imputation_scaling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order</th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>...</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 82 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
       "0      1  526301100           20        RL         141.0     31770   Pave   \n",
       "1      1  526301100           20        RL         141.0     31770   Pave   \n",
       "2      2  526350040           20        RH          80.0     11622   Pave   \n",
       "3      3  526351010           20        RL          81.0     14267   Pave   \n",
       "4      4  526353030           20        RL          93.0     11160   Pave   \n",
       "\n",
       "  Alley Lot Shape Land Contour  ... Pool Area Pool QC  Fence Misc Feature  \\\n",
       "0   NaN       IR1          Lvl  ...         0     NaN    NaN          NaN   \n",
       "1   NaN       IR1          Lvl  ...         0     NaN    NaN          NaN   \n",
       "2   NaN       Reg          Lvl  ...         0     NaN  MnPrv          NaN   \n",
       "3   NaN       IR1          Lvl  ...         0     NaN    NaN         Gar2   \n",
       "4   NaN       Reg          Lvl  ...         0     NaN    NaN          NaN   \n",
       "\n",
       "  Misc Val Mo Sold Yr Sold Sale Type  Sale Condition  SalePrice  \n",
       "0        0       5    2010       WD           Normal     215000  \n",
       "1        0       5    2010       WD           Normal     215000  \n",
       "2        0       6    2010       WD           Normal     105000  \n",
       "3    12500       6    2010       WD           Normal     172000  \n",
       "4        0       4    2010       WD           Normal     244000  \n",
       "\n",
       "[5 rows x 82 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('Ames_Housing_Data1.tsv', sep='\\t')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Explore Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2931, 82)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Order', 'PID', 'MS SubClass', 'MS Zoning', 'Lot Frontage', 'Lot Area',\n",
       "       'Street', 'Alley', 'Lot Shape', 'Land Contour', 'Utilities',\n",
       "       'Lot Config', 'Land Slope', 'Neighborhood', 'Condition 1',\n",
       "       'Condition 2', 'Bldg Type', 'House Style', 'Overall Qual',\n",
       "       'Overall Cond', 'Year Built', 'Year Remod/Add', 'Roof Style',\n",
       "       'Roof Matl', 'Exterior 1st', 'Exterior 2nd', 'Mas Vnr Type',\n",
       "       'Mas Vnr Area', 'Exter Qual', 'Exter Cond', 'Foundation', 'Bsmt Qual',\n",
       "       'Bsmt Cond', 'Bsmt Exposure', 'BsmtFin Type 1', 'BsmtFin SF 1',\n",
       "       'BsmtFin Type 2', 'BsmtFin SF 2', 'Bsmt Unf SF', 'Total Bsmt SF',\n",
       "       'Heating', 'Heating QC', 'Central Air', 'Electrical', '1st Flr SF',\n",
       "       '2nd Flr SF', 'Low Qual Fin SF', 'Gr Liv Area', 'Bsmt Full Bath',\n",
       "       'Bsmt Half Bath', 'Full Bath', 'Half Bath', 'Bedroom AbvGr',\n",
       "       'Kitchen AbvGr', 'Kitchen Qual', 'TotRms AbvGrd', 'Functional',\n",
       "       'Fireplaces', 'Fireplace Qu', 'Garage Type', 'Garage Yr Blt',\n",
       "       'Garage Finish', 'Garage Cars', 'Garage Area', 'Garage Qual',\n",
       "       'Garage Cond', 'Paved Drive', 'Wood Deck SF', 'Open Porch SF',\n",
       "       'Enclosed Porch', '3Ssn Porch', 'Screen Porch', 'Pool Area', 'Pool QC',\n",
       "       'Fence', 'Misc Feature', 'Misc Val', 'Mo Sold', 'Yr Sold', 'Sale Type',\n",
       "       'Sale Condition', 'SalePrice'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Order</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1465.000341</td>\n",
       "      <td>846.250554</td>\n",
       "      <td>1.0</td>\n",
       "      <td>732.5</td>\n",
       "      <td>1465.0</td>\n",
       "      <td>2197.5</td>\n",
       "      <td>2930.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PID</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>714400299.309792</td>\n",
       "      <td>188730640.349985</td>\n",
       "      <td>526301100.0</td>\n",
       "      <td>528477015.0</td>\n",
       "      <td>535453210.0</td>\n",
       "      <td>907181095.0</td>\n",
       "      <td>1007100110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>MS SubClass</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.374616</td>\n",
       "      <td>42.636341</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>MS Zoning</th>\n",
       "      <td>2931</td>\n",
       "      <td>7</td>\n",
       "      <td>RL</td>\n",
       "      <td>2274</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>Lot Frontage</th>\n",
       "      <td>2441.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>69.253994</td>\n",
       "      <td>23.405675</td>\n",
       "      <td>21.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>68.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>313.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lot Area</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10155.298874</td>\n",
       "      <td>7888.789128</td>\n",
       "      <td>1300.0</td>\n",
       "      <td>7440.5</td>\n",
       "      <td>9439.0</td>\n",
       "      <td>11556.0</td>\n",
       "      <td>215245.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Street</th>\n",
       "      <td>2931</td>\n",
       "      <td>2</td>\n",
       "      <td>Pave</td>\n",
       "      <td>2919</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>Lot Shape</th>\n",
       "      <td>2931</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>Land Contour</th>\n",
       "      <td>2931</td>\n",
       "      <td>4</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>2634</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>2931</td>\n",
       "      <td>3</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>2928</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>Lot Config</th>\n",
       "      <td>2931</td>\n",
       "      <td>5</td>\n",
       "      <td>Inside</td>\n",
       "      <td>2140</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>Land Slope</th>\n",
       "      <td>2931</td>\n",
       "      <td>3</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>2790</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>2931</td>\n",
       "      <td>28</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>444</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>Condition 1</th>\n",
       "      <td>2931</td>\n",
       "      <td>9</td>\n",
       "      <td>Norm</td>\n",
       "      <td>2523</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>Condition 2</th>\n",
       "      <td>2931</td>\n",
       "      <td>8</td>\n",
       "      <td>Norm</td>\n",
       "      <td>2901</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>Bldg Type</th>\n",
       "      <td>2931</td>\n",
       "      <td>5</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2426</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>House Style</th>\n",
       "      <td>2931</td>\n",
       "      <td>8</td>\n",
       "      <td>1Story</td>\n",
       "      <td>1482</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>Overall Qual</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.094848</td>\n",
       "      <td>1.410786</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>Overall Cond</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.562948</td>\n",
       "      <td>1.111396</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>Year Built</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1971.352439</td>\n",
       "      <td>30.240926</td>\n",
       "      <td>1872.0</td>\n",
       "      <td>1954.0</td>\n",
       "      <td>1973.0</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1984.258274</td>\n",
       "      <td>20.861542</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>Roof Style</th>\n",
       "      <td>2931</td>\n",
       "      <td>6</td>\n",
       "      <td>Gable</td>\n",
       "      <td>2321</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>Roof Matl</th>\n",
       "      <td>2931</td>\n",
       "      <td>8</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>2888</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>Exterior 1st</th>\n",
       "      <td>2931</td>\n",
       "      <td>16</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>1026</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>Exterior 2nd</th>\n",
       "      <td>2931</td>\n",
       "      <td>17</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>1015</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>Mas Vnr Type</th>\n",
       "      <td>2908</td>\n",
       "      <td>5</td>\n",
       "      <td>None</td>\n",
       "      <td>1752</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>Mas Vnr Area</th>\n",
       "      <td>2908.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>101.900275</td>\n",
       "      <td>179.081899</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>Exter Qual</th>\n",
       "      <td>2931</td>\n",
       "      <td>4</td>\n",
       "      <td>TA</td>\n",
       "      <td>1800</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>Exter Cond</th>\n",
       "      <td>2931</td>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2550</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>2931</td>\n",
       "      <td>6</td>\n",
       "      <td>PConc</td>\n",
       "      <td>1310</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>Bsmt Qual</th>\n",
       "      <td>2851</td>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>1284</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>Bsmt Cond</th>\n",
       "      <td>2851</td>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2616</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>Bsmt Exposure</th>\n",
       "      <td>2848</td>\n",
       "      <td>4</td>\n",
       "      <td>No</td>\n",
       "      <td>1906</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>BsmtFin Type 1</th>\n",
       "      <td>2851</td>\n",
       "      <td>6</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>859</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>BsmtFin SF 1</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>442.696587</td>\n",
       "      <td>455.527506</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>370.5</td>\n",
       "      <td>734.0</td>\n",
       "      <td>5644.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BsmtFin Type 2</th>\n",
       "      <td>2850</td>\n",
       "      <td>6</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2500</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>BsmtFin SF 2</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>49.705461</td>\n",
       "      <td>169.142089</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>Bsmt Unf SF</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>559.222184</td>\n",
       "      <td>439.424553</td>\n",
       "      <td>0.0</td>\n",
       "      <td>219.0</td>\n",
       "      <td>465.5</td>\n",
       "      <td>801.75</td>\n",
       "      <td>2336.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1051.624232</td>\n",
       "      <td>440.540157</td>\n",
       "      <td>0.0</td>\n",
       "      <td>793.0</td>\n",
       "      <td>990.0</td>\n",
       "      <td>1301.5</td>\n",
       "      <td>6110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Heating</th>\n",
       "      <td>2931</td>\n",
       "      <td>6</td>\n",
       "      <td>GasA</td>\n",
       "      <td>2886</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>Heating QC</th>\n",
       "      <td>2931</td>\n",
       "      <td>5</td>\n",
       "      <td>Ex</td>\n",
       "      <td>1495</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>Central Air</th>\n",
       "      <td>2931</td>\n",
       "      <td>2</td>\n",
       "      <td>Y</td>\n",
       "      <td>2735</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>2930</td>\n",
       "      <td>5</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>2683</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>1st Flr SF</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1159.727056</td>\n",
       "      <td>391.93129</td>\n",
       "      <td>334.0</td>\n",
       "      <td>876.5</td>\n",
       "      <td>1084.0</td>\n",
       "      <td>1385.5</td>\n",
       "      <td>5095.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>335.341522</td>\n",
       "      <td>428.36742</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>703.5</td>\n",
       "      <td>2065.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Low Qual Fin SF</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.675196</td>\n",
       "      <td>46.302687</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>Gr Liv Area</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1499.743773</td>\n",
       "      <td>505.430862</td>\n",
       "      <td>334.0</td>\n",
       "      <td>1126.0</td>\n",
       "      <td>1442.0</td>\n",
       "      <td>1742.5</td>\n",
       "      <td>5642.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <td>2929.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.431547</td>\n",
       "      <td>0.524836</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>Bsmt Half Bath</th>\n",
       "      <td>2929.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.061113</td>\n",
       "      <td>0.245214</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>Full Bath</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.56636</td>\n",
       "      <td>0.552945</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>Half Bath</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.379393</td>\n",
       "      <td>0.502592</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>Bedroom AbvGr</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.854316</td>\n",
       "      <td>0.827594</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>Kitchen AbvGr</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.044353</td>\n",
       "      <td>0.214041</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>Kitchen Qual</th>\n",
       "      <td>2931</td>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>1495</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>TotRms AbvGrd</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.443193</td>\n",
       "      <td>1.57273</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>2931</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2729</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>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.599795</td>\n",
       "      <td>0.648327</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>Fireplace Qu</th>\n",
       "      <td>1509</td>\n",
       "      <td>5</td>\n",
       "      <td>Gd</td>\n",
       "      <td>745</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>Garage Type</th>\n",
       "      <td>2774</td>\n",
       "      <td>6</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1732</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>Garage Yr Blt</th>\n",
       "      <td>2772.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1978.125902</td>\n",
       "      <td>25.526128</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>Garage Finish</th>\n",
       "      <td>2772</td>\n",
       "      <td>3</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1231</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>Garage Cars</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.766894</td>\n",
       "      <td>0.760449</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>Garage Area</th>\n",
       "      <td>2930.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>472.838567</td>\n",
       "      <td>215.012252</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>Garage Qual</th>\n",
       "      <td>2772</td>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2616</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>Garage Cond</th>\n",
       "      <td>2772</td>\n",
       "      <td>5</td>\n",
       "      <td>TA</td>\n",
       "      <td>2666</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>Paved Drive</th>\n",
       "      <td>2931</td>\n",
       "      <td>3</td>\n",
       "      <td>Y</td>\n",
       "      <td>2652</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>Wood Deck SF</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>93.791539</td>\n",
       "      <td>126.358242</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>Open Porch SF</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.538383</td>\n",
       "      <td>67.472412</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>742.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.003753</td>\n",
       "      <td>64.129522</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>3Ssn Porch</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.591607</td>\n",
       "      <td>25.137086</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>Screen Porch</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.996588</td>\n",
       "      <td>56.078577</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>Pool Area</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.242579</td>\n",
       "      <td>35.59113</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>Pool QC</th>\n",
       "      <td>13</td>\n",
       "      <td>4</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>572</td>\n",
       "      <td>4</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>330</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>Misc Feature</th>\n",
       "      <td>106</td>\n",
       "      <td>5</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>Misc Val</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>50.617878</td>\n",
       "      <td>566.248407</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>Mo Sold</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.215626</td>\n",
       "      <td>2.714122</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>Yr Sold</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2007.791198</td>\n",
       "      <td>1.317021</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>Sale Type</th>\n",
       "      <td>2931</td>\n",
       "      <td>10</td>\n",
       "      <td>WD</td>\n",
       "      <td>2537</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>Sale Condition</th>\n",
       "      <td>2931</td>\n",
       "      <td>6</td>\n",
       "      <td>Normal</td>\n",
       "      <td>2414</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>SalePrice</th>\n",
       "      <td>2931.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>180807.729785</td>\n",
       "      <td>79875.557267</td>\n",
       "      <td>12789.0</td>\n",
       "      <td>129500.0</td>\n",
       "      <td>160000.0</td>\n",
       "      <td>213500.0</td>\n",
       "      <td>755000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  count unique      top  freq              mean  \\\n",
       "Order            2931.0    NaN      NaN   NaN       1465.000341   \n",
       "PID              2931.0    NaN      NaN   NaN  714400299.309792   \n",
       "MS SubClass      2931.0    NaN      NaN   NaN         57.374616   \n",
       "MS Zoning          2931      7       RL  2274               NaN   \n",
       "Lot Frontage     2441.0    NaN      NaN   NaN         69.253994   \n",
       "Lot Area         2931.0    NaN      NaN   NaN      10155.298874   \n",
       "Street             2931      2     Pave  2919               NaN   \n",
       "Alley               198      2     Grvl   120               NaN   \n",
       "Lot Shape          2931      4      Reg  1859               NaN   \n",
       "Land Contour       2931      4      Lvl  2634               NaN   \n",
       "Utilities          2931      3   AllPub  2928               NaN   \n",
       "Lot Config         2931      5   Inside  2140               NaN   \n",
       "Land Slope         2931      3      Gtl  2790               NaN   \n",
       "Neighborhood       2931     28    NAmes   444               NaN   \n",
       "Condition 1        2931      9     Norm  2523               NaN   \n",
       "Condition 2        2931      8     Norm  2901               NaN   \n",
       "Bldg Type          2931      5     1Fam  2426               NaN   \n",
       "House Style        2931      8   1Story  1482               NaN   \n",
       "Overall Qual     2931.0    NaN      NaN   NaN          6.094848   \n",
       "Overall Cond     2931.0    NaN      NaN   NaN          5.562948   \n",
       "Year Built       2931.0    NaN      NaN   NaN       1971.352439   \n",
       "Year Remod/Add   2931.0    NaN      NaN   NaN       1984.258274   \n",
       "Roof Style         2931      6    Gable  2321               NaN   \n",
       "Roof Matl          2931      8  CompShg  2888               NaN   \n",
       "Exterior 1st       2931     16  VinylSd  1026               NaN   \n",
       "Exterior 2nd       2931     17  VinylSd  1015               NaN   \n",
       "Mas Vnr Type       2908      5     None  1752               NaN   \n",
       "Mas Vnr Area     2908.0    NaN      NaN   NaN        101.900275   \n",
       "Exter Qual         2931      4       TA  1800               NaN   \n",
       "Exter Cond         2931      5       TA  2550               NaN   \n",
       "Foundation         2931      6    PConc  1310               NaN   \n",
       "Bsmt Qual          2851      5       TA  1284               NaN   \n",
       "Bsmt Cond          2851      5       TA  2616               NaN   \n",
       "Bsmt Exposure      2848      4       No  1906               NaN   \n",
       "BsmtFin Type 1     2851      6      GLQ   859               NaN   \n",
       "BsmtFin SF 1     2930.0    NaN      NaN   NaN        442.696587   \n",
       "BsmtFin Type 2     2850      6      Unf  2500               NaN   \n",
       "BsmtFin SF 2     2930.0    NaN      NaN   NaN         49.705461   \n",
       "Bsmt Unf SF      2930.0    NaN      NaN   NaN        559.222184   \n",
       "Total Bsmt SF    2930.0    NaN      NaN   NaN       1051.624232   \n",
       "Heating            2931      6     GasA  2886               NaN   \n",
       "Heating QC         2931      5       Ex  1495               NaN   \n",
       "Central Air        2931      2        Y  2735               NaN   \n",
       "Electrical         2930      5    SBrkr  2683               NaN   \n",
       "1st Flr SF       2931.0    NaN      NaN   NaN       1159.727056   \n",
       "2nd Flr SF       2931.0    NaN      NaN   NaN        335.341522   \n",
       "Low Qual Fin SF  2931.0    NaN      NaN   NaN          4.675196   \n",
       "Gr Liv Area      2931.0    NaN      NaN   NaN       1499.743773   \n",
       "Bsmt Full Bath   2929.0    NaN      NaN   NaN          0.431547   \n",
       "Bsmt Half Bath   2929.0    NaN      NaN   NaN          0.061113   \n",
       "Full Bath        2931.0    NaN      NaN   NaN           1.56636   \n",
       "Half Bath        2931.0    NaN      NaN   NaN          0.379393   \n",
       "Bedroom AbvGr    2931.0    NaN      NaN   NaN          2.854316   \n",
       "Kitchen AbvGr    2931.0    NaN      NaN   NaN          1.044353   \n",
       "Kitchen Qual       2931      5       TA  1495               NaN   \n",
       "TotRms AbvGrd    2931.0    NaN      NaN   NaN          6.443193   \n",
       "Functional         2931      8      Typ  2729               NaN   \n",
       "Fireplaces       2931.0    NaN      NaN   NaN          0.599795   \n",
       "Fireplace Qu       1509      5       Gd   745               NaN   \n",
       "Garage Type        2774      6   Attchd  1732               NaN   \n",
       "Garage Yr Blt    2772.0    NaN      NaN   NaN       1978.125902   \n",
       "Garage Finish      2772      3      Unf  1231               NaN   \n",
       "Garage Cars      2930.0    NaN      NaN   NaN          1.766894   \n",
       "Garage Area      2930.0    NaN      NaN   NaN        472.838567   \n",
       "Garage Qual        2772      5       TA  2616               NaN   \n",
       "Garage Cond        2772      5       TA  2666               NaN   \n",
       "Paved Drive        2931      3        Y  2652               NaN   \n",
       "Wood Deck SF     2931.0    NaN      NaN   NaN         93.791539   \n",
       "Open Porch SF    2931.0    NaN      NaN   NaN         47.538383   \n",
       "Enclosed Porch   2931.0    NaN      NaN   NaN         23.003753   \n",
       "3Ssn Porch       2931.0    NaN      NaN   NaN          2.591607   \n",
       "Screen Porch     2931.0    NaN      NaN   NaN         15.996588   \n",
       "Pool Area        2931.0    NaN      NaN   NaN          2.242579   \n",
       "Pool QC              13      4       Ex     4               NaN   \n",
       "Fence               572      4    MnPrv   330               NaN   \n",
       "Misc Feature        106      5     Shed    95               NaN   \n",
       "Misc Val         2931.0    NaN      NaN   NaN         50.617878   \n",
       "Mo Sold          2931.0    NaN      NaN   NaN          6.215626   \n",
       "Yr Sold          2931.0    NaN      NaN   NaN       2007.791198   \n",
       "Sale Type          2931     10      WD   2537               NaN   \n",
       "Sale Condition     2931      6   Normal  2414               NaN   \n",
       "SalePrice        2931.0    NaN      NaN   NaN     180807.729785   \n",
       "\n",
       "                              std          min          25%          50%  \\\n",
       "Order                  846.250554          1.0        732.5       1465.0   \n",
       "PID              188730640.349985  526301100.0  528477015.0  535453210.0   \n",
       "MS SubClass             42.636341         20.0         20.0         50.0   \n",
       "MS Zoning                     NaN          NaN          NaN          NaN   \n",
       "Lot Frontage            23.405675         21.0         58.0         68.0   \n",
       "Lot Area              7888.789128       1300.0       7440.5       9439.0   \n",
       "Street                        NaN          NaN          NaN          NaN   \n",
       "Alley                         NaN          NaN          NaN          NaN   \n",
       "Lot Shape                     NaN          NaN          NaN          NaN   \n",
       "Land Contour                  NaN          NaN          NaN          NaN   \n",
       "Utilities                     NaN          NaN          NaN          NaN   \n",
       "Lot Config                    NaN          NaN          NaN          NaN   \n",
       "Land Slope                    NaN          NaN          NaN          NaN   \n",
       "Neighborhood                  NaN          NaN          NaN          NaN   \n",
       "Condition 1                   NaN          NaN          NaN          NaN   \n",
       "Condition 2                   NaN          NaN          NaN          NaN   \n",
       "Bldg Type                     NaN          NaN          NaN          NaN   \n",
       "House Style                   NaN          NaN          NaN          NaN   \n",
       "Overall Qual             1.410786          1.0          5.0          6.0   \n",
       "Overall Cond             1.111396          1.0          5.0          5.0   \n",
       "Year Built              30.240926       1872.0       1954.0       1973.0   \n",
       "Year Remod/Add          20.861542       1950.0       1965.0       1993.0   \n",
       "Roof Style                    NaN          NaN          NaN          NaN   \n",
       "Roof Matl                     NaN          NaN          NaN          NaN   \n",
       "Exterior 1st                  NaN          NaN          NaN          NaN   \n",
       "Exterior 2nd                  NaN          NaN          NaN          NaN   \n",
       "Mas Vnr Type                  NaN          NaN          NaN          NaN   \n",
       "Mas Vnr Area           179.081899          0.0          0.0          0.0   \n",
       "Exter Qual                    NaN          NaN          NaN          NaN   \n",
       "Exter Cond                    NaN          NaN          NaN          NaN   \n",
       "Foundation                    NaN          NaN          NaN          NaN   \n",
       "Bsmt Qual                     NaN          NaN          NaN          NaN   \n",
       "Bsmt Cond                     NaN          NaN          NaN          NaN   \n",
       "Bsmt Exposure                 NaN          NaN          NaN          NaN   \n",
       "BsmtFin Type 1                NaN          NaN          NaN          NaN   \n",
       "BsmtFin SF 1           455.527506          0.0          0.0        370.5   \n",
       "BsmtFin Type 2                NaN          NaN          NaN          NaN   \n",
       "BsmtFin SF 2           169.142089          0.0          0.0          0.0   \n",
       "Bsmt Unf SF            439.424553          0.0        219.0        465.5   \n",
       "Total Bsmt SF          440.540157          0.0        793.0        990.0   \n",
       "Heating                       NaN          NaN          NaN          NaN   \n",
       "Heating QC                    NaN          NaN          NaN          NaN   \n",
       "Central Air                   NaN          NaN          NaN          NaN   \n",
       "Electrical                    NaN          NaN          NaN          NaN   \n",
       "1st Flr SF              391.93129        334.0        876.5       1084.0   \n",
       "2nd Flr SF              428.36742          0.0          0.0          0.0   \n",
       "Low Qual Fin SF         46.302687          0.0          0.0          0.0   \n",
       "Gr Liv Area            505.430862        334.0       1126.0       1442.0   \n",
       "Bsmt Full Bath           0.524836          0.0          0.0          0.0   \n",
       "Bsmt Half Bath           0.245214          0.0          0.0          0.0   \n",
       "Full Bath                0.552945          0.0          1.0          2.0   \n",
       "Half Bath                0.502592          0.0          0.0          0.0   \n",
       "Bedroom AbvGr            0.827594          0.0          2.0          3.0   \n",
       "Kitchen AbvGr            0.214041          0.0          1.0          1.0   \n",
       "Kitchen Qual                  NaN          NaN          NaN          NaN   \n",
       "TotRms AbvGrd             1.57273          2.0          5.0          6.0   \n",
       "Functional                    NaN          NaN          NaN          NaN   \n",
       "Fireplaces               0.648327          0.0          0.0          1.0   \n",
       "Fireplace Qu                  NaN          NaN          NaN          NaN   \n",
       "Garage Type                   NaN          NaN          NaN          NaN   \n",
       "Garage Yr Blt           25.526128       1895.0       1960.0       1979.0   \n",
       "Garage Finish                 NaN          NaN          NaN          NaN   \n",
       "Garage Cars              0.760449          0.0          1.0          2.0   \n",
       "Garage Area            215.012252          0.0        320.0        480.0   \n",
       "Garage Qual                   NaN          NaN          NaN          NaN   \n",
       "Garage Cond                   NaN          NaN          NaN          NaN   \n",
       "Paved Drive                   NaN          NaN          NaN          NaN   \n",
       "Wood Deck SF           126.358242          0.0          0.0          0.0   \n",
       "Open Porch SF           67.472412          0.0          0.0         27.0   \n",
       "Enclosed Porch          64.129522          0.0          0.0          0.0   \n",
       "3Ssn Porch              25.137086          0.0          0.0          0.0   \n",
       "Screen Porch            56.078577          0.0          0.0          0.0   \n",
       "Pool Area                35.59113          0.0          0.0          0.0   \n",
       "Pool QC                       NaN          NaN          NaN          NaN   \n",
       "Fence                         NaN          NaN          NaN          NaN   \n",
       "Misc Feature                  NaN          NaN          NaN          NaN   \n",
       "Misc Val               566.248407          0.0          0.0          0.0   \n",
       "Mo Sold                  2.714122          1.0          4.0          6.0   \n",
       "Yr Sold                  1.317021       2006.0       2007.0       2008.0   \n",
       "Sale Type                     NaN          NaN          NaN          NaN   \n",
       "Sale Condition                NaN          NaN          NaN          NaN   \n",
       "SalePrice            79875.557267      12789.0     129500.0     160000.0   \n",
       "\n",
       "                         75%           max  \n",
       "Order                 2197.5        2930.0  \n",
       "PID              907181095.0  1007100110.0  \n",
       "MS SubClass             70.0         190.0  \n",
       "MS Zoning                NaN           NaN  \n",
       "Lot Frontage            80.0         313.0  \n",
       "Lot Area             11556.0      215245.0  \n",
       "Street                   NaN           NaN  \n",
       "Alley                    NaN           NaN  \n",
       "Lot Shape                NaN           NaN  \n",
       "Land Contour             NaN           NaN  \n",
       "Utilities                NaN           NaN  \n",
       "Lot Config               NaN           NaN  \n",
       "Land Slope               NaN           NaN  \n",
       "Neighborhood             NaN           NaN  \n",
       "Condition 1              NaN           NaN  \n",
       "Condition 2              NaN           NaN  \n",
       "Bldg Type                NaN           NaN  \n",
       "House Style              NaN           NaN  \n",
       "Overall Qual             7.0          10.0  \n",
       "Overall Cond             6.0           9.0  \n",
       "Year Built            2001.0        2010.0  \n",
       "Year Remod/Add        2004.0        2010.0  \n",
       "Roof Style               NaN           NaN  \n",
       "Roof Matl                NaN           NaN  \n",
       "Exterior 1st             NaN           NaN  \n",
       "Exterior 2nd             NaN           NaN  \n",
       "Mas Vnr Type             NaN           NaN  \n",
       "Mas Vnr Area           164.0        1600.0  \n",
       "Exter Qual               NaN           NaN  \n",
       "Exter Cond               NaN           NaN  \n",
       "Foundation               NaN           NaN  \n",
       "Bsmt Qual                NaN           NaN  \n",
       "Bsmt Cond                NaN           NaN  \n",
       "Bsmt Exposure            NaN           NaN  \n",
       "BsmtFin Type 1           NaN           NaN  \n",
       "BsmtFin SF 1           734.0        5644.0  \n",
       "BsmtFin Type 2           NaN           NaN  \n",
       "BsmtFin SF 2             0.0        1526.0  \n",
       "Bsmt Unf SF           801.75        2336.0  \n",
       "Total Bsmt SF         1301.5        6110.0  \n",
       "Heating                  NaN           NaN  \n",
       "Heating QC               NaN           NaN  \n",
       "Central Air              NaN           NaN  \n",
       "Electrical               NaN           NaN  \n",
       "1st Flr SF            1385.5        5095.0  \n",
       "2nd Flr SF             703.5        2065.0  \n",
       "Low Qual Fin SF          0.0        1064.0  \n",
       "Gr Liv Area           1742.5        5642.0  \n",
       "Bsmt Full Bath           1.0           3.0  \n",
       "Bsmt Half Bath           0.0           2.0  \n",
       "Full Bath                2.0           4.0  \n",
       "Half Bath                1.0           2.0  \n",
       "Bedroom AbvGr            3.0           8.0  \n",
       "Kitchen AbvGr            1.0           3.0  \n",
       "Kitchen Qual             NaN           NaN  \n",
       "TotRms AbvGrd            7.0          15.0  \n",
       "Functional               NaN           NaN  \n",
       "Fireplaces               1.0           4.0  \n",
       "Fireplace Qu             NaN           NaN  \n",
       "Garage Type              NaN           NaN  \n",
       "Garage Yr Blt         2002.0        2207.0  \n",
       "Garage Finish            NaN           NaN  \n",
       "Garage Cars              2.0           5.0  \n",
       "Garage Area            576.0        1488.0  \n",
       "Garage Qual              NaN           NaN  \n",
       "Garage Cond              NaN           NaN  \n",
       "Paved Drive              NaN           NaN  \n",
       "Wood Deck SF           168.0        1424.0  \n",
       "Open Porch SF           70.0         742.0  \n",
       "Enclosed Porch           0.0        1012.0  \n",
       "3Ssn Porch               0.0         508.0  \n",
       "Screen Porch             0.0         576.0  \n",
       "Pool Area                0.0         800.0  \n",
       "Pool QC                  NaN           NaN  \n",
       "Fence                    NaN           NaN  \n",
       "Misc Feature             NaN           NaN  \n",
       "Misc Val                 0.0       17000.0  \n",
       "Mo Sold                  8.0          12.0  \n",
       "Yr Sold               2009.0        2010.0  \n",
       "Sale Type                NaN           NaN  \n",
       "Sale Condition           NaN           NaN  \n",
       "SalePrice           213500.0      755000.0  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 200)\n",
    "df.describe(include='all').T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "missing_vals = df.isna().sum().sort_values() / len(df)\n",
    "missing_vals = missing_vals[missing_vals > 0]   #filter\n",
    "# missing_vals.shape\n",
    "\n",
    "#divide missing columns into two groups\n",
    "missing_vals_first = missing_vals.head(len(missing_vals)//2 + 1)\n",
    "missing_vals_last = missing_vals.tail(len(missing_vals)//2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Electrical        0.000341\n",
       "BsmtFin SF 1      0.000341\n",
       "BsmtFin SF 2      0.000341\n",
       "Bsmt Unf SF       0.000341\n",
       "Total Bsmt SF     0.000341\n",
       "Garage Cars       0.000341\n",
       "Garage Area       0.000341\n",
       "Bsmt Half Bath    0.000682\n",
       "Bsmt Full Bath    0.000682\n",
       "Mas Vnr Area      0.007847\n",
       "Mas Vnr Type      0.007847\n",
       "Bsmt Qual         0.027294\n",
       "Bsmt Cond         0.027294\n",
       "BsmtFin Type 1    0.027294\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_vals_first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20, 8))\n",
    "plt.title('Missing Value Percentage', fontdict=dict(fontsize=30))\n",
    "missing_vals_first.plot(kind = 'bar', color='xkcd:hunter green')\n",
    "plt.grid(True, which='both', linestyle='--')\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('Missing Percentage')\n",
    "\n",
    "for col, data in enumerate(missing_vals_first):\n",
    "    plt.text(x=col - 0.2, y = data + 0.01, s=f\"{data * 100:.{2}f}%\" , fontdict=dict(fontsize=15))\n",
    "\n",
    "plt.figure(figsize=(20, 8))\n",
    "missing_vals_last.plot(kind = 'bar', color='xkcd:hunter green')\n",
    "plt.grid(True, which='both', linestyle='--')\n",
    "for col, data in enumerate(missing_vals_last):\n",
    "    plt.text(x=col - 0.2, y = data + 0.01, s=f\"{data * 100:.{2}f}%\" , fontdict=dict(fontsize=15))\n",
    "plt.xlabel('Features')\n",
    "plt.ylabel('CountMissing')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fence           0.804845\n",
       "Alley           0.932446\n",
       "Misc Feature    0.963835\n",
       "Pool QC         0.995565\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing_vals_last[missing_vals_last > 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>Order</th>\n",
       "      <th>PID</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Lot Frontage</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Street</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>...</th>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>526301100</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>141.0</td>\n",
       "      <td>31770</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>526350040</td>\n",
       "      <td>20</td>\n",
       "      <td>RH</td>\n",
       "      <td>80.0</td>\n",
       "      <td>11622</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>120</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>105000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>526351010</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>81.0</td>\n",
       "      <td>14267</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12500</td>\n",
       "      <td>6</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>172000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>526353030</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>93.0</td>\n",
       "      <td>11160</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>244000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 78 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Order        PID  MS SubClass MS Zoning  Lot Frontage  Lot Area Street  \\\n",
       "0      1  526301100           20        RL         141.0     31770   Pave   \n",
       "1      1  526301100           20        RL         141.0     31770   Pave   \n",
       "2      2  526350040           20        RH          80.0     11622   Pave   \n",
       "3      3  526351010           20        RL          81.0     14267   Pave   \n",
       "4      4  526353030           20        RL          93.0     11160   Pave   \n",
       "\n",
       "  Lot Shape Land Contour Utilities  ... Enclosed Porch 3Ssn Porch  \\\n",
       "0       IR1          Lvl    AllPub  ...              0          0   \n",
       "1       IR1          Lvl    AllPub  ...              0          0   \n",
       "2       Reg          Lvl    AllPub  ...              0          0   \n",
       "3       IR1          Lvl    AllPub  ...              0          0   \n",
       "4       Reg          Lvl    AllPub  ...              0          0   \n",
       "\n",
       "  Screen Porch Pool Area Misc Val Mo Sold Yr Sold  Sale Type  Sale Condition  \\\n",
       "0            0         0        0       5    2010        WD           Normal   \n",
       "1            0         0        0       5    2010        WD           Normal   \n",
       "2          120         0        0       6    2010        WD           Normal   \n",
       "3            0         0    12500       6    2010        WD           Normal   \n",
       "4            0         0        0       4    2010        WD           Normal   \n",
       "\n",
       "   SalePrice  \n",
       "0     215000  \n",
       "1     215000  \n",
       "2     105000  \n",
       "3     172000  \n",
       "4     244000  \n",
       "\n",
       "[5 rows x 78 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(['Fence', 'Alley', 'Misc Feature', 'Pool QC'], axis=1, inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2930, 78)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#remove duplicate\n",
    "df = df.drop_duplicates()\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Encode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df.columns:\n",
    "    if df[col].dtype == 'O':\n",
    "        series = df[col]\n",
    "        label_encoder = LabelEncoder()\n",
    "        df[col] = pd.Series(label_encoder.fit_transform(series[series.notnull()]), index=series[series.notnull()].index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Lot Frontage       490\n",
       "Mas Vnr Type        23\n",
       "Mas Vnr Area        23\n",
       "Bsmt Qual           80\n",
       "Bsmt Cond           80\n",
       "Bsmt Exposure       83\n",
       "BsmtFin Type 1      80\n",
       "BsmtFin SF 1         1\n",
       "BsmtFin Type 2      81\n",
       "BsmtFin SF 2         1\n",
       "Bsmt Unf SF          1\n",
       "Total Bsmt SF        1\n",
       "Electrical           1\n",
       "Bsmt Full Bath       2\n",
       "Bsmt Half Bath       2\n",
       "Fireplace Qu      1422\n",
       "Garage Type        157\n",
       "Garage Yr Blt      159\n",
       "Garage Finish      159\n",
       "Garage Cars          1\n",
       "Garage Area          1\n",
       "Garage Qual        159\n",
       "Garage Cond        159\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "missing = df.isna().sum()\n",
    "missing[missing>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#import libraries\n",
    "from sklearn.impute import KNNImputer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Order              0\n",
       "PID                0\n",
       "MS SubClass        0\n",
       "MS Zoning          0\n",
       "Lot Frontage       0\n",
       "Lot Area           0\n",
       "Street             0\n",
       "Lot Shape          0\n",
       "Land Contour       0\n",
       "Utilities          0\n",
       "Lot Config         0\n",
       "Land Slope         0\n",
       "Neighborhood       0\n",
       "Condition 1        0\n",
       "Condition 2        0\n",
       "Bldg Type          0\n",
       "House Style        0\n",
       "Overall Qual       0\n",
       "Overall Cond       0\n",
       "Year Built         0\n",
       "Year Remod/Add     0\n",
       "Roof Style         0\n",
       "Roof Matl          0\n",
       "Exterior 1st       0\n",
       "Exterior 2nd       0\n",
       "Mas Vnr Type       0\n",
       "Mas Vnr Area       0\n",
       "Exter Qual         0\n",
       "Exter Cond         0\n",
       "Foundation         0\n",
       "Bsmt Qual          0\n",
       "Bsmt Cond          0\n",
       "Bsmt Exposure      0\n",
       "BsmtFin Type 1     0\n",
       "BsmtFin SF 1       0\n",
       "BsmtFin Type 2     0\n",
       "BsmtFin SF 2       0\n",
       "Bsmt Unf SF        0\n",
       "Total Bsmt SF      0\n",
       "Heating            0\n",
       "Heating QC         0\n",
       "Central Air        0\n",
       "Electrical         0\n",
       "1st Flr SF         0\n",
       "2nd Flr SF         0\n",
       "Low Qual Fin SF    0\n",
       "Gr Liv Area        0\n",
       "Bsmt Full Bath     0\n",
       "Bsmt Half Bath     0\n",
       "Full Bath          0\n",
       "Half Bath          0\n",
       "Bedroom AbvGr      0\n",
       "Kitchen AbvGr      0\n",
       "Kitchen Qual       0\n",
       "TotRms AbvGrd      0\n",
       "Functional         0\n",
       "Fireplaces         0\n",
       "Fireplace Qu       0\n",
       "Garage Type        0\n",
       "Garage Yr Blt      0\n",
       "Garage Finish      0\n",
       "Garage Cars        0\n",
       "Garage Area        0\n",
       "Garage Qual        0\n",
       "Garage Cond        0\n",
       "Paved Drive        0\n",
       "Wood Deck SF       0\n",
       "Open Porch SF      0\n",
       "Enclosed Porch     0\n",
       "3Ssn Porch         0\n",
       "Screen Porch       0\n",
       "Pool Area          0\n",
       "Misc Val           0\n",
       "Mo Sold            0\n",
       "Yr Sold            0\n",
       "Sale Type          0\n",
       "Sale Condition     0\n",
       "SalePrice          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "impute = KNNImputer(n_neighbors=10, weights='distance')\n",
    "df_imputed = pd.DataFrame(impute.fit_transform(df), columns=df.columns)\n",
    "df_imputed.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "for col in df_imputed.columns:\n",
    "    df_imputed[col] = df_imputed[col].astype('float32')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scaling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Min-Max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0000000e+00, 0.0000000e+00, 0.0000000e+00, ..., 1.0000000e+00,\n",
       "        8.0000001e-01, 2.7244410e-01],\n",
       "       [3.4141346e-04, 1.0180473e-04, 0.0000000e+00, ..., 1.0000000e+00,\n",
       "        8.0000001e-01, 1.2423826e-01],\n",
       "       [6.8282697e-04, 1.0383129e-04, 0.0000000e+00, ..., 1.0000000e+00,\n",
       "        8.0000001e-01, 2.1450910e-01],\n",
       "       ...,\n",
       "       [9.9931717e-01, 8.2591486e-01, 3.8235295e-01, ..., 1.0000000e+00,\n",
       "        8.0000001e-01, 1.6061604e-01],\n",
       "       [9.9965858e-01, 8.2737064e-01, 0.0000000e+00, ..., 1.0000000e+00,\n",
       "        8.0000001e-01, 2.1181443e-01],\n",
       "       [1.0000000e+00, 8.2747662e-01, 2.3529413e-01, ..., 1.0000000e+00,\n",
       "        8.0000001e-01, 2.3606628e-01]], dtype=float32)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "min_max = MinMaxScaler().fit_transform(df_imputed)\n",
    "min_max"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Standar Scaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.7314597 , -0.9971636 , -0.8770048 , ...,  0.34164014,\n",
       "         0.20456588,  0.42822877],\n",
       "       [-1.7302774 , -0.99690413, -0.8770048 , ...,  0.34164014,\n",
       "         0.20456588, -0.94895655],\n",
       "       [-1.7290952 , -0.99689907, -0.8770048 , ...,  0.34164014,\n",
       "         0.20456588, -0.1101255 ],\n",
       "       ...,\n",
       "       [ 1.7290952 ,  1.1072451 ,  0.6477163 , ...,  0.34164014,\n",
       "         0.20456588, -0.61092013],\n",
       "       [ 1.7302774 ,  1.1109545 , -0.8770048 , ...,  0.34164014,\n",
       "         0.20456588, -0.13516523],\n",
       "       [ 1.7314597 ,  1.1112245 ,  0.06128506, ...,  0.34164014,\n",
       "         0.20456588,  0.09019236]], dtype=float32)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "std_scale = StandardScaler().fit_transform(df_imputed)\n",
    "std_scale"
   ]
  }
 ],
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  "kernelspec": {
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