowQualFinSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath',\n",
    "       'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'KitchenQual',\n",
    "       'TotRmsAbvGrd', 'Functional', 'Fireplaces', 'FireplaceQu', 'GarageType',\n",
    "       'GarageYrBlt', 'GarageFinish', 'GarageCars', 'GarageArea', 'GarageQual',\n",
    "       'GarageCond', 'PavedDrive', 'WoodDeckSF', 'OpenPorchSF',\n",
    "       'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'PoolQC',\n",
    "       'Fence', 'MiscFeature', 'MiscVal', 'MoSold', 'YrSold', 'SaleType',\n",
    "       'SaleCondition'])\n",
    "df_main.describe(include='all').T"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a6244b7",
   "metadata": {},
   "source": [
    "## Visualize Data Distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b31501e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XXRTf/OY3Y999941tttkmLr300hgyZEh8//vf7zbTGWecEbvvvntss802ceKJJ8bdd98dixYtiiFDhsSwYcOiVCp1zfLKexN86lOfiokTJ8bYsWNjzz33jNNPPz2uv/76rsf8zne+E/vuu2987nOfiy222CKOP/74Hn9r7tRTT40TTzwxjjjiiNh0001j7733jq997Wtx8cUXL/Prd9ZZZ0VbW1vXn4033nilvh+wKnzqU5/q820rlUr8/ve/jzlz5sTHP/7xlX7uK6+8MubMmRO///3vV/qxAFg7eInOXEseq185xq6pM7GWfO4ZM2b0+S86LXm/pS3rc1jeffqqnhk7OjpixowZdX1N+/K9WNnP42c/+9kyZ6lUKvGzn/1shR53Ra3O79fSlvf9e+X7tSatyc892+r6XJvpawgAzWxt+++4RrfKzvx56KGHYsKECd3+ts1b3/rWeOGFF+Jvf/tbjzdwX9r//M//xNZbb911eeONN+76Zi55Otdjjz0WnZ2d8da3vrXrugEDBsTOO+8cDz30ULfH3G677br+ecMNN4yIiGeeeWa5s/ziF7+Is846Kx5++OHo6OiIcrkcixYtihdffDGGDh0as2fPjoMOOqjbfXbeeee45ZZbui7ff//9MX369DjjjDO6rqtUKt0eZ2lf/OIXu53O1tHRYQFEuvPOO6/PC6BisRg77LBDjBkzJr7zne+s9ALoyCOPjGKxGDvttFPdC6Abb7yx185YNTo7O+P222+PffbZJwYMWPtPo1nyJaeif78kbq4lvrZe9m31auQG1+RLsNHTksfqiIiddtop7rvvvjWyAFryuUePHh2tra19Wq4sPfOSxowZ0+vnsLz79NWECRP6PGNbW1vX/w/q69e0L9+Llf08DjjggPj2t7/d6yylUikOOOCAFXrcFbU6v19LW97375Xv15q0Jj/3bKvrc22mryEANLO17b/jGt1a88KAG2+8cWy++ea9fmydddZZocdc8hciryyllvcLhyeffDLe+c53xnHHHRdnnHFGjBgxIn7zm9/E0UcfHS+99FKff5n8wgsvxKmnnhrvfve7e3xsWb8MGzRoUAwaNKhPj9/sisVijBs3rt+/sdna4E1velOfb1soFOLTn/50FAqF2H777VfqeY8//viu1y399Kc/Xfd7/gwdOrTrbD5WvUGDBsVOO+0Uw4YNa7xTbb0ayOqzxNd28ODBGlyNGrnBjJfk8Z4/tW6XXzlWR/y/Y+yqmOHVbrPkcxcKhTjllFP69J4/S8/c22Mu/Tks7z591dLSsswZl3bqqad2tdjbPL29509fvhcr+3kUi8X4whe+0Ot7/px44olr/L+lV+f3a2nL+/4t+f1aU9bk555tdX2uzfQ1hLWd38lArv7e4Nr233GNbpV9tbbeeuuYMWNGt/+zNn369Bg+fHjXmy8OHDhwpf9m4WabbRYDBw6M6dOnd13X2dkZv//972Obbbbp8+P0Nsu9994b1Wo1zj777HjLW94SW2yxRTz11FPdbrPlllv2OAth6ctvetObYvbs2bH55pv3+OMHdOW1tLTEmDFjfC3XkLvuuutVb1MoFOLQQw+N17/+9XXdb/311++xVB06dGi8//3v77q80UYbxaGHHtrjvocffni88Y1vfNXnYNXTIORq5AYPOeSQVfYLulKpFKNGjVrubbbbbrs47LDDup6zUCi86puiLmu+pZ9r6cfpy3MNHTq0x+MMHTq023222267bpcPP/zwHvfp7fi5tFKpFB/84Ae7PdbSx+pXjrFL3mbpx11//fV7PM7666/f43Nf+jZLfx5LP/eOO+7Y4zje2+Msfb+l9fY5vNp9+qq3GZf++my33Xbd/rJMb/Mcdthhrzrj6vo89t13315/fvbZZ5+VetwVtTq/X0tb1s9YPX+5aVVak597ttX1uTbT1xDWZo3836LQHzRDg2vbf8c1shX6KWlvb49Zs2Z1+3PsscfGX//61/j4xz8eDz/8cNx8881x8sknx2c+85muH8axY8fGb3/723jyySfj2WefXaG/GbnOOuvEcccdF5///Ofj//7v/+LBBx+MY445Jl588cU4+uij+/w4Y8eOjSeeeCJmzZoVzz77bCxevDg233zz6OzsjO985zvx+OOPxw9+8IP43ve+1+1+H//4x2PKlClxzjnnxF/+8pe4+OKL47bbbuv2i4KvfvWrcfXVV8epp54aDzzwQDz00ENx3XXXxVe+8pW6P196KpfLcccdd0S5XM4epWmst956vV7/ys/9yJEje13QjBw5crmPe8EFF8Qll1zS7bqlL0dEHHbYYd0e65XnO+OMM7r+/dKfD3prGw1CrkZu8JBDDonXvOY1EbH8s4Da2tpi3XXX7Xbd6aef3u3yxRdfHBdeeGG369Zbb72ux21paYnTTz89DjvssK7nHDlyZK/vwThy5Miu48zIkSPjmGOO6fbx448/vsdzXXzxxd2OQX15rksuuaTH41xyySXd7nP66ad3u3zooYf2uE9vx8/evj5Lz9PbsXrp2yz9uBdccEGP21xwwQWv+rn39nn8f+3df5CUBR348c/uHT9O5e5C4YDsSNM0E8k06XKyUBPUaSr9Q0nJymgsLBUqsympqRnLGiubJqdpJv0j7ddkTYahk2A1ohbJVBaMMiaVIqXjHSrq7e7z/eP75b4doN4i8OHZfb1mmJHbvePzwL7vWfmw+2xv+/P4zr7Ozj7vpY5hLJ8zVtvP+N3vfneHmccyz678Weyu49jZ4yfTnvzz2t7OHmOZ9uaxZ9tTx9pOv4ewryrzc1FoBe3S4L72PK6sdulvS1etWhXHHnvsqB9f/OIXY/ny5XHvvffG7Nmz46KLLooLL7xw1MLjE5/4RHR0dMRRRx0VU6ZMiY0bN+7S0F/+8pfj7LPPjoULF8Yb3/jGePDBB2PFihUv+BfUO3P22WfH/PnzY+7cuTFlypS46aabYvbs2XHNNdfEV77ylTj66KPjBz/4wQ5vk3DiiSfGddddF9dcc03Mnj07fv3rX8dll1026u3c5s2bF7fcckvcdttt8aY3vSne/OY3x9e//nXvQ7ybFEURW7Zs2eW3QKF5v/jFL0b9vLe3N3p7e2PhwoXR19cXS5Ys2elbGv7sZz8b9fP3ve99I29vOHfu3Ojr64v+/v6Rbf6sWbN2ek2uiRMnxtKlS0d+3aVLl8bEiROjt7c3zj///KhWq6NeLcSepUHIVeYGt30/7+vri4ULF0Zvb2/sv//+0dvbO/LqhGq1Gp/+9Kfj8ssvH3nlzKxZs+Kkk04aeS41c+bMOPzww6Ovry/mzp0bEf/37fA+9alPxcKFC6Narcb5558fvb29o37NJUuWxOGHHz5y3pk5c2b09fXF0qVLR91n+7cVOvfcc0f9WnPnzo3DDz985Bw0ll9r2zlu+6/T398/6nO2nef+9/y6/efs7Py5s9+f7efZ2bl6+/v09/fv8Gttf5++vr6XPPadHcf2/vc8/kJfZyzXD9uVzxmr7Wfs7++PBQsWRKVSiQULFuywpHyheXblz2J3HcfOHj+Z9uSf1/Z29hjLtDePPdueOtaJEyfGJZdcEpMmTYqPf/zjLf17CPuqMj8XhVbQLg3ua8/jyqpStPojZS9YtGhRrFu3Ln73u9/ttq85NDQUPT09MTg4GN3d3bvt67aC4eHhWL58eZxxxhmluNA1e8fWrVtj3rx5ERGxYsUK1xvZg8rW4P8+Nurvqe9DV7t7EbWIjpv/3zUiSjizBvesMjfosUErKFuD0Go0CLk0CLk0SDN7gzL8ddI+52tf+1q84x3viP333z9uvfXWuOGGG3Z4OwUAAAAAAIAMlj+74N57742rr746tmzZEoceemhce+218aEPfSh7rLbR0dERAwMD0dHRkT0KtCUNQi4NQi4NQi4NQi4NQi4N0gzLn13w4x//OHuEtlatVmPq1KnZY0Db0iDk0iDk0iDk0iDk0iDk0iDNqGYPAM0aHh6OX/3qVzE8PJw9CrQlDUIuDUIuDUIuDUIuDUIuDdIMyx9KqVarZY8AbU2DkEuDkEuDkEuDkEuDkEuDjJXlDwAAAAAAQAux/AEAAAAAAGghlj+UTmdnZ8ydOzc6OzuzR4G2pEHIpUHIpUHIpUHIpUHIpUGaYflDKXV1dWWPAG1Ng5BLg5BLg5BLg5BLg5BLg4yV5Q+lU6vVYvny5S5uBkk0CLk0CLk0CLk0CLk0CLk0SDMsfwAAAAAAAFqI5Q8AAAAAAEALsfwBAAAAAABoIZY/lE5nZ2ecccYZ0dnZmT0KtCUNQi4NQi4NQi4NQi4NQi4N0gzLH0pp69at2SNAW9Mg5NIg5NIg5NIg5NIg5NIgY2X5Q+nUarVYuXJl1Gq17FGgLWkQcmkQcmkQcmkQcmkQcmmQZlj+AAAAAAAAtBDLHwAAAAAAgBZi+UMpuagZ5NIg5NIg5NIg5NIg5NIg5NIgY+WRQumMGzcuzjzzzOwxoG1pEHJpEHJpEHJpEHJpEHJpkGZ45Q+l02g0YvPmzdFoNLJHgbakQcilQcilQcilQcilQcilQZph+UPp1Ov1WL16ddTr9exRoC1pEHJpEHJpEHJpEHJpEHJpkGZY/gAAAAAAALQQyx8AAAAAAIAWYvlD6VQqlZg0aVJUKpXsUaAtaRByaRByaRByaRByaRByaZBmdGYPAM3q7OyMk08+OXsMaFsahFwahFwahFwahFwahFwapBle+UPpNBqNePjhh6PRaGSPAm1Jg5BLg5BLg5BLg5BLg5BLgzTD8ofSqdfrsXbt2qjX69mjQFvSIOTSIOTSIOTSIOTSIOTSIM2w/AEAAAAAAGghlj8AAAAAAAAtxPKH0qlUKjFlypSoVCrZo0Bb0iDk0iDk0iDk0iDk0iDk0iDN6MweAJrV2dkZb3nLW7LHgLalQcilQcilQcilQcilQcilQZrhlT+UTr1ej3Xr1rmwGSTRIOTSIOTSIOTSIOTSIOTSIM2w/KF0Go1GrF+/PhqNRvYo0JY0CLk0CLk0CLk0CLk0CLk0SDMsfwAAAAAAAFqIa/4ALWHixImxYsWKkf8GYN/g+zMAAADsfZY/lE61Wo3+/v6oVr1wjf+vUqlEV1dX9hhtQYOQq2wN+v5Mqylbg9BqNAi5NAi5NEgzLH8onY6Ojjj22GOzx4C2pUHIpUHIpUHIpUHIpUHIpUGaYUVI6dTr9bjvvvuiXq9njwJtSYOQS4OQS4OQS4OQS4OQS4M0w/KH0mk0GrFx48ZoNBrZo0Bb0iDk0iDk0iDk0iDk0iDk0iDNsPwBAAAAAABoIZY/AAAAAAAALcTyh9KpVqtxxBFHRLXq4QsZNAi5NAi5NAi5NAi5NAi5NEgzOrMHgGZ1dHTEkUcemT0GtC0NQi4NQi4NQi4NQi4NQi4N0gwrQkqnVqvFXXfdFbVaLXsUaEsahFwahFwahFwahFwahFwapBmWP5ROURTxn//8J4qiyB4F2pIGIZcGIZcGIZcGIZcGIZcGaYblDwAAAAAAQAux/AEAAAAAAGghlj+UTkdHR7zhDW+Ijo6O7FGgLWkQcmkQcmkQcmkQcmkQcmmQZnRmDwDNqlarMXPmzOwxoG1pEHJpEHJpEHJpEHJpEHJpkGZ45Q+lU6vV4o477oharZY9CrQlDUIuDUIuDUIuDUIuDUIuDdIMyx9KpyiK2LJlSxRFkT0KtCUNQi4NQi4NQi4NQi4NQi4N0gzLHwAAAAAAgBZi+QMAAAAAANBCLH8onY6OjhgYGIiOjo7sUaAtaRByaRByaRByaRByaRByaZBmdGYPAM2qVqsxderU7DGgbWkQcmkQcmkQcmkQcmkQcmmQZnjlD6UzPDwcv/rVr2J4eDh7FGhLGoRcGoRcGoRcGoRcGoRcGqQZlj+UUq1Wyx4B2poGIZcGIZcGIZcGIZcGIZcGGSvLHwAAAAAAgBZi+QMAAAAAANBCLH8onc7Ozpg7d250dnZmjwJtSYOQS4OQS4OQS4OQS4OQS4M0w/KHUurq6soeAdqaBiGXBiGXBiGXBiGXBiGXBhkryx9Kp1arxfLly13cDJJoEHJpEHJpEHJpEHJpEHJpkGZY/gAAAAAAALQQyx8AAAAAAIAWYvkDAAAAAADQQipFURTZQ7CjoaGh6OnpicHBweju7s4eZ59SFEXUarXo7OyMSqWSPQ60nbI1uHXr1pg3b15ERNTfU4/oTB5oLGoRHTd3REQ5Z16xYoULUO5BZWsQWo0GIZcGIZcGIZcGaWZv4JU/lNLWrVuzR4C2pkHIpUHIpUHIpUHIpUHIpUHGyvKH0qnVarFy5cqo1WrZo0Bb0iDk0iDk0iDk0iDk0iDk0iDNsPwBAAAAAABoIZY/AAAAAAAALcTyh1Lq7CzD1c+hdWkQcmkQcmkQcmkQcmkQcmmQsaoURVFkD8GOhoaGoqenJwYHB6O7uzt7HIDS2rp1a8ybNy8iIurvqUeU4TlSLaLj5o6IKOfMK1asiK6uruSBAAAAAFpLM3sDr/yhdBqNRmzevDkajUb2KNCWSt1grUQ/yjwze1SpG4QWoEHIpUHIpUHIpUGaUYZ/Swyj1Ov1WL16dZxxxhlRrdpfwt5W5gY7ftmRPULTyjgze1aZG4RWoEHIpUHIpUHIpUGa4RECAAAAAADQQrzyB4CWNnHixFixYkX2GE0piiKee+65iIiYMGFCVCqV5ImaM3HixOwRAAAAANqa5Q+lU6lUYtKkSaX7y1BoFWVrsFKpRFdXV/YYTdtvv/2yR2AfVbYGodVoEHJpEHJpEHJpkGZUiqIosodgR0NDQ9HT0xODg4PR3d2dPQ4AAAAAAJComb2Ba/5QOo1GIx5++OFoNBrZo0Bb0iDk0iDk0iDk0iDk0iDk0iDNsPyhdOr1eqxduzbq9Xr2KNCWNAi5NAi5NAi5NAi5NAi5NEgzLH8AAAAAAABaiOUPAAAAAABAC7H8oXQqlUpMmTIlKpVK9ijQljQIuTQIuTQIuTQIuTQIuTRIMypFURTZQ7CjoaGh6OnpicHBweju7s4eBwAAAAAASNTM3sArfyider0e69atc2EzSKJByKVByKVByKVByKVByKVBmmH5Q+k0Go1Yv359NBqN7FGgLWkQcmkQcmkQcmkQcmkQcmmQZlj+AAAAAAAAtBDLHwAAAAAAgBZi+UPpVKvV6O/vj2rVwxcyaBByaRByaRByaRByaRByaZBmVIqiKLKHYEdDQ0PR09MTg4OD0d3dnT0OAAAAAACQqJm9gRUhpVOv1+O+++6Ler2ePQq0JQ1CLg1CLg1CLg1CLg1CLg3SDMsfSqfRaMTGjRuj0WhkjwJtSYOQS4OQS4OQS4OQS4OQS4M0w/IHAAAAAACghXRmD8DObbsU09DQUPIk+57h4eF45plnYmhoKMaNG5c9DrQdDUIuDUIuDUIuDUIuDUIuDbJtX7Btf/BiLH/2UVu2bImIiFe96lXJkwAAAAAAAPuKLVu2RE9Pz4vep1KMZUXEXtdoNOKRRx6JSZMmRaVSyR5nnzI0NBSvetWr4p///Gd0d3dnjwNtR4OQS4OQS4OQS4OQS4OQS4MURRFbtmyJGTNmRLX64lf18cqffVS1Wo2DDz44e4x9Wnd3t29ykEiDkEuDkEuDkEuDkEuDkEuD7e2lXvGzzYuvhgAAAAAAACgVyx8AAAAAAIAWYvlD6UyYMCGWLVsWEyZMyB4F2pIGIZcGIZcGIZcGIZcGIZcGaUalKIoiewgAAAAAAAB2D6/8AQAAAAAAaCGWPwAAAAAAAC3E8gcAAAAAAKCFWP4AAAAAAAC0EMsfAAAAAACAFmL5Q6l8+9vfjle/+tUxceLEmDNnTtx7773ZI0FL+vznPx+VSmXUjyOPPHLk9meffTYWL14cBx54YBxwwAFx9tlnx2OPPZY4MZTbb3/723jnO98ZM2bMiEqlEj//+c9H3V4URVx55ZUxffr06OrqilNPPTUeeOCBUfd54okn4rzzzovu7u7o7e2NCy+8MJ566qm9eBRQXi/V4Pvf//4dzovz588fdR8Nwq676qqr4k1velNMmjQppk6dGu9+97tj/fr1o+4zluefGzdujDPPPDP222+/mDp1anzyk5+MWq22Nw8FSmksDb797W/f4Vx40UUXjbqPBmHXfOc734ljjjkmuru7o7u7OwYGBuLWW28dud05kF1l+UNp/OhHP4olS5bEsmXL4k9/+lPMnj075s2bF5s3b84eDVrS61//+nj00UdHfvz+978fue2yyy6LX/7yl/GTn/wk7rzzznjkkUfirLPOSpwWyu3pp5+O2bNnx7e//e2d3n711VfHtddeG9ddd13cc889sf/++8e8efPi2WefHbnPeeedF/fff3/cfvvtccstt8Rvf/vb+PCHP7y3DgFK7aUajIiYP3/+qPPiTTfdNOp2DcKuu/POO2Px4sVx9913x+233x7Dw8Nx2mmnxdNPPz1yn5d6/lmv1+PMM8+M559/Pu6666644YYb4vrrr48rr7wy45CgVMbSYETEokWLRp0Lr7766pHbNAi77uCDD44vf/nLsWbNmvjjH/8YJ598crzrXe+K+++/PyKcA3kZCiiJE044oVi8ePHIz+v1ejFjxoziqquuSpwKWtOyZcuK2bNn7/S2J598shg3blzxk5/8ZORjf//734uIKFavXr2XJoTWFRHFzTffPPLzRqNRTJs2rfjqV7868rEnn3yymDBhQnHTTTcVRVEUf/vb34qIKP7whz+M3OfWW28tKpVK8e9//3uvzQ6tYPsGi6IoLrjgguJd73rXC36OBmH32rx5cxERxZ133lkUxdiefy5fvryoVqvFpk2bRu7zne98p+ju7i6ee+65vXsAUHLbN1gURfG2t72tuOSSS17wczQIu9crXvGK4nvf+55zIC+LV/5QCs8//3ysWbMmTj311JGPVavVOPXUU2P16tWJk0HreuCBB2LGjBlx6KGHxnnnnRcbN26MiIg1a9bE8PDwqB6PPPLI6O/v1yPsAQ899FBs2rRpVHM9PT0xZ86ckeZWr14dvb29cfzxx4/c59RTT41qtRr33HPPXp8ZWtGqVati6tSpccQRR8RHPvKRePzxx0du0yDsXoODgxERMXny5IgY2/PP1atXx6xZs6Kvr2/kPvPmzYuhoaGRfzkNjM32DW7zgx/8IA466KA4+uij44orrohnnnlm5DYNwu5Rr9fjhz/8YTz99NMxMDDgHMjL0pk9AIzFf//736jX66O+iUVE9PX1xbp165KmgtY1Z86cuP766+OII46IRx99NL7whS/EW9/61vjrX/8amzZtivHjx0dvb++oz+nr64tNmzblDAwtbFtXOzsHbrtt06ZNMXXq1FG3d3Z2xuTJk3UJu8H8+fPjrLPOikMOOSQ2bNgQn/nMZ+L000+P1atXR0dHhwZhN2o0GnHppZfGiSeeGEcffXRExJief27atGmn58pttwFjs7MGIyLe+973xsyZM2PGjBnx5z//OS6//PJYv359/OxnP4sIDcLL9Ze//CUGBgbi2WefjQMOOCBuvvnmOOqoo2Lt2rXOgewyyx8AdnD66aeP/PcxxxwTc+bMiZkzZ8aPf/zj6OrqSpwMAPa+c889d+S/Z82aFcccc0y85jWviVWrVsUpp5ySOBm0nsWLF8df//rXUdebBPaeF2rwf69jN2vWrJg+fXqccsopsWHDhnjNa16zt8eElnPEEUfE2rVrY3BwMH7605/GBRdcEHfeeWf2WJSct32jFA466KDo6OiIxx57bNTHH3vssZg2bVrSVNA+ent747WvfW08+OCDMW3atHj++efjySefHHUfPcKesa2rFzsHTps2LTZv3jzq9lqtFk888YQuYQ849NBD46CDDooHH3wwIjQIu8vFF18ct9xyS6xcuTIOPvjgkY+P5fnntGnTdnqu3HYb8NJeqMGdmTNnTkTEqHOhBmHXjR8/Pg477LA47rjj4qqrrorZs2fHN7/5TedAXhbLH0ph/Pjxcdxxx8VvfvObkY81Go34zW9+EwMDA4mTQXt46qmnYsOGDTF9+vQ47rjjYty4caN6XL9+fWzcuFGPsAcccsghMW3atFHNDQ0NxT333DPS3MDAQDz55JOxZs2akfvccccd0Wg0Rv7HHNh9/vWvf8Xjjz8e06dPjwgNwstVFEVcfPHFcfPNN8cdd9wRhxxyyKjbx/L8c2BgIP7yl7+MWsTefvvt0d3dHUcdddTeORAoqZdqcGfWrl0bETHqXKhB2H0ajUY899xzzoG8LN72jdJYsmRJXHDBBXH88cfHCSecEN/4xjfi6aefjg984APZo0HL+cQnPhHvfOc7Y+bMmfHII4/EsmXLoqOjIxYsWBA9PT1x4YUXxpIlS2Ly5MnR3d0dH/vYx2JgYCDe/OY3Z48OpfTUU0+N/KvJiIiHHnoo1q5dG5MnT47+/v649NJL40tf+lIcfvjhccghh8TnPve5mDFjRrz73e+OiIjXve51MX/+/Fi0aFFcd911MTw8HBdffHGce+65MWPGjKSjgvJ4sQYnT54cX/jCF+Lss8+OadOmxYYNG+JTn/pUHHbYYTFv3ryI0CC8XIsXL44bb7wxfvGLX8SkSZNGrk/Q09MTXV1dY3r+edppp8VRRx0VCxcujKuvvjo2bdoUn/3sZ2Px4sUxYcKEzMODfd5LNbhhw4a48cYb44wzzogDDzww/vznP8dll10WJ510UhxzzDERoUF4Oa644oo4/fTTo7+/P7Zs2RI33nhjrFq1KlasWOEcyMtTQIl861vfKvr7+4vx48cXJ5xwQnH33XdnjwQt6ZxzzimmT59ejB8/vnjlK19ZnHPOOcWDDz44cvvWrVuLj370o8UrXvGKYr/99ive8573FI8++mjixFBuK1euLCJihx8XXHBBURRF0Wg0is997nNFX19fMWHChOKUU04p1q9fP+prPP7448WCBQuKAw44oOju7i4+8IEPFFu2bEk4GiifF2vwmWeeKU477bRiypQpxbhx44qZM2cWixYtKjZt2jTqa2gQdt3O+ouI4vvf//7Ifcby/PMf//hHcfrppxddXV3FQQcdVCxdurQYHh7ey0cD5fNSDW7cuLE46aSTismTJxcTJkwoDjvssOKTn/xkMTg4OOrraBB2zQc/+MFi5syZxfjx44spU6YUp5xySnHbbbeN3O4cyK6qFEVR7M1lEwAAAAAAAHuOa/4AAAAAAAC0EMsfAAAAAACAFmL5AwAAAAAA0EIsfwAAAAAAAFqI5Q8AAAAAAEALsfwBAAAAAABoIZY/AAAAAAAALcTyBwAAAAAAoIVY/gAAAAAAALQQyx8AAAAAAIAWYvkDAAAAAADQQv4POUs1C1d2A4YAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,8))\n",
    "plt.title('Data Distribution', fontdict=dict(fontsize=30))\n",
    "plt.grid(True, which='both', linestyle='--')\n",
    "sns.boxplot(data=df_main[['MSSubClass', 'Foundation', 'LotFrontage']], orient='h')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a68430a8",
   "metadata": {},
   "source": [
    "# PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "34fcd1bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.decomposition import PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "e7c3c820",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MSSubClass       float32\n",
       "MSZoning         float32\n",
       "LotFrontage      float32\n",
       "LotArea          float32\n",
       "Street           float32\n",
       "Alley            float32\n",
       "LotShape         float32\n",
       "LandContour      float32\n",
       "Utilities        float32\n",
       "LotConfig        float32\n",
       "LandSlope        float32\n",
       "Neighborhood     float32\n",
       "Condition1       float32\n",
       "Condition2       float32\n",
       "BldgType         float32\n",
       "HouseStyle       float32\n",
       "OverallQual      float32\n",
       "OverallCond      float32\n",
       "YearBuilt        float32\n",
       "YearRemodAdd     float32\n",
       "RoofStyle        float32\n",
       "RoofMatl         float32\n",
       "Exterior1st      float32\n",
       "Exterior2nd      float32\n",
       "MasVnrType       float32\n",
       "MasVnrArea       float32\n",
       "ExterQual        float32\n",
       "ExterCond        float32\n",
       "Foundation       float32\n",
       "BsmtQual         float32\n",
       "BsmtCond         float32\n",
       "BsmtExposure     float32\n",
       "BsmtFinType1     float32\n",
       "BsmtFinSF1       float32\n",
       "BsmtFinType2     float32\n",
       "BsmtFinSF2       float32\n",
       "BsmtUnfSF        float32\n",
       "TotalBsmtSF      float32\n",
       "Heating          float32\n",
       "HeatingQC        float32\n",
       "CentralAir       float32\n",
       "Electrical       float32\n",
       "1stFlrSF         float32\n",
       "2ndFlrSF         float32\n",
       "LowQualFinSF     float32\n",
       "GrLivArea        float32\n",
       "BsmtFullBath     float32\n",
       "BsmtHalfBath     float32\n",
       "FullBath         float32\n",
       "HalfBath         float32\n",
       "BedroomAbvGr     float32\n",
       "KitchenAbvGr     float32\n",
       "KitchenQual      float32\n",
       "TotRmsAbvGrd     float32\n",
       "Functional       float32\n",
       "Fireplaces       float32\n",
       "FireplaceQu      float32\n",
       "GarageType       float32\n",
       "GarageYrBlt      float32\n",
       "GarageFinish     float32\n",
       "GarageCars       float32\n",
       "GarageArea       float32\n",
       "GarageQual       float32\n",
       "GarageCond       float32\n",
       "PavedDrive       float32\n",
       "WoodDeckSF       float32\n",
       "OpenPorchSF      float32\n",
       "EnclosedPorch    float32\n",
       "3SsnPorch        float32\n",
       "ScreenPorch      float32\n",
       "PoolArea         float32\n",
       "PoolQC           float32\n",
       "Fence            float32\n",
       "MiscFeature      float32\n",
       "MiscVal          float32\n",
       "MoSold           float32\n",
       "YrSold           float32\n",
       "SaleType         float32\n",
       "SaleCondition    float32\n",
       "dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#convert dtypes to float32\n",
    "for i in df_main.columns:\n",
    "    df_main[i] = df_main[i].astype('float32')\n",
    "df_main.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b956baf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2917, 3)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PCA\n",
    "pca = PCA(n_components = 3)\n",
    "x_pca = pca.fit_transform(df_main)\n",
    "x_pca.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "c6a33ea0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_32443/59444205.py:3: UserWarning: No data for colormapping provided via 'c'. Parameters 'cmap' will be ignored\n",
      "  ax.scatter(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(1, figsize=(8, 6))\n",
    "ax = fig.add_subplot(111, projection=\"3d\", elev=-150, azim=110)\n",
    "ax.scatter(\n",
    "    x_pca[:, 0],\n",
    "    x_pca[:, 1],\n",
    "    x_pca[:, 2],\n",
    "    cmap=plt.cm.Set1,\n",
    "    edgecolor=\"k\",\n",
    "    s=40,\n",
    ")\n",
    "ax.set_title(\"First three PCA directions\")\n",
    "ax.set_xlabel(\"1st eigenvector\")\n",
    "ax.xaxis.set_ticklabels([])\n",
    "ax.set_ylabel(\"2nd eigenvector\")\n",
    "ax.yaxis.set_ticklabels([])\n",
    "ax.set_zlabel(\"3rd eigenvector\")\n",
    "ax.zaxis.set_ticklabels([])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "131c62a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}