MacPaw.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pickle\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.base import BaseEstimator\n",
    "from sklearn.base import TransformerMixin\n",
    "from sklearn.preprocessing import OneHotEncoder\n",
    "from sklearn.preprocessing import OrdinalEncoder\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.manifold import TSNE\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from sklearn.linear_model import LinearRegression \n",
    "from sklearn.linear_model import SGDRegressor\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.ensemble import VotingRegressor\n",
    "from sklearn.ensemble import GradientBoostingRegressor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\", None)\n",
    "pd.set_option(\"max_colwidth\", None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "DATA_FOLDER = \"./msi2021-data-science/data/\"\n",
    "\n",
    "train_path = os.path.join(DATA_FOLDER, \"train.csv\")\n",
    "test_path = os.path.join(DATA_FOLDER, \"test.csv\")\n",
    "\n",
    "data = pd.read_csv(train_path)\n",
    "X_final = pd.read_csv(test_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>Alley</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>FireplaceQu</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>PoolQC</th>\n",
       "      <th>Fence</th>\n",
       "      <th>MiscFeature</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>8450</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2003</td>\n",
       "      <td>2003</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>196.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>706</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>150</td>\n",
       "      <td>856</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>856</td>\n",
       "      <td>854</td>\n",
       "      <td>0</td>\n",
       "      <td>1710</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>548</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>61</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>208500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>80.0</td>\n",
       "      <td>9600</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Veenker</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>1976</td>\n",
       "      <td>1976</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>978</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>284</td>\n",
       "      <td>1262</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1262</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>298</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>181500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>11250</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>CollgCr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2001</td>\n",
       "      <td>2002</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>162.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>486</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>434</td>\n",
       "      <td>920</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>920</td>\n",
       "      <td>866</td>\n",
       "      <td>0</td>\n",
       "      <td>1786</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2001.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>608</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>223500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>60.0</td>\n",
       "      <td>9550</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>1915</td>\n",
       "      <td>1970</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Shng</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkTil</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>216</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>540</td>\n",
       "      <td>756</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>961</td>\n",
       "      <td>756</td>\n",
       "      <td>0</td>\n",
       "      <td>1717</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Detchd</td>\n",
       "      <td>1998.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>3</td>\n",
       "      <td>642</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>35</td>\n",
       "      <td>272</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2006</td>\n",
       "      <td>WD</td>\n",
       "      <td>Abnorml</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>84.0</td>\n",
       "      <td>14260</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>FR2</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NoRidge</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>350.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Av</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>655</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>490</td>\n",
       "      <td>1145</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1145</td>\n",
       "      <td>1053</td>\n",
       "      <td>0</td>\n",
       "      <td>2198</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2000.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>3</td>\n",
       "      <td>836</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>192</td>\n",
       "      <td>84</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>1456</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>62.0</td>\n",
       "      <td>7917</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1999</td>\n",
       "      <td>2000</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>953</td>\n",
       "      <td>953</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>953</td>\n",
       "      <td>694</td>\n",
       "      <td>0</td>\n",
       "      <td>1647</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>7</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1999.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>460</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>2007</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>1457</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>85.0</td>\n",
       "      <td>13175</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NWAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>1978</td>\n",
       "      <td>1988</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Plywood</td>\n",
       "      <td>Plywood</td>\n",
       "      <td>Stone</td>\n",
       "      <td>119.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>790</td>\n",
       "      <td>Rec</td>\n",
       "      <td>163</td>\n",
       "      <td>589</td>\n",
       "      <td>1542</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>2073</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2073</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>7</td>\n",
       "      <td>Min1</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1978.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2</td>\n",
       "      <td>500</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>349</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>210000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1457</th>\n",
       "      <td>1458</td>\n",
       "      <td>70</td>\n",
       "      <td>RL</td>\n",
       "      <td>66.0</td>\n",
       "      <td>9042</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Crawfor</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>1941</td>\n",
       "      <td>2006</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>CemntBd</td>\n",
       "      <td>CmentBd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Stone</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>275</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>877</td>\n",
       "      <td>1152</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1188</td>\n",
       "      <td>1152</td>\n",
       "      <td>0</td>\n",
       "      <td>2340</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>2</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1941.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>1</td>\n",
       "      <td>252</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GdPrv</td>\n",
       "      <td>Shed</td>\n",
       "      <td>2500</td>\n",
       "      <td>5</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>266500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>1459</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>9717</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1950</td>\n",
       "      <td>1996</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>49</td>\n",
       "      <td>Rec</td>\n",
       "      <td>1029</td>\n",
       "      <td>0</td>\n",
       "      <td>1078</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>FuseA</td>\n",
       "      <td>1078</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1078</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>5</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1950.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>1</td>\n",
       "      <td>240</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>366</td>\n",
       "      <td>0</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>142125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1459</th>\n",
       "      <td>1460</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>75.0</td>\n",
       "      <td>9937</td>\n",
       "      <td>Pave</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Edwards</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>1965</td>\n",
       "      <td>1965</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>BLQ</td>\n",
       "      <td>830</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>290</td>\n",
       "      <td>136</td>\n",
       "      <td>1256</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1256</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1256</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1965.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>1</td>\n",
       "      <td>276</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>736</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>147500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1460 rows × 81 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street Alley LotShape  \\\n",
       "0        1          60       RL         65.0     8450   Pave   NaN      Reg   \n",
       "1        2          20       RL         80.0     9600   Pave   NaN      Reg   \n",
       "2        3          60       RL         68.0    11250   Pave   NaN      IR1   \n",
       "3        4          70       RL         60.0     9550   Pave   NaN      IR1   \n",
       "4        5          60       RL         84.0    14260   Pave   NaN      IR1   \n",
       "...    ...         ...      ...          ...      ...    ...   ...      ...   \n",
       "1455  1456          60       RL         62.0     7917   Pave   NaN      Reg   \n",
       "1456  1457          20       RL         85.0    13175   Pave   NaN      Reg   \n",
       "1457  1458          70       RL         66.0     9042   Pave   NaN      Reg   \n",
       "1458  1459          20       RL         68.0     9717   Pave   NaN      Reg   \n",
       "1459  1460          20       RL         75.0     9937   Pave   NaN      Reg   \n",
       "\n",
       "     LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "0            Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "1            Lvl    AllPub       FR2       Gtl      Veenker      Feedr   \n",
       "2            Lvl    AllPub    Inside       Gtl      CollgCr       Norm   \n",
       "3            Lvl    AllPub    Corner       Gtl      Crawfor       Norm   \n",
       "4            Lvl    AllPub       FR2       Gtl      NoRidge       Norm   \n",
       "...          ...       ...       ...       ...          ...        ...   \n",
       "1455         Lvl    AllPub    Inside       Gtl      Gilbert       Norm   \n",
       "1456         Lvl    AllPub    Inside       Gtl       NWAmes       Norm   \n",
       "1457         Lvl    AllPub    Inside       Gtl      Crawfor       Norm   \n",
       "1458         Lvl    AllPub    Inside       Gtl        NAmes       Norm   \n",
       "1459         Lvl    AllPub    Inside       Gtl      Edwards       Norm   \n",
       "\n",
       "     Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "0          Norm     1Fam     2Story            7            5       2003   \n",
       "1          Norm     1Fam     1Story            6            8       1976   \n",
       "2          Norm     1Fam     2Story            7            5       2001   \n",
       "3          Norm     1Fam     2Story            7            5       1915   \n",
       "4          Norm     1Fam     2Story            8            5       2000   \n",
       "...         ...      ...        ...          ...          ...        ...   \n",
       "1455       Norm     1Fam     2Story            6            5       1999   \n",
       "1456       Norm     1Fam     1Story            6            6       1978   \n",
       "1457       Norm     1Fam     2Story            7            9       1941   \n",
       "1458       Norm     1Fam     1Story            5            6       1950   \n",
       "1459       Norm     1Fam     1Story            5            6       1965   \n",
       "\n",
       "      YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "0             2003     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "1             1976     Gable  CompShg     MetalSd     MetalSd       None   \n",
       "2             2002     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "3             1970     Gable  CompShg     Wd Sdng     Wd Shng       None   \n",
       "4             2000     Gable  CompShg     VinylSd     VinylSd    BrkFace   \n",
       "...            ...       ...      ...         ...         ...        ...   \n",
       "1455          2000     Gable  CompShg     VinylSd     VinylSd       None   \n",
       "1456          1988     Gable  CompShg     Plywood     Plywood      Stone   \n",
       "1457          2006     Gable  CompShg     CemntBd     CmentBd       None   \n",
       "1458          1996       Hip  CompShg     MetalSd     MetalSd       None   \n",
       "1459          1965     Gable  CompShg     HdBoard     HdBoard       None   \n",
       "\n",
       "      MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond  \\\n",
       "0          196.0        Gd        TA      PConc       Gd       TA   \n",
       "1            0.0        TA        TA     CBlock       Gd       TA   \n",
       "2          162.0        Gd        TA      PConc       Gd       TA   \n",
       "3            0.0        TA        TA     BrkTil       TA       Gd   \n",
       "4          350.0        Gd        TA      PConc       Gd       TA   \n",
       "...          ...       ...       ...        ...      ...      ...   \n",
       "1455         0.0        TA        TA      PConc       Gd       TA   \n",
       "1456       119.0        TA        TA     CBlock       Gd       TA   \n",
       "1457         0.0        Ex        Gd      Stone       TA       Gd   \n",
       "1458         0.0        TA        TA     CBlock       TA       TA   \n",
       "1459         0.0        Gd        TA     CBlock       TA       TA   \n",
       "\n",
       "     BsmtExposure BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  \\\n",
       "0              No          GLQ         706          Unf           0   \n",
       "1              Gd          ALQ         978          Unf           0   \n",
       "2              Mn          GLQ         486          Unf           0   \n",
       "3              No          ALQ         216          Unf           0   \n",
       "4              Av          GLQ         655          Unf           0   \n",
       "...           ...          ...         ...          ...         ...   \n",
       "1455           No          Unf           0          Unf           0   \n",
       "1456           No          ALQ         790          Rec         163   \n",
       "1457           No          GLQ         275          Unf           0   \n",
       "1458           Mn          GLQ          49          Rec        1029   \n",
       "1459           No          BLQ         830          LwQ         290   \n",
       "\n",
       "      BsmtUnfSF  TotalBsmtSF Heating HeatingQC CentralAir Electrical  \\\n",
       "0           150          856    GasA        Ex          Y      SBrkr   \n",
       "1           284         1262    GasA        Ex          Y      SBrkr   \n",
       "2           434          920    GasA        Ex          Y      SBrkr   \n",
       "3           540          756    GasA        Gd          Y      SBrkr   \n",
       "4           490         1145    GasA        Ex          Y      SBrkr   \n",
       "...         ...          ...     ...       ...        ...        ...   \n",
       "1455        953          953    GasA        Ex          Y      SBrkr   \n",
       "1456        589         1542    GasA        TA          Y      SBrkr   \n",
       "1457        877         1152    GasA        Ex          Y      SBrkr   \n",
       "1458          0         1078    GasA        Gd          Y      FuseA   \n",
       "1459        136         1256    GasA        Gd          Y      SBrkr   \n",
       "\n",
       "      1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  \\\n",
       "0          856       854             0       1710             1             0   \n",
       "1         1262         0             0       1262             0             1   \n",
       "2          920       866             0       1786             1             0   \n",
       "3          961       756             0       1717             1             0   \n",
       "4         1145      1053             0       2198             1             0   \n",
       "...        ...       ...           ...        ...           ...           ...   \n",
       "1455       953       694             0       1647             0             0   \n",
       "1456      2073         0             0       2073             1             0   \n",
       "1457      1188      1152             0       2340             0             0   \n",
       "1458      1078         0             0       1078             1             0   \n",
       "1459      1256         0             0       1256             1             0   \n",
       "\n",
       "      FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr KitchenQual  \\\n",
       "0            2         1             3             1          Gd   \n",
       "1            2         0             3             1          TA   \n",
       "2            2         1             3             1          Gd   \n",
       "3            1         0             3             1          Gd   \n",
       "4            2         1             4             1          Gd   \n",
       "...        ...       ...           ...           ...         ...   \n",
       "1455         2         1             3             1          TA   \n",
       "1456         2         0             3             1          TA   \n",
       "1457         2         0             4             1          Gd   \n",
       "1458         1         0             2             1          Gd   \n",
       "1459         1         1             3             1          TA   \n",
       "\n",
       "      TotRmsAbvGrd Functional  Fireplaces FireplaceQu GarageType  GarageYrBlt  \\\n",
       "0                8        Typ           0         NaN     Attchd       2003.0   \n",
       "1                6        Typ           1          TA     Attchd       1976.0   \n",
       "2                6        Typ           1          TA     Attchd       2001.0   \n",
       "3                7        Typ           1          Gd     Detchd       1998.0   \n",
       "4                9        Typ           1          TA     Attchd       2000.0   \n",
       "...            ...        ...         ...         ...        ...          ...   \n",
       "1455             7        Typ           1          TA     Attchd       1999.0   \n",
       "1456             7       Min1           2          TA     Attchd       1978.0   \n",
       "1457             9        Typ           2          Gd     Attchd       1941.0   \n",
       "1458             5        Typ           0         NaN     Attchd       1950.0   \n",
       "1459             6        Typ           0         NaN     Attchd       1965.0   \n",
       "\n",
       "     GarageFinish  GarageCars  GarageArea GarageQual GarageCond PavedDrive  \\\n",
       "0             RFn           2         548         TA         TA          Y   \n",
       "1             RFn           2         460         TA         TA          Y   \n",
       "2             RFn           2         608         TA         TA          Y   \n",
       "3             Unf           3         642         TA         TA          Y   \n",
       "4             RFn           3         836         TA         TA          Y   \n",
       "...           ...         ...         ...        ...        ...        ...   \n",
       "1455          RFn           2         460         TA         TA          Y   \n",
       "1456          Unf           2         500         TA         TA          Y   \n",
       "1457          RFn           1         252         TA         TA          Y   \n",
       "1458          Unf           1         240         TA         TA          Y   \n",
       "1459          Fin           1         276         TA         TA          Y   \n",
       "\n",
       "      WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "0              0           61              0          0            0   \n",
       "1            298            0              0          0            0   \n",
       "2              0           42              0          0            0   \n",
       "3              0           35            272          0            0   \n",
       "4            192           84              0          0            0   \n",
       "...          ...          ...            ...        ...          ...   \n",
       "1455           0           40              0          0            0   \n",
       "1456         349            0              0          0            0   \n",
       "1457           0           60              0          0            0   \n",
       "1458         366            0            112          0            0   \n",
       "1459         736           68              0          0            0   \n",
       "\n",
       "      PoolArea PoolQC  Fence MiscFeature  MiscVal  MoSold  YrSold SaleType  \\\n",
       "0            0    NaN    NaN         NaN        0       2    2008       WD   \n",
       "1            0    NaN    NaN         NaN        0       5    2007       WD   \n",
       "2            0    NaN    NaN         NaN        0       9    2008       WD   \n",
       "3            0    NaN    NaN         NaN        0       2    2006       WD   \n",
       "4            0    NaN    NaN         NaN        0      12    2008       WD   \n",
       "...        ...    ...    ...         ...      ...     ...     ...      ...   \n",
       "1455         0    NaN    NaN         NaN        0       8    2007       WD   \n",
       "1456         0    NaN  MnPrv         NaN        0       2    2010       WD   \n",
       "1457         0    NaN  GdPrv        Shed     2500       5    2010       WD   \n",
       "1458         0    NaN    NaN         NaN        0       4    2010       WD   \n",
       "1459         0    NaN    NaN         NaN        0       6    2008       WD   \n",
       "\n",
       "     SaleCondition  SalePrice  \n",
       "0           Normal     208500  \n",
       "1           Normal     181500  \n",
       "2           Normal     223500  \n",
       "3          Abnorml     140000  \n",
       "4           Normal     250000  \n",
       "...            ...        ...  \n",
       "1455        Normal     175000  \n",
       "1456        Normal     210000  \n",
       "1457        Normal     266500  \n",
       "1458        Normal     142125  \n",
       "1459        Normal     147500  \n",
       "\n",
       "[1460 rows x 81 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1460 entries, 0 to 1459\n",
      "Data columns (total 81 columns):\n",
      " #   Column         Non-Null Count  Dtype  \n",
      "---  ------         --------------  -----  \n",
      " 0   Id             1460 non-null   int64  \n",
      " 1   MSSubClass     1460 non-null   int64  \n",
      " 2   MSZoning       1460 non-null   object \n",
      " 3   LotFrontage    1201 non-null   float64\n",
      " 4   LotArea        1460 non-null   int64  \n",
      " 5   Street         1460 non-null   object \n",
      " 6   Alley          91 non-null     object \n",
      " 7   LotShape       1460 non-null   object \n",
      " 8   LandContour    1460 non-null   object \n",
      " 9   Utilities      1460 non-null   object \n",
      " 10  LotConfig      1460 non-null   object \n",
      " 11  LandSlope      1460 non-null   object \n",
      " 12  Neighborhood   1460 non-null   object \n",
      " 13  Condition1     1460 non-null   object \n",
      " 14  Condition2     1460 non-null   object \n",
      " 15  BldgType       1460 non-null   object \n",
      " 16  HouseStyle     1460 non-null   object \n",
      " 17  OverallQual    1460 non-null   int64  \n",
      " 18  OverallCond    1460 non-null   int64  \n",
      " 19  YearBuilt      1460 non-null   int64  \n",
      " 20  YearRemodAdd   1460 non-null   int64  \n",
      " 21  RoofStyle      1460 non-null   object \n",
      " 22  RoofMatl       1460 non-null   object \n",
      " 23  Exterior1st    1460 non-null   object \n",
      " 24  Exterior2nd    1460 non-null   object \n",
      " 25  MasVnrType     1452 non-null   object \n",
      " 26  MasVnrArea     1452 non-null   float64\n",
      " 27  ExterQual      1460 non-null   object \n",
      " 28  ExterCond      1460 non-null   object \n",
      " 29  Foundation     1460 non-null   object \n",
      " 30  BsmtQual       1423 non-null   object \n",
      " 31  BsmtCond       1423 non-null   object \n",
      " 32  BsmtExposure   1422 non-null   object \n",
      " 33  BsmtFinType1   1423 non-null   object \n",
      " 34  BsmtFinSF1     1460 non-null   int64  \n",
      " 35  BsmtFinType2   1422 non-null   object \n",
      " 36  BsmtFinSF2     1460 non-null   int64  \n",
      " 37  BsmtUnfSF      1460 non-null   int64  \n",
      " 38  TotalBsmtSF    1460 non-null   int64  \n",
      " 39  Heating        1460 non-null   object \n",
      " 40  HeatingQC      1460 non-null   object \n",
      " 41  CentralAir     1460 non-null   object \n",
      " 42  Electrical     1459 non-null   object \n",
      " 43  1stFlrSF       1460 non-null   int64  \n",
      " 44  2ndFlrSF       1460 non-null   int64  \n",
      " 45  LowQualFinSF   1460 non-null   int64  \n",
      " 46  GrLivArea      1460 non-null   int64  \n",
      " 47  BsmtFullBath   1460 non-null   int64  \n",
      " 48  BsmtHalfBath   1460 non-null   int64  \n",
      " 49  FullBath       1460 non-null   int64  \n",
      " 50  HalfBath       1460 non-null   int64  \n",
      " 51  BedroomAbvGr   1460 non-null   int64  \n",
      " 52  KitchenAbvGr   1460 non-null   int64  \n",
      " 53  KitchenQual    1460 non-null   object \n",
      " 54  TotRmsAbvGrd   1460 non-null   int64  \n",
      " 55  Functional     1460 non-null   object \n",
      " 56  Fireplaces     1460 non-null   int64  \n",
      " 57  FireplaceQu    770 non-null    object \n",
      " 58  GarageType     1379 non-null   object \n",
      " 59  GarageYrBlt    1379 non-null   float64\n",
      " 60  GarageFinish   1379 non-null   object \n",
      " 61  GarageCars     1460 non-null   int64  \n",
      " 62  GarageArea     1460 non-null   int64  \n",
      " 63  GarageQual     1379 non-null   object \n",
      " 64  GarageCond     1379 non-null   object \n",
      " 65  PavedDrive     1460 non-null   object \n",
      " 66  WoodDeckSF     1460 non-null   int64  \n",
      " 67  OpenPorchSF    1460 non-null   int64  \n",
      " 68  EnclosedPorch  1460 non-null   int64  \n",
      " 69  3SsnPorch      1460 non-null   int64  \n",
      " 70  ScreenPorch    1460 non-null   int64  \n",
      " 71  PoolArea       1460 non-null   int64  \n",
      " 72  PoolQC         7 non-null      object \n",
      " 73  Fence          281 non-null    object \n",
      " 74  MiscFeature    54 non-null     object \n",
      " 75  MiscVal        1460 non-null   int64  \n",
      " 76  MoSold         1460 non-null   int64  \n",
      " 77  YrSold         1460 non-null   int64  \n",
      " 78  SaleType       1460 non-null   object \n",
      " 79  SaleCondition  1460 non-null   object \n",
      " 80  SalePrice      1460 non-null   int64  \n",
      "dtypes: float64(3), int64(35), object(43)\n",
      "memory usage: 924.0+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "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",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1201.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1452.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1379.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "      <td>1460.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>56.897260</td>\n",
       "      <td>70.049958</td>\n",
       "      <td>10516.828082</td>\n",
       "      <td>6.099315</td>\n",
       "      <td>5.575342</td>\n",
       "      <td>1971.267808</td>\n",
       "      <td>1984.865753</td>\n",
       "      <td>103.685262</td>\n",
       "      <td>443.639726</td>\n",
       "      <td>46.549315</td>\n",
       "      <td>567.240411</td>\n",
       "      <td>1057.429452</td>\n",
       "      <td>1162.626712</td>\n",
       "      <td>346.992466</td>\n",
       "      <td>5.844521</td>\n",
       "      <td>1515.463699</td>\n",
       "      <td>0.425342</td>\n",
       "      <td>0.057534</td>\n",
       "      <td>1.565068</td>\n",
       "      <td>0.382877</td>\n",
       "      <td>2.866438</td>\n",
       "      <td>1.046575</td>\n",
       "      <td>6.517808</td>\n",
       "      <td>0.613014</td>\n",
       "      <td>1978.506164</td>\n",
       "      <td>1.767123</td>\n",
       "      <td>472.980137</td>\n",
       "      <td>94.244521</td>\n",
       "      <td>46.660274</td>\n",
       "      <td>21.954110</td>\n",
       "      <td>3.409589</td>\n",
       "      <td>15.060959</td>\n",
       "      <td>2.758904</td>\n",
       "      <td>43.489041</td>\n",
       "      <td>6.321918</td>\n",
       "      <td>2007.815753</td>\n",
       "      <td>180921.195890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>421.610009</td>\n",
       "      <td>42.300571</td>\n",
       "      <td>24.284752</td>\n",
       "      <td>9981.264932</td>\n",
       "      <td>1.382997</td>\n",
       "      <td>1.112799</td>\n",
       "      <td>30.202904</td>\n",
       "      <td>20.645407</td>\n",
       "      <td>181.066207</td>\n",
       "      <td>456.098091</td>\n",
       "      <td>161.319273</td>\n",
       "      <td>441.866955</td>\n",
       "      <td>438.705324</td>\n",
       "      <td>386.587738</td>\n",
       "      <td>436.528436</td>\n",
       "      <td>48.623081</td>\n",
       "      <td>525.480383</td>\n",
       "      <td>0.518911</td>\n",
       "      <td>0.238753</td>\n",
       "      <td>0.550916</td>\n",
       "      <td>0.502885</td>\n",
       "      <td>0.815778</td>\n",
       "      <td>0.220338</td>\n",
       "      <td>1.625393</td>\n",
       "      <td>0.644666</td>\n",
       "      <td>24.689725</td>\n",
       "      <td>0.747315</td>\n",
       "      <td>213.804841</td>\n",
       "      <td>125.338794</td>\n",
       "      <td>66.256028</td>\n",
       "      <td>61.119149</td>\n",
       "      <td>29.317331</td>\n",
       "      <td>55.757415</td>\n",
       "      <td>40.177307</td>\n",
       "      <td>496.123024</td>\n",
       "      <td>2.703626</td>\n",
       "      <td>1.328095</td>\n",
       "      <td>79442.502883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>1300.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1872.000000</td>\n",
       "      <td>1950.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>334.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1900.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2006.000000</td>\n",
       "      <td>34900.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>365.750000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7553.500000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1954.000000</td>\n",
       "      <td>1967.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>223.000000</td>\n",
       "      <td>795.750000</td>\n",
       "      <td>882.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1129.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1961.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>334.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2007.000000</td>\n",
       "      <td>129975.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>730.500000</td>\n",
       "      <td>50.000000</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>9478.500000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1973.000000</td>\n",
       "      <td>1994.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>383.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>477.500000</td>\n",
       "      <td>991.500000</td>\n",
       "      <td>1087.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1464.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2008.000000</td>\n",
       "      <td>163000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1095.250000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>11601.500000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>2000.000000</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>166.000000</td>\n",
       "      <td>712.250000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>808.000000</td>\n",
       "      <td>1298.250000</td>\n",
       "      <td>1391.250000</td>\n",
       "      <td>728.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1776.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>576.000000</td>\n",
       "      <td>168.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>214000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1460.000000</td>\n",
       "      <td>190.000000</td>\n",
       "      <td>313.000000</td>\n",
       "      <td>215245.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>1600.000000</td>\n",
       "      <td>5644.000000</td>\n",
       "      <td>1474.000000</td>\n",
       "      <td>2336.000000</td>\n",
       "      <td>6110.000000</td>\n",
       "      <td>4692.000000</td>\n",
       "      <td>2065.000000</td>\n",
       "      <td>572.000000</td>\n",
       "      <td>5642.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1418.000000</td>\n",
       "      <td>857.000000</td>\n",
       "      <td>547.000000</td>\n",
       "      <td>552.000000</td>\n",
       "      <td>508.000000</td>\n",
       "      <td>480.000000</td>\n",
       "      <td>738.000000</td>\n",
       "      <td>15500.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>2010.000000</td>\n",
       "      <td>755000.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                Id   MSSubClass  LotFrontage        LotArea  OverallQual  \\\n",
       "count  1460.000000  1460.000000  1201.000000    1460.000000  1460.000000   \n",
       "mean    730.500000    56.897260    70.049958   10516.828082     6.099315   \n",
       "std     421.610009    42.300571    24.284752    9981.264932     1.382997   \n",
       "min       1.000000    20.000000    21.000000    1300.000000     1.000000   \n",
       "25%     365.750000    20.000000    59.000000    7553.500000     5.000000   \n",
       "50%     730.500000    50.000000    69.000000    9478.500000     6.000000   \n",
       "75%    1095.250000    70.000000    80.000000   11601.500000     7.000000   \n",
       "max    1460.000000   190.000000   313.000000  215245.000000    10.000000   \n",
       "\n",
       "       OverallCond    YearBuilt  YearRemodAdd   MasVnrArea   BsmtFinSF1  \\\n",
       "count  1460.000000  1460.000000   1460.000000  1452.000000  1460.000000   \n",
       "mean      5.575342  1971.267808   1984.865753   103.685262   443.639726   \n",
       "std       1.112799    30.202904     20.645407   181.066207   456.098091   \n",
       "min       1.000000  1872.000000   1950.000000     0.000000     0.000000   \n",
       "25%       5.000000  1954.000000   1967.000000     0.000000     0.000000   \n",
       "50%       5.000000  1973.000000   1994.000000     0.000000   383.500000   \n",
       "75%       6.000000  2000.000000   2004.000000   166.000000   712.250000   \n",
       "max       9.000000  2010.000000   2010.000000  1600.000000  5644.000000   \n",
       "\n",
       "        BsmtFinSF2    BsmtUnfSF  TotalBsmtSF     1stFlrSF     2ndFlrSF  \\\n",
       "count  1460.000000  1460.000000  1460.000000  1460.000000  1460.000000   \n",
       "mean     46.549315   567.240411  1057.429452  1162.626712   346.992466   \n",
       "std     161.319273   441.866955   438.705324   386.587738   436.528436   \n",
       "min       0.000000     0.000000     0.000000   334.000000     0.000000   \n",
       "25%       0.000000   223.000000   795.750000   882.000000     0.000000   \n",
       "50%       0.000000   477.500000   991.500000  1087.000000     0.000000   \n",
       "75%       0.000000   808.000000  1298.250000  1391.250000   728.000000   \n",
       "max    1474.000000  2336.000000  6110.000000  4692.000000  2065.000000   \n",
       "\n",
       "       LowQualFinSF    GrLivArea  BsmtFullBath  BsmtHalfBath     FullBath  \\\n",
       "count   1460.000000  1460.000000   1460.000000   1460.000000  1460.000000   \n",
       "mean       5.844521  1515.463699      0.425342      0.057534     1.565068   \n",
       "std       48.623081   525.480383      0.518911      0.238753     0.550916   \n",
       "min        0.000000   334.000000      0.000000      0.000000     0.000000   \n",
       "25%        0.000000  1129.500000      0.000000      0.000000     1.000000   \n",
       "50%        0.000000  1464.000000      0.000000      0.000000     2.000000   \n",
       "75%        0.000000  1776.750000      1.000000      0.000000     2.000000   \n",
       "max      572.000000  5642.000000      3.000000      2.000000     3.000000   \n",
       "\n",
       "          HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd   Fireplaces  \\\n",
       "count  1460.000000   1460.000000   1460.000000   1460.000000  1460.000000   \n",
       "mean      0.382877      2.866438      1.046575      6.517808     0.613014   \n",
       "std       0.502885      0.815778      0.220338      1.625393     0.644666   \n",
       "min       0.000000      0.000000      0.000000      2.000000     0.000000   \n",
       "25%       0.000000      2.000000      1.000000      5.000000     0.000000   \n",
       "50%       0.000000      3.000000      1.000000      6.000000     1.000000   \n",
       "75%       1.000000      3.000000      1.000000      7.000000     1.000000   \n",
       "max       2.000000      8.000000      3.000000     14.000000     3.000000   \n",
       "\n",
       "       GarageYrBlt   GarageCars   GarageArea   WoodDeckSF  OpenPorchSF  \\\n",
       "count  1379.000000  1460.000000  1460.000000  1460.000000  1460.000000   \n",
       "mean   1978.506164     1.767123   472.980137    94.244521    46.660274   \n",
       "std      24.689725     0.747315   213.804841   125.338794    66.256028   \n",
       "min    1900.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%    1961.000000     1.000000   334.500000     0.000000     0.000000   \n",
       "50%    1980.000000     2.000000   480.000000     0.000000    25.000000   \n",
       "75%    2002.000000     2.000000   576.000000   168.000000    68.000000   \n",
       "max    2010.000000     4.000000  1418.000000   857.000000   547.000000   \n",
       "\n",
       "       EnclosedPorch    3SsnPorch  ScreenPorch     PoolArea       MiscVal  \\\n",
       "count    1460.000000  1460.000000  1460.000000  1460.000000   1460.000000   \n",
       "mean       21.954110     3.409589    15.060959     2.758904     43.489041   \n",
       "std        61.119149    29.317331    55.757415    40.177307    496.123024   \n",
       "min         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "25%         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "50%         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "75%         0.000000     0.000000     0.000000     0.000000      0.000000   \n",
       "max       552.000000   508.000000   480.000000   738.000000  15500.000000   \n",
       "\n",
       "            MoSold       YrSold      SalePrice  \n",
       "count  1460.000000  1460.000000    1460.000000  \n",
       "mean      6.321918  2007.815753  180921.195890  \n",
       "std       2.703626     1.328095   79442.502883  \n",
       "min       1.000000  2006.000000   34900.000000  \n",
       "25%       5.000000  2007.000000  129975.000000  \n",
       "50%       6.000000  2008.000000  163000.000000  \n",
       "75%       8.000000  2009.000000  214000.000000  \n",
       "max      12.000000  2010.000000  755000.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice        1.000000\n",
       "OverallQual      0.790982\n",
       "GrLivArea        0.708624\n",
       "GarageCars       0.640409\n",
       "GarageArea       0.623431\n",
       "TotalBsmtSF      0.613581\n",
       "1stFlrSF         0.605852\n",
       "FullBath         0.560664\n",
       "TotRmsAbvGrd     0.533723\n",
       "YearBuilt        0.522897\n",
       "YearRemodAdd     0.507101\n",
       "GarageYrBlt      0.486362\n",
       "MasVnrArea       0.477493\n",
       "Fireplaces       0.466929\n",
       "BsmtFinSF1       0.386420\n",
       "LotFrontage      0.351799\n",
       "WoodDeckSF       0.324413\n",
       "2ndFlrSF         0.319334\n",
       "OpenPorchSF      0.315856\n",
       "HalfBath         0.284108\n",
       "LotArea          0.263843\n",
       "BsmtFullBath     0.227122\n",
       "BsmtUnfSF        0.214479\n",
       "BedroomAbvGr     0.168213\n",
       "ScreenPorch      0.111447\n",
       "PoolArea         0.092404\n",
       "MoSold           0.046432\n",
       "3SsnPorch        0.044584\n",
       "BsmtFinSF2      -0.011378\n",
       "BsmtHalfBath    -0.016844\n",
       "MiscVal         -0.021190\n",
       "Id              -0.021917\n",
       "LowQualFinSF    -0.025606\n",
       "YrSold          -0.028923\n",
       "OverallCond     -0.077856\n",
       "MSSubClass      -0.084284\n",
       "EnclosedPorch   -0.128578\n",
       "KitchenAbvGr    -0.135907\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix = data.corr()\n",
    "corr_matrix[\"SalePrice\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see, that dataset have many high correlating features. Later we will use Random Forest or Decision Tree for prove this results. Also, we will check not only numerical attrubutes. Lets plot with a few best features. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 49 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas.plotting import scatter_matrix\n",
    "\n",
    "best_corr_attributes = [name for name in corr_matrix.keys()\n",
    "              if np.fabs(corr_matrix[\"SalePrice\"].loc[name]) >= 0.6]\n",
    "\n",
    "scatter_matrix(data[best_corr_attributes], figsize=(20, 20))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "source": [
    "# Preparing data for algorithms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "train, test = train_test_split(data, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "drop_na = [\"GarageType\", \"GarageYrBlt\", \"GarageFinish\", \"GarageQual\", \"GarageCond\", \"LotFrontage\",\n",
    "                        \"MasVnrType\", \"MasVnrArea\", \"BsmtQual\", \"BsmtCond\", \"BsmtExposure\", \"BsmtFinType1\",\n",
    "                        \"BsmtFinType2\",\"Electrical\"]\n",
    "drop_columns = [\"Alley\", \"FireplaceQu\", \"PoolQC\", \"Fence\", \"MiscFeature\"]\n",
    "\n",
    "class DataCleaner(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"\n",
    "    This class remove empty rows or columns\n",
    "    \n",
    "    Methods:\n",
    "    fit(X, y=None):\n",
    "        Return self object\n",
    "        \n",
    "    transform(X):\n",
    "        Remove rows and columns in X\n",
    "    \n",
    "    \"\"\"\n",
    "    def __init__(self, drop_na, drop_columns):\n",
    "        \"\"\"\n",
    "        Parameters\n",
    "        drop_na : list\n",
    "            Column names in which rows with cells that contains N/A will be deleted\n",
    "        \n",
    "        drop_columns : list \n",
    "            Column names that must be deleted\n",
    "        \n",
    "        \"\"\"\n",
    "        self._drop_na = drop_na\n",
    "        self._drop_columns = drop_columns\n",
    "   \n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        \"\"\"\n",
    "        Remove columns and rows by params that you passed in constrictor \n",
    "        \n",
    "        Parameters\n",
    "        X : pandas.core.frame.DataFrame\n",
    "            Input data\n",
    "            \n",
    "        Returns\n",
    "        pandas.core.frame.DataFrame\n",
    "            Data with removed rows and cols that you passed in constrictor \n",
    "        \"\"\"\n",
    "        \n",
    "        new_X = X.drop(self._drop_columns, axis=1)\n",
    "        new_X = new_X.dropna(subset=self._drop_na)\n",
    "        \n",
    "        return new_X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>SaleType</th>\n",
       "      <th>SaleCondition</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>966</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>10237</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>RRAn</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2007</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>783</td>\n",
       "      <td>783</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>783</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1484</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2</td>\n",
       "      <td>393</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "      <td>178900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>622</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10800</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NWAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1974</td>\n",
       "      <td>1997</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>956</td>\n",
       "      <td>Rec</td>\n",
       "      <td>182</td>\n",
       "      <td>384</td>\n",
       "      <td>1522</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1548</td>\n",
       "      <td>1066</td>\n",
       "      <td>0</td>\n",
       "      <td>2614</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>38</td>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>240000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>430</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>130.0</td>\n",
       "      <td>11457</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Timber</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1988</td>\n",
       "      <td>1988</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>1005</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>387</td>\n",
       "      <td>1392</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1412</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1412</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2</td>\n",
       "      <td>576</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>175000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1374</th>\n",
       "      <td>1375</td>\n",
       "      <td>60</td>\n",
       "      <td>FV</td>\n",
       "      <td>85.0</td>\n",
       "      <td>10625</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Somerst</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>CemntBd</td>\n",
       "      <td>CmentBd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>1026</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1026</td>\n",
       "      <td>932</td>\n",
       "      <td>0</td>\n",
       "      <td>1958</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>3</td>\n",
       "      <td>936</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>154</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>61.0</td>\n",
       "      <td>7658</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NridgHt</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>TwnhsE</td>\n",
       "      <td>1Story</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>412.0</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>456</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>1296</td>\n",
       "      <td>1752</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1752</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1752</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Ex</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>576</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>196</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "      <td>319900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street LotShape  \\\n",
       "965    966          60       RL         65.0    10237   Pave      Reg   \n",
       "621    622          60       RL         90.0    10800   Pave      Reg   \n",
       "429    430          20       RL        130.0    11457   Pave      IR1   \n",
       "1374  1375          60       FV         85.0    10625   Pave      Reg   \n",
       "45      46         120       RL         61.0     7658   Pave      Reg   \n",
       "\n",
       "     LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "965          Lvl    AllPub    Inside       Gtl      Gilbert       RRAn   \n",
       "621          Lvl    AllPub    Inside       Gtl       NWAmes       Norm   \n",
       "429          Lvl    AllPub    Corner       Gtl       Timber       Norm   \n",
       "1374         Lvl    AllPub    Inside       Gtl      Somerst       Norm   \n",
       "45           Lvl    AllPub    Inside       Gtl      NridgHt       Norm   \n",
       "\n",
       "     Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "965        Norm     1Fam     2Story            6            5       2005   \n",
       "621        Norm     1Fam     2Story            6            7       1974   \n",
       "429        Norm     1Fam     1Story            6            5       1988   \n",
       "1374       Norm     1Fam     2Story            7            5       2005   \n",
       "45         Norm   TwnhsE     1Story            9            5       2005   \n",
       "\n",
       "      YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "965           2007     Gable  CompShg     VinylSd     VinylSd       None   \n",
       "621           1997     Gable  CompShg     HdBoard     HdBoard       None   \n",
       "429           1988     Gable  CompShg     HdBoard     HdBoard       None   \n",
       "1374          2005     Gable  CompShg     CemntBd     CmentBd       None   \n",
       "45            2005       Hip  CompShg     MetalSd     MetalSd    BrkFace   \n",
       "\n",
       "      MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond  \\\n",
       "965          0.0        Gd        TA      PConc       Gd       TA   \n",
       "621          0.0        TA        TA     CBlock       TA       TA   \n",
       "429          0.0        TA        TA     CBlock       Gd       TA   \n",
       "1374         0.0        Gd        TA      PConc       Gd       TA   \n",
       "45         412.0        Ex        TA      PConc       Ex       TA   \n",
       "\n",
       "     BsmtExposure BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  \\\n",
       "965            No          Unf           0          Unf           0   \n",
       "621            No          ALQ         956          Rec         182   \n",
       "429            Mn          GLQ        1005          Unf           0   \n",
       "1374           No          Unf           0          Unf           0   \n",
       "45             No          GLQ         456          Unf           0   \n",
       "\n",
       "      BsmtUnfSF  TotalBsmtSF Heating HeatingQC CentralAir Electrical  \\\n",
       "965         783          783    GasA        Ex          Y      SBrkr   \n",
       "621         384         1522    GasA        TA          Y      SBrkr   \n",
       "429         387         1392    GasA        TA          Y      SBrkr   \n",
       "1374       1026         1026    GasA        Ex          Y      SBrkr   \n",
       "45         1296         1752    GasA        Ex          Y      SBrkr   \n",
       "\n",
       "      1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  \\\n",
       "965        783       701             0       1484             0             0   \n",
       "621       1548      1066             0       2614             0             0   \n",
       "429       1412         0             0       1412             1             0   \n",
       "1374      1026       932             0       1958             0             0   \n",
       "45        1752         0             0       1752             1             0   \n",
       "\n",
       "      FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr KitchenQual  \\\n",
       "965          2         1             3             1          Gd   \n",
       "621          2         1             4             1          TA   \n",
       "429          2         0             3             1          Gd   \n",
       "1374         2         1             3             1          Gd   \n",
       "45           2         0             2             1          Ex   \n",
       "\n",
       "      TotRmsAbvGrd Functional  Fireplaces GarageType  GarageYrBlt  \\\n",
       "965              8        Typ           1     Attchd       2005.0   \n",
       "621              9        Typ           1     Attchd       1974.0   \n",
       "429              6        Typ           1     Attchd       1988.0   \n",
       "1374             9        Typ           1     Attchd       2005.0   \n",
       "45               6        Typ           1     Attchd       2005.0   \n",
       "\n",
       "     GarageFinish  GarageCars  GarageArea GarageQual GarageCond PavedDrive  \\\n",
       "965           Fin           2         393         TA         TA          Y   \n",
       "621           RFn           2         624         TA         TA          Y   \n",
       "429           Unf           2         576         TA         TA          Y   \n",
       "1374          Fin           3         936         TA         TA          Y   \n",
       "45            RFn           2         576         TA         TA          Y   \n",
       "\n",
       "      WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "965            0           72              0          0            0   \n",
       "621           38          243              0          0            0   \n",
       "429            0            0            169          0            0   \n",
       "1374         154          210              0          0            0   \n",
       "45           196           82              0          0            0   \n",
       "\n",
       "      PoolArea  MiscVal  MoSold  YrSold SaleType SaleCondition  SalePrice  \n",
       "965          0        0       7    2007      New       Partial     178900  \n",
       "621          0        0       6    2008       WD        Normal     240000  \n",
       "429          0        0       3    2009       WD        Normal     175000  \n",
       "1374         0        0       7    2008       WD        Normal     250000  \n",
       "45           0        0       2    2010       WD        Normal     319900  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_temp = train.copy()\n",
    "\n",
    "train_temp = DataCleaner(drop_na, drop_columns).fit_transform(train_temp)\n",
    "train_temp.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class DataColumnSplitter(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"\n",
    "    This class stplit input data to data without column that you indicate (data_ field) and \n",
    "    indicated column (column_ filed)\n",
    "    \n",
    "    Attributes\n",
    "    data_ : pandas.core.frame.DataFrame\n",
    "        Inputed data without labels\n",
    "        \n",
    "    column_ : pandas.core.series.Series\n",
    "        Removed column\n",
    "    \n",
    "    target_column_ : str \n",
    "        Name of column that you indicate\n",
    "    \n",
    "    Methods\n",
    "    fit(X, y=None)\n",
    "        Return self object \n",
    "        \n",
    "    transfrom(X)\n",
    "        Remove selected column from DataFrame and save removed column in calss field named column_\n",
    "    \"\"\"\n",
    "    def __init__(self, target_column):\n",
    "        \"\"\"\n",
    "        Parameters:\n",
    "        target_column : str\n",
    "            Name of column\n",
    "        \"\"\"\n",
    "        self.target_column_ = target_column\n",
    "        self.data_ = None\n",
    "        self.column_ = None\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        \"\"\"\n",
    "        Remove selected column from DataFrame and save removed column in calss field named column_\n",
    "        \n",
    "        Parameters\n",
    "        X : pandas.core.frame.DataFrame\n",
    "            Input data\n",
    "        \n",
    "        Returns\n",
    "        pandas.core.frame.DataFrame\n",
    "            Data without selected column \n",
    "        \"\"\"\n",
    "        \n",
    "        if self.target_column_ in X.columns:\n",
    "            data = X.drop(self.target_column_, axis=1)\n",
    "            self.column_ = X[self.target_column_].copy()\n",
    "            self.data_ = data\n",
    "        else:\n",
    "            self.data_ = X\n",
    "        return self.data_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = None, None\n",
    "\n",
    "dcs = DataColumnSplitter(\"SalePrice\")\n",
    "X_train = dcs.fit_transform(train_temp)\n",
    "y_train = dcs.column_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>MSZoning</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>Street</th>\n",
       "      <th>LotShape</th>\n",
       "      <th>LandContour</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>LotConfig</th>\n",
       "      <th>LandSlope</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Condition1</th>\n",
       "      <th>Condition2</th>\n",
       "      <th>BldgType</th>\n",
       "      <th>HouseStyle</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>RoofStyle</th>\n",
       "      <th>RoofMatl</th>\n",
       "      <th>Exterior1st</th>\n",
       "      <th>Exterior2nd</th>\n",
       "      <th>MasVnrType</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>ExterQual</th>\n",
       "      <th>ExterCond</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>BsmtQual</th>\n",
       "      <th>BsmtCond</th>\n",
       "      <th>BsmtExposure</th>\n",
       "      <th>BsmtFinType1</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinType2</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>Heating</th>\n",
       "      <th>HeatingQC</th>\n",
       "      <th>CentralAir</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>KitchenQual</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageType</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageFinish</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>GarageQual</th>\n",
       "      <th>GarageCond</th>\n",
       "      <th>PavedDrive</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>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>965</th>\n",
       "      <td>966</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>65.0</td>\n",
       "      <td>10237</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>RRAn</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2007</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>783</td>\n",
       "      <td>783</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>783</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1484</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>8</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>2</td>\n",
       "      <td>393</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>New</td>\n",
       "      <td>Partial</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>621</th>\n",
       "      <td>622</td>\n",
       "      <td>60</td>\n",
       "      <td>RL</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10800</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NWAmes</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1974</td>\n",
       "      <td>1997</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>956</td>\n",
       "      <td>Rec</td>\n",
       "      <td>182</td>\n",
       "      <td>384</td>\n",
       "      <td>1522</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1548</td>\n",
       "      <td>1066</td>\n",
       "      <td>0</td>\n",
       "      <td>2614</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>38</td>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>430</td>\n",
       "      <td>20</td>\n",
       "      <td>RL</td>\n",
       "      <td>130.0</td>\n",
       "      <td>11457</td>\n",
       "      <td>Pave</td>\n",
       "      <td>IR1</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Timber</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>1Story</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1988</td>\n",
       "      <td>1988</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>HdBoard</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>Mn</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>1005</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>387</td>\n",
       "      <td>1392</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1412</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1412</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>2</td>\n",
       "      <td>576</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1374</th>\n",
       "      <td>1375</td>\n",
       "      <td>60</td>\n",
       "      <td>FV</td>\n",
       "      <td>85.0</td>\n",
       "      <td>10625</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>Somerst</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>2Story</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>Gable</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>CemntBd</td>\n",
       "      <td>CmentBd</td>\n",
       "      <td>None</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Gd</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>1026</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1026</td>\n",
       "      <td>932</td>\n",
       "      <td>0</td>\n",
       "      <td>1958</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>9</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>Fin</td>\n",
       "      <td>3</td>\n",
       "      <td>936</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>154</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>46</td>\n",
       "      <td>120</td>\n",
       "      <td>RL</td>\n",
       "      <td>61.0</td>\n",
       "      <td>7658</td>\n",
       "      <td>Pave</td>\n",
       "      <td>Reg</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>NridgHt</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>TwnhsE</td>\n",
       "      <td>1Story</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>Hip</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>MetalSd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>412.0</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>PConc</td>\n",
       "      <td>Ex</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>456</td>\n",
       "      <td>Unf</td>\n",
       "      <td>0</td>\n",
       "      <td>1296</td>\n",
       "      <td>1752</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Y</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>1752</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1752</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>Ex</td>\n",
       "      <td>6</td>\n",
       "      <td>Typ</td>\n",
       "      <td>1</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>RFn</td>\n",
       "      <td>2</td>\n",
       "      <td>576</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Y</td>\n",
       "      <td>196</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>WD</td>\n",
       "      <td>Normal</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id  MSSubClass MSZoning  LotFrontage  LotArea Street LotShape  \\\n",
       "965    966          60       RL         65.0    10237   Pave      Reg   \n",
       "621    622          60       RL         90.0    10800   Pave      Reg   \n",
       "429    430          20       RL        130.0    11457   Pave      IR1   \n",
       "1374  1375          60       FV         85.0    10625   Pave      Reg   \n",
       "45      46         120       RL         61.0     7658   Pave      Reg   \n",
       "\n",
       "     LandContour Utilities LotConfig LandSlope Neighborhood Condition1  \\\n",
       "965          Lvl    AllPub    Inside       Gtl      Gilbert       RRAn   \n",
       "621          Lvl    AllPub    Inside       Gtl       NWAmes       Norm   \n",
       "429          Lvl    AllPub    Corner       Gtl       Timber       Norm   \n",
       "1374         Lvl    AllPub    Inside       Gtl      Somerst       Norm   \n",
       "45           Lvl    AllPub    Inside       Gtl      NridgHt       Norm   \n",
       "\n",
       "     Condition2 BldgType HouseStyle  OverallQual  OverallCond  YearBuilt  \\\n",
       "965        Norm     1Fam     2Story            6            5       2005   \n",
       "621        Norm     1Fam     2Story            6            7       1974   \n",
       "429        Norm     1Fam     1Story            6            5       1988   \n",
       "1374       Norm     1Fam     2Story            7            5       2005   \n",
       "45         Norm   TwnhsE     1Story            9            5       2005   \n",
       "\n",
       "      YearRemodAdd RoofStyle RoofMatl Exterior1st Exterior2nd MasVnrType  \\\n",
       "965           2007     Gable  CompShg     VinylSd     VinylSd       None   \n",
       "621           1997     Gable  CompShg     HdBoard     HdBoard       None   \n",
       "429           1988     Gable  CompShg     HdBoard     HdBoard       None   \n",
       "1374          2005     Gable  CompShg     CemntBd     CmentBd       None   \n",
       "45            2005       Hip  CompShg     MetalSd     MetalSd    BrkFace   \n",
       "\n",
       "      MasVnrArea ExterQual ExterCond Foundation BsmtQual BsmtCond  \\\n",
       "965          0.0        Gd        TA      PConc       Gd       TA   \n",
       "621          0.0        TA        TA     CBlock       TA       TA   \n",
       "429          0.0        TA        TA     CBlock       Gd       TA   \n",
       "1374         0.0        Gd        TA      PConc       Gd       TA   \n",
       "45         412.0        Ex        TA      PConc       Ex       TA   \n",
       "\n",
       "     BsmtExposure BsmtFinType1  BsmtFinSF1 BsmtFinType2  BsmtFinSF2  \\\n",
       "965            No          Unf           0          Unf           0   \n",
       "621            No          ALQ         956          Rec         182   \n",
       "429            Mn          GLQ        1005          Unf           0   \n",
       "1374           No          Unf           0          Unf           0   \n",
       "45             No          GLQ         456          Unf           0   \n",
       "\n",
       "      BsmtUnfSF  TotalBsmtSF Heating HeatingQC CentralAir Electrical  \\\n",
       "965         783          783    GasA        Ex          Y      SBrkr   \n",
       "621         384         1522    GasA        TA          Y      SBrkr   \n",
       "429         387         1392    GasA        TA          Y      SBrkr   \n",
       "1374       1026         1026    GasA        Ex          Y      SBrkr   \n",
       "45         1296         1752    GasA        Ex          Y      SBrkr   \n",
       "\n",
       "      1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  BsmtHalfBath  \\\n",
       "965        783       701             0       1484             0             0   \n",
       "621       1548      1066             0       2614             0             0   \n",
       "429       1412         0             0       1412             1             0   \n",
       "1374      1026       932             0       1958             0             0   \n",
       "45        1752         0             0       1752             1             0   \n",
       "\n",
       "      FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr KitchenQual  \\\n",
       "965          2         1             3             1          Gd   \n",
       "621          2         1             4             1          TA   \n",
       "429          2         0             3             1          Gd   \n",
       "1374         2         1             3             1          Gd   \n",
       "45           2         0             2             1          Ex   \n",
       "\n",
       "      TotRmsAbvGrd Functional  Fireplaces GarageType  GarageYrBlt  \\\n",
       "965              8        Typ           1     Attchd       2005.0   \n",
       "621              9        Typ           1     Attchd       1974.0   \n",
       "429              6        Typ           1     Attchd       1988.0   \n",
       "1374             9        Typ           1     Attchd       2005.0   \n",
       "45               6        Typ           1     Attchd       2005.0   \n",
       "\n",
       "     GarageFinish  GarageCars  GarageArea GarageQual GarageCond PavedDrive  \\\n",
       "965           Fin           2         393         TA         TA          Y   \n",
       "621           RFn           2         624         TA         TA          Y   \n",
       "429           Unf           2         576         TA         TA          Y   \n",
       "1374          Fin           3         936         TA         TA          Y   \n",
       "45            RFn           2         576         TA         TA          Y   \n",
       "\n",
       "      WoodDeckSF  OpenPorchSF  EnclosedPorch  3SsnPorch  ScreenPorch  \\\n",
       "965            0           72              0          0            0   \n",
       "621           38          243              0          0            0   \n",
       "429            0            0            169          0            0   \n",
       "1374         154          210              0          0            0   \n",
       "45           196           82              0          0            0   \n",
       "\n",
       "      PoolArea  MiscVal  MoSold  YrSold SaleType SaleCondition  \n",
       "965          0        0       7    2007      New       Partial  \n",
       "621          0        0       6    2008       WD        Normal  \n",
       "429          0        0       3    2009       WD        Normal  \n",
       "1374         0        0       7    2008       WD        Normal  \n",
       "45           0        0       2    2010       WD        Normal  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "965     178900\n",
       "621     240000\n",
       "429     175000\n",
       "1374    250000\n",
       "45      319900\n",
       "         ...  \n",
       "1456    210000\n",
       "1062     90000\n",
       "1302    290000\n",
       "1204    153500\n",
       "83      126500\n",
       "Name: SalePrice, Length: 878, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_better_numeric_columns(importances_features, threshold):\n",
    "    \"\"\"\n",
    "    Leaves the best attributes, by threshold\n",
    "    \n",
    "    Parameters\n",
    "    importances_features : pandas.core.frame.DataFrame\n",
    "        Table with indices as data column names and importances values in first column for all indices \n",
    "        \n",
    "    threshold : float \n",
    "        All rows whose importances values are less that threshold will be removed\n",
    "        \n",
    "    Returns\n",
    "    list \n",
    "        Column names whose importances values are gtrater that threshold\n",
    "    \n",
    "    \"\"\"\n",
    "    sorted_features = importances_features.sort_values(ascending=False)\n",
    "    columns = None\n",
    "    \n",
    "    if isinstance(threshold, float) and 0. < threshold < 1.:\n",
    "        columns = [name for name in sorted_features.index if sorted_features.loc[name] > threshold]\n",
    "    \n",
    "    elif isinstance(threshold, int) and threshold > 0:\n",
    "        columns = sorted_features.iloc[:threshold].index\n",
    "        \n",
    "    elif isinstance(threshold, float) and not 0. < threshold < 1.:\n",
    "        raise ValueError(\"Threshold as float must be in segment (0, 1)\")\n",
    "        \n",
    "    elif isinstance(threshold, float) and threshold <= 0:\n",
    "        raise ValueError(\"Threshold as int must be positive (> 0)\")\n",
    "        \n",
    "    else:\n",
    "        raise ValueError(\"Invalid variable 'threshold' data type. It must be int or float\")\n",
    "        \n",
    "    return list(columns)\n",
    "\n",
    "class BestAttributesSelector(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"\n",
    "    Select best attributes by corr matrix, tree, random forest or other\n",
    "    \n",
    "    Methods\n",
    "    fit(X, y=None)\n",
    "        Return self\n",
    "        \n",
    "    transform(self, X)\n",
    "        Select best attrubutes\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, importance_attrs, numbersonly=False):\n",
    "        \"\"\"\n",
    "        Patameters\n",
    "        importance_attrs : list\n",
    "            List with most importance column names\n",
    "            \n",
    "        \"\"\"\n",
    "        self._importance_attrs = importance_attrs\n",
    "        \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        \"\"\"\n",
    "        Select best attributes in input series\n",
    "        \n",
    "        Patameters\n",
    "        X : pandas.core.frame.DataFrame\n",
    "            Input data \n",
    "        \n",
    "        Returns\n",
    "        pandas.core.frame.DataFrame\n",
    "            DataFrame with best attributes            \n",
    "        \"\"\"\n",
    "        new_X = X[self._importance_attrs]\n",
    "            \n",
    "        return new_X\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'GarageArea']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_better_numeric_columns(corr_matrix[\"SalePrice\"], 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "class ColumnNamesSaver(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"\n",
    "    Save column names\n",
    "    \n",
    "    Attributes\n",
    "    column_names_ : list\n",
    "        Column names\n",
    "    \n",
    "    Methods\n",
    "    fit(X, y=None)\n",
    "        Return self\n",
    "        \n",
    "    transform(self, X)\n",
    "        Save column names \n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self.column_names_ = None\n",
    "    \n",
    "    def fit(self, X, y=None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        \"\"\"\n",
    "        Save column names\n",
    "        \n",
    "        Parameters\n",
    "        X : pandas.core.frame.DataFrame\n",
    "            Input data \n",
    "            \n",
    "        Returns\n",
    "        X : pandas.core.frame.DataFrame\n",
    "            Not transformed input data\n",
    "        \"\"\"\n",
    "        self.column_names_ = list(X.columns)\n",
    "        return X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "class CathgoricalEncoder(BaseEstimator, TransformerMixin):\n",
    "    \"\"\"\n",
    "    Transform cathegorical attributes to numerical columns\n",
    "    \n",
    "    Methods\n",
    "    fit(X, y=None)\n",
    "        Return self\n",
    "    \n",
    "    transfrom(X)\n",
    "        Transfrom cathegorical attributes to numerical columns\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        self._encoder = OneHotEncoder()\n",
    "        \n",
    "    def fit(self, X, y = None):\n",
    "        return self\n",
    "    \n",
    "    def transform(self, X):\n",
    "        \"\"\"\n",
    "        Select cathegirical attributes from input DataFrame, use sklearn.OneHotEncoder to transfrom data and \n",
    "        merge transfromed data with numerical attributes\n",
    "        \n",
    "        Parameters\n",
    "        X : pandas.core.frame.DataFrame\n",
    "            Input data\n",
    "            \n",
    "        Returns\n",
    "        pandas.core.frame.DataFrame\n",
    "            Prepared data\n",
    "        \"\"\"\n",
    "        cat_attributes = [name for name in X.dtypes.keys() \n",
    "                  if str(X[name].dtype) == \"object\"]\n",
    "        \n",
    "        cat_matrix = self._encoder.fit_transform(X[cat_attributes])\n",
    "        encoded_part = pd.DataFrame(cat_matrix.toarray(), columns=self._get_new_column_names(cat_matrix, cat_attributes, self._encoder))\n",
    "        \n",
    "        new_X = X.drop(cat_attributes, axis=1)\n",
    "        new_X.reset_index(drop=True, inplace=True)\n",
    "        encoded_part.reset_index(drop=True, inplace=True)\n",
    "        \n",
    "        new_X = pd.concat([new_X, encoded_part], axis=1)\n",
    "        \n",
    "        return new_X\n",
    "            \n",
    "    @staticmethod\n",
    "    def _get_new_column_names(matrix, cathegorical_attributes, encoder):\n",
    "        new_column_names = []\n",
    "        for column_name, values in zip(cathegorical_attributes, encoder.categories_):\n",
    "            for value in values:\n",
    "                new_column_names.append(f\"{column_name}_{value}\")\n",
    "        return new_column_names\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>MSSubClass</th>\n",
       "      <th>LotFrontage</th>\n",
       "      <th>LotArea</th>\n",
       "      <th>OverallQual</th>\n",
       "      <th>OverallCond</th>\n",
       "      <th>YearBuilt</th>\n",
       "      <th>YearRemodAdd</th>\n",
       "      <th>MasVnrArea</th>\n",
       "      <th>BsmtFinSF1</th>\n",
       "      <th>BsmtFinSF2</th>\n",
       "      <th>BsmtUnfSF</th>\n",
       "      <th>TotalBsmtSF</th>\n",
       "      <th>1stFlrSF</th>\n",
       "      <th>2ndFlrSF</th>\n",
       "      <th>LowQualFinSF</th>\n",
       "      <th>GrLivArea</th>\n",
       "      <th>BsmtFullBath</th>\n",
       "      <th>BsmtHalfBath</th>\n",
       "      <th>FullBath</th>\n",
       "      <th>HalfBath</th>\n",
       "      <th>BedroomAbvGr</th>\n",
       "      <th>KitchenAbvGr</th>\n",
       "      <th>TotRmsAbvGrd</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>GarageYrBlt</th>\n",
       "      <th>GarageCars</th>\n",
       "      <th>GarageArea</th>\n",
       "      <th>WoodDeckSF</th>\n",
       "      <th>OpenPorchSF</th>\n",
       "      <th>EnclosedPorch</th>\n",
       "      <th>3SsnPorch</th>\n",
       "      <th>ScreenPorch</th>\n",
       "      <th>PoolArea</th>\n",
       "      <th>MiscVal</th>\n",
       "      <th>MoSold</th>\n",
       "      <th>YrSold</th>\n",
       "      <th>MSZoning_C (all)</th>\n",
       "      <th>MSZoning_FV</th>\n",
       "      <th>MSZoning_RH</th>\n",
       "      <th>MSZoning_RL</th>\n",
       "      <th>MSZoning_RM</th>\n",
       "      <th>Street_Grvl</th>\n",
       "      <th>Street_Pave</th>\n",
       "      <th>LotShape_IR1</th>\n",
       "      <th>LotShape_IR2</th>\n",
       "      <th>LotShape_IR3</th>\n",
       "      <th>LotShape_Reg</th>\n",
       "      <th>LandContour_Bnk</th>\n",
       "      <th>LandContour_HLS</th>\n",
       "      <th>LandContour_Low</th>\n",
       "      <th>LandContour_Lvl</th>\n",
       "      <th>Utilities_AllPub</th>\n",
       "      <th>LotConfig_Corner</th>\n",
       "      <th>LotConfig_CulDSac</th>\n",
       "      <th>LotConfig_FR2</th>\n",
       "      <th>LotConfig_FR3</th>\n",
       "      <th>LotConfig_Inside</th>\n",
       "      <th>LandSlope_Gtl</th>\n",
       "      <th>LandSlope_Mod</th>\n",
       "      <th>LandSlope_Sev</th>\n",
       "      <th>Neighborhood_Blmngtn</th>\n",
       "      <th>Neighborhood_Blueste</th>\n",
       "      <th>Neighborhood_BrDale</th>\n",
       "      <th>Neighborhood_BrkSide</th>\n",
       "      <th>Neighborhood_ClearCr</th>\n",
       "      <th>Neighborhood_CollgCr</th>\n",
       "      <th>Neighborhood_Crawfor</th>\n",
       "      <th>Neighborhood_Edwards</th>\n",
       "      <th>Neighborhood_Gilbert</th>\n",
       "      <th>Neighborhood_IDOTRR</th>\n",
       "      <th>Neighborhood_MeadowV</th>\n",
       "      <th>Neighborhood_Mitchel</th>\n",
       "      <th>Neighborhood_NAmes</th>\n",
       "      <th>Neighborhood_NPkVill</th>\n",
       "      <th>Neighborhood_NWAmes</th>\n",
       "      <th>Neighborhood_NoRidge</th>\n",
       "      <th>Neighborhood_NridgHt</th>\n",
       "      <th>Neighborhood_OldTown</th>\n",
       "      <th>Neighborhood_SWISU</th>\n",
       "      <th>Neighborhood_Sawyer</th>\n",
       "      <th>Neighborhood_SawyerW</th>\n",
       "      <th>Neighborhood_Somerst</th>\n",
       "      <th>Neighborhood_StoneBr</th>\n",
       "      <th>Neighborhood_Timber</th>\n",
       "      <th>Neighborhood_Veenker</th>\n",
       "      <th>Condition1_Artery</th>\n",
       "      <th>Condition1_Feedr</th>\n",
       "      <th>Condition1_Norm</th>\n",
       "      <th>Condition1_PosA</th>\n",
       "      <th>Condition1_PosN</th>\n",
       "      <th>Condition1_RRAe</th>\n",
       "      <th>Condition1_RRAn</th>\n",
       "      <th>Condition1_RRNe</th>\n",
       "      <th>Condition1_RRNn</th>\n",
       "      <th>Condition2_Artery</th>\n",
       "      <th>Condition2_Feedr</th>\n",
       "      <th>Condition2_Norm</th>\n",
       "      <th>Condition2_PosA</th>\n",
       "      <th>Condition2_PosN</th>\n",
       "      <th>Condition2_RRNn</th>\n",
       "      <th>BldgType_1Fam</th>\n",
       "      <th>BldgType_2fmCon</th>\n",
       "      <th>BldgType_Duplex</th>\n",
       "      <th>BldgType_Twnhs</th>\n",
       "      <th>BldgType_TwnhsE</th>\n",
       "      <th>HouseStyle_1.5Fin</th>\n",
       "      <th>HouseStyle_1.5Unf</th>\n",
       "      <th>HouseStyle_1Story</th>\n",
       "      <th>HouseStyle_2.5Fin</th>\n",
       "      <th>HouseStyle_2.5Unf</th>\n",
       "      <th>HouseStyle_2Story</th>\n",
       "      <th>HouseStyle_SFoyer</th>\n",
       "      <th>HouseStyle_SLvl</th>\n",
       "      <th>RoofStyle_Flat</th>\n",
       "      <th>RoofStyle_Gable</th>\n",
       "      <th>RoofStyle_Gambrel</th>\n",
       "      <th>RoofStyle_Hip</th>\n",
       "      <th>RoofStyle_Mansard</th>\n",
       "      <th>RoofMatl_ClyTile</th>\n",
       "      <th>RoofMatl_CompShg</th>\n",
       "      <th>RoofMatl_Roll</th>\n",
       "      <th>RoofMatl_Tar&amp;Grv</th>\n",
       "      <th>RoofMatl_WdShake</th>\n",
       "      <th>RoofMatl_WdShngl</th>\n",
       "      <th>Exterior1st_AsbShng</th>\n",
       "      <th>Exterior1st_BrkComm</th>\n",
       "      <th>Exterior1st_BrkFace</th>\n",
       "      <th>Exterior1st_CBlock</th>\n",
       "      <th>Exterior1st_CemntBd</th>\n",
       "      <th>Exterior1st_HdBoard</th>\n",
       "      <th>Exterior1st_ImStucc</th>\n",
       "      <th>Exterior1st_MetalSd</th>\n",
       "      <th>Exterior1st_Plywood</th>\n",
       "      <th>Exterior1st_Stone</th>\n",
       "      <th>Exterior1st_Stucco</th>\n",
       "      <th>Exterior1st_VinylSd</th>\n",
       "      <th>Exterior1st_Wd Sdng</th>\n",
       "      <th>Exterior1st_WdShing</th>\n",
       "      <th>Exterior2nd_AsbShng</th>\n",
       "      <th>Exterior2nd_AsphShn</th>\n",
       "      <th>Exterior2nd_Brk Cmn</th>\n",
       "      <th>Exterior2nd_BrkFace</th>\n",
       "      <th>Exterior2nd_CBlock</th>\n",
       "      <th>Exterior2nd_CmentBd</th>\n",
       "      <th>Exterior2nd_HdBoard</th>\n",
       "      <th>Exterior2nd_ImStucc</th>\n",
       "      <th>Exterior2nd_MetalSd</th>\n",
       "      <th>Exterior2nd_Other</th>\n",
       "      <th>Exterior2nd_Plywood</th>\n",
       "      <th>Exterior2nd_Stone</th>\n",
       "      <th>Exterior2nd_Stucco</th>\n",
       "      <th>Exterior2nd_VinylSd</th>\n",
       "      <th>Exterior2nd_Wd Sdng</th>\n",
       "      <th>Exterior2nd_Wd Shng</th>\n",
       "      <th>MasVnrType_BrkCmn</th>\n",
       "      <th>MasVnrType_BrkFace</th>\n",
       "      <th>MasVnrType_None</th>\n",
       "      <th>MasVnrType_Stone</th>\n",
       "      <th>ExterQual_Ex</th>\n",
       "      <th>ExterQual_Fa</th>\n",
       "      <th>ExterQual_Gd</th>\n",
       "      <th>ExterQual_TA</th>\n",
       "      <th>ExterCond_Ex</th>\n",
       "      <th>ExterCond_Fa</th>\n",
       "      <th>ExterCond_Gd</th>\n",
       "      <th>ExterCond_TA</th>\n",
       "      <th>Foundation_BrkTil</th>\n",
       "      <th>Foundation_CBlock</th>\n",
       "      <th>Foundation_PConc</th>\n",
       "      <th>Foundation_Stone</th>\n",
       "      <th>Foundation_Wood</th>\n",
       "      <th>BsmtQual_Ex</th>\n",
       "      <th>BsmtQual_Fa</th>\n",
       "      <th>BsmtQual_Gd</th>\n",
       "      <th>BsmtQual_TA</th>\n",
       "      <th>BsmtCond_Fa</th>\n",
       "      <th>BsmtCond_Gd</th>\n",
       "      <th>BsmtCond_Po</th>\n",
       "      <th>BsmtCond_TA</th>\n",
       "      <th>BsmtExposure_Av</th>\n",
       "      <th>BsmtExposure_Gd</th>\n",
       "      <th>BsmtExposure_Mn</th>\n",
       "      <th>BsmtExposure_No</th>\n",
       "      <th>BsmtFinType1_ALQ</th>\n",
       "      <th>BsmtFinType1_BLQ</th>\n",
       "      <th>BsmtFinType1_GLQ</th>\n",
       "      <th>BsmtFinType1_LwQ</th>\n",
       "      <th>BsmtFinType1_Rec</th>\n",
       "      <th>BsmtFinType1_Unf</th>\n",
       "      <th>BsmtFinType2_ALQ</th>\n",
       "      <th>BsmtFinType2_BLQ</th>\n",
       "      <th>BsmtFinType2_GLQ</th>\n",
       "      <th>BsmtFinType2_LwQ</th>\n",
       "      <th>BsmtFinType2_Rec</th>\n",
       "      <th>BsmtFinType2_Unf</th>\n",
       "      <th>Heating_GasA</th>\n",
       "      <th>Heating_GasW</th>\n",
       "      <th>Heating_Grav</th>\n",
       "      <th>Heating_OthW</th>\n",
       "      <th>HeatingQC_Ex</th>\n",
       "      <th>HeatingQC_Fa</th>\n",
       "      <th>HeatingQC_Gd</th>\n",
       "      <th>HeatingQC_Po</th>\n",
       "      <th>HeatingQC_TA</th>\n",
       "      <th>CentralAir_N</th>\n",
       "      <th>CentralAir_Y</th>\n",
       "      <th>Electrical_FuseA</th>\n",
       "      <th>Electrical_FuseF</th>\n",
       "      <th>Electrical_FuseP</th>\n",
       "      <th>Electrical_Mix</th>\n",
       "      <th>Electrical_SBrkr</th>\n",
       "      <th>KitchenQual_Ex</th>\n",
       "      <th>KitchenQual_Fa</th>\n",
       "      <th>KitchenQual_Gd</th>\n",
       "      <th>KitchenQual_TA</th>\n",
       "      <th>Functional_Maj1</th>\n",
       "      <th>Functional_Maj2</th>\n",
       "      <th>Functional_Min1</th>\n",
       "      <th>Functional_Min2</th>\n",
       "      <th>Functional_Mod</th>\n",
       "      <th>Functional_Typ</th>\n",
       "      <th>GarageType_2Types</th>\n",
       "      <th>GarageType_Attchd</th>\n",
       "      <th>GarageType_Basment</th>\n",
       "      <th>GarageType_BuiltIn</th>\n",
       "      <th>GarageType_CarPort</th>\n",
       "      <th>GarageType_Detchd</th>\n",
       "      <th>GarageFinish_Fin</th>\n",
       "      <th>GarageFinish_RFn</th>\n",
       "      <th>GarageFinish_Unf</th>\n",
       "      <th>GarageQual_Ex</th>\n",
       "      <th>GarageQual_Fa</th>\n",
       "      <th>GarageQual_Gd</th>\n",
       "      <th>GarageQual_Po</th>\n",
       "      <th>GarageQual_TA</th>\n",
       "      <th>GarageCond_Ex</th>\n",
       "      <th>GarageCond_Fa</th>\n",
       "      <th>GarageCond_Gd</th>\n",
       "      <th>GarageCond_Po</th>\n",
       "      <th>GarageCond_TA</th>\n",
       "      <th>PavedDrive_N</th>\n",
       "      <th>PavedDrive_P</th>\n",
       "      <th>PavedDrive_Y</th>\n",
       "      <th>SaleType_COD</th>\n",
       "      <th>SaleType_CWD</th>\n",
       "      <th>SaleType_Con</th>\n",
       "      <th>SaleType_ConLD</th>\n",
       "      <th>SaleType_ConLI</th>\n",
       "      <th>SaleType_ConLw</th>\n",
       "      <th>SaleType_New</th>\n",
       "      <th>SaleType_Oth</th>\n",
       "      <th>SaleType_WD</th>\n",
       "      <th>SaleCondition_Abnorml</th>\n",
       "      <th>SaleCondition_AdjLand</th>\n",
       "      <th>SaleCondition_Alloca</th>\n",
       "      <th>SaleCondition_Family</th>\n",
       "      <th>SaleCondition_Normal</th>\n",
       "      <th>SaleCondition_Partial</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>966</td>\n",
       "      <td>60</td>\n",
       "      <td>65.0</td>\n",
       "      <td>10237</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>783</td>\n",
       "      <td>783</td>\n",
       "      <td>783</td>\n",
       "      <td>701</td>\n",
       "      <td>0</td>\n",
       "      <td>1484</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2</td>\n",
       "      <td>393</td>\n",
       "      <td>0</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2007</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>622</td>\n",
       "      <td>60</td>\n",
       "      <td>90.0</td>\n",
       "      <td>10800</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>1974</td>\n",
       "      <td>1997</td>\n",
       "      <td>0.0</td>\n",
       "      <td>956</td>\n",
       "      <td>182</td>\n",
       "      <td>384</td>\n",
       "      <td>1522</td>\n",
       "      <td>1548</td>\n",
       "      <td>1066</td>\n",
       "      <td>0</td>\n",
       "      <td>2614</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>1974.0</td>\n",
       "      <td>2</td>\n",
       "      <td>624</td>\n",
       "      <td>38</td>\n",
       "      <td>243</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>430</td>\n",
       "      <td>20</td>\n",
       "      <td>130.0</td>\n",
       "      <td>11457</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>1988</td>\n",
       "      <td>1988</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1005</td>\n",
       "      <td>0</td>\n",
       "      <td>387</td>\n",
       "      <td>1392</td>\n",
       "      <td>1412</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1412</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>1988.0</td>\n",
       "      <td>2</td>\n",
       "      <td>576</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>169</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>2009</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1375</td>\n",
       "      <td>60</td>\n",
       "      <td>85.0</td>\n",
       "      <td>10625</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1026</td>\n",
       "      <td>1026</td>\n",
       "      <td>1026</td>\n",
       "      <td>932</td>\n",
       "      <td>0</td>\n",
       "      <td>1958</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>3</td>\n",
       "      <td>936</td>\n",
       "      <td>154</td>\n",
       "      <td>210</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>2008</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46</td>\n",
       "      <td>120</td>\n",
       "      <td>61.0</td>\n",
       "      <td>7658</td>\n",
       "      <td>9</td>\n",
       "      <td>5</td>\n",
       "      <td>2005</td>\n",
       "      <td>2005</td>\n",
       "      <td>412.0</td>\n",
       "      <td>456</td>\n",
       "      <td>0</td>\n",
       "      <td>1296</td>\n",
       "      <td>1752</td>\n",
       "      <td>1752</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1752</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>2</td>\n",
       "      <td>576</td>\n",
       "      <td>196</td>\n",
       "      <td>82</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Id  MSSubClass  LotFrontage  LotArea  OverallQual  OverallCond  \\\n",
       "0   966          60         65.0    10237            6            5   \n",
       "1   622          60         90.0    10800            6            7   \n",
       "2   430          20        130.0    11457            6            5   \n",
       "3  1375          60         85.0    10625            7            5   \n",
       "4    46         120         61.0     7658            9            5   \n",
       "\n",
       "   YearBuilt  YearRemodAdd  MasVnrArea  BsmtFinSF1  BsmtFinSF2  BsmtUnfSF  \\\n",
       "0       2005          2007         0.0           0           0        783   \n",
       "1       1974          1997         0.0         956         182        384   \n",
       "2       1988          1988         0.0        1005           0        387   \n",
       "3       2005          2005         0.0           0           0       1026   \n",
       "4       2005          2005       412.0         456           0       1296   \n",
       "\n",
       "   TotalBsmtSF  1stFlrSF  2ndFlrSF  LowQualFinSF  GrLivArea  BsmtFullBath  \\\n",
       "0          783       783       701             0       1484             0   \n",
       "1         1522      1548      1066             0       2614             0   \n",
       "2         1392      1412         0             0       1412             1   \n",
       "3         1026      1026       932             0       1958             0   \n",
       "4         1752      1752         0             0       1752             1   \n",
       "\n",
       "   BsmtHalfBath  FullBath  HalfBath  BedroomAbvGr  KitchenAbvGr  TotRmsAbvGrd  \\\n",
       "0             0         2         1             3             1             8   \n",
       "1             0         2         1             4             1             9   \n",
       "2             0         2         0             3             1             6   \n",
       "3             0         2         1             3             1             9   \n",
       "4             0         2         0             2             1             6   \n",
       "\n",
       "   Fireplaces  GarageYrBlt  GarageCars  GarageArea  WoodDeckSF  OpenPorchSF  \\\n",
       "0           1       2005.0           2         393           0           72   \n",
       "1           1       1974.0           2         624          38          243   \n",
       "2           1       1988.0           2         576           0            0   \n",
       "3           1       2005.0           3         936         154          210   \n",
       "4           1       2005.0           2         576         196           82   \n",
       "\n",
       "   EnclosedPorch  3SsnPorch  ScreenPorch  PoolArea  MiscVal  MoSold  YrSold  \\\n",
       "0              0          0            0         0        0       7    2007   \n",
       "1              0          0            0         0        0       6    2008   \n",
       "2            169          0            0         0        0       3    2009   \n",
       "3              0          0            0         0        0       7    2008   \n",
       "4              0          0            0         0        0       2    2010   \n",
       "\n",
       "   MSZoning_C (all)  MSZoning_FV  MSZoning_RH  MSZoning_RL  MSZoning_RM  \\\n",
       "0               0.0          0.0          0.0          1.0          0.0   \n",
       "1               0.0          0.0          0.0          1.0          0.0   \n",
       "2               0.0          0.0          0.0          1.0          0.0   \n",
       "3               0.0          1.0          0.0          0.0          0.0   \n",
       "4               0.0          0.0          0.0          1.0          0.0   \n",
       "\n",
       "   Street_Grvl  Street_Pave  LotShape_IR1  LotShape_IR2  LotShape_IR3  \\\n",
       "0          0.0          1.0           0.0           0.0           0.0   \n",
       "1          0.0          1.0           0.0           0.0           0.0   \n",
       "2          0.0          1.0           1.0           0.0           0.0   \n",
       "3          0.0          1.0           0.0           0.0           0.0   \n",
       "4          0.0          1.0           0.0           0.0           0.0   \n",
       "\n",
       "   LotShape_Reg  LandContour_Bnk  LandContour_HLS  LandContour_Low  \\\n",
       "0           1.0              0.0              0.0              0.0   \n",
       "1           1.0              0.0              0.0              0.0   \n",
       "2           0.0              0.0              0.0              0.0   \n",
       "3           1.0              0.0              0.0              0.0   \n",
       "4           1.0              0.0              0.0              0.0   \n",
       "\n",
       "   LandContour_Lvl  Utilities_AllPub  LotConfig_Corner  LotConfig_CulDSac  \\\n",
       "0              1.0               1.0               0.0                0.0   \n",
       "1              1.0               1.0               0.0                0.0   \n",
       "2              1.0               1.0               1.0                0.0   \n",
       "3              1.0               1.0               0.0                0.0   \n",
       "4              1.0               1.0               0.0                0.0   \n",
       "\n",
       "   LotConfig_FR2  LotConfig_FR3  LotConfig_Inside  LandSlope_Gtl  \\\n",
       "0            0.0            0.0               1.0            1.0   \n",
       "1            0.0            0.0               1.0            1.0   \n",
       "2            0.0            0.0               0.0            1.0   \n",
       "3            0.0            0.0               1.0            1.0   \n",
       "4            0.0            0.0               1.0            1.0   \n",
       "\n",
       "   LandSlope_Mod  LandSlope_Sev  Neighborhood_Blmngtn  Neighborhood_Blueste  \\\n",
       "0            0.0            0.0                   0.0                   0.0   \n",
       "1            0.0            0.0                   0.0                   0.0   \n",
       "2            0.0            0.0                   0.0                   0.0   \n",
       "3            0.0            0.0                   0.0                   0.0   \n",
       "4            0.0            0.0                   0.0                   0.0   \n",
       "\n",
       "   Neighborhood_BrDale  Neighborhood_BrkSide  Neighborhood_ClearCr  \\\n",
       "0                  0.0                   0.0                   0.0   \n",
       "1                  0.0                   0.0                   0.0   \n",
       "2                  0.0                   0.0                   0.0   \n",
       "3                  0.0                   0.0                   0.0   \n",
       "4                  0.0                   0.0                   0.0   \n",
       "\n",
       "   Neighborhood_CollgCr  Neighborhood_Crawfor  Neighborhood_Edwards  \\\n",
       "0                   0.0                   0.0                   0.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   0.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "\n",
       "   Neighborhood_Gilbert  Neighborhood_IDOTRR  Neighborhood_MeadowV  \\\n",
       "0                   1.0                  0.0                   0.0   \n",
       "1                   0.0                  0.0                   0.0   \n",
       "2                   0.0                  0.0                   0.0   \n",
       "3                   0.0                  0.0                   0.0   \n",
       "4                   0.0                  0.0                   0.0   \n",
       "\n",
       "   Neighborhood_Mitchel  Neighborhood_NAmes  Neighborhood_NPkVill  \\\n",
       "0                   0.0                 0.0                   0.0   \n",
       "1                   0.0                 0.0                   0.0   \n",
       "2                   0.0                 0.0                   0.0   \n",
       "3                   0.0                 0.0                   0.0   \n",
       "4                   0.0                 0.0                   0.0   \n",
       "\n",
       "   Neighborhood_NWAmes  Neighborhood_NoRidge  Neighborhood_NridgHt  \\\n",
       "0                  0.0                   0.0                   0.0   \n",
       "1                  1.0                   0.0                   0.0   \n",
       "2                  0.0                   0.0                   0.0   \n",
       "3                  0.0                   0.0                   0.0   \n",
       "4                  0.0                   0.0                   1.0   \n",
       "\n",
       "   Neighborhood_OldTown  Neighborhood_SWISU  Neighborhood_Sawyer  \\\n",
       "0                   0.0                 0.0                  0.0   \n",
       "1                   0.0                 0.0                  0.0   \n",
       "2                   0.0                 0.0                  0.0   \n",
       "3                   0.0                 0.0                  0.0   \n",
       "4                   0.0                 0.0                  0.0   \n",
       "\n",
       "   Neighborhood_SawyerW  Neighborhood_Somerst  Neighborhood_StoneBr  \\\n",
       "0                   0.0                   0.0                   0.0   \n",
       "1                   0.0                   0.0                   0.0   \n",
       "2                   0.0                   0.0                   0.0   \n",
       "3                   0.0                   1.0                   0.0   \n",
       "4                   0.0                   0.0                   0.0   \n",
       "\n",
       "   Neighborhood_Timber  Neighborhood_Veenker  Condition1_Artery  \\\n",
       "0                  0.0                   0.0                0.0   \n",
       "1                  0.0                   0.0                0.0   \n",
       "2                  1.0                   0.0                0.0   \n",
       "3                  0.0                   0.0                0.0   \n",
       "4                  0.0                   0.0                0.0   \n",
       "\n",
       "   Condition1_Feedr  Condition1_Norm  Condition1_PosA  Condition1_PosN  \\\n",
       "0               0.0              0.0              0.0              0.0   \n",
       "1               0.0              1.0              0.0              0.0   \n",
       "2               0.0              1.0              0.0              0.0   \n",
       "3               0.0              1.0              0.0              0.0   \n",
       "4               0.0              1.0              0.0              0.0   \n",
       "\n",
       "   Condition1_RRAe  Condition1_RRAn  Condition1_RRNe  Condition1_RRNn  \\\n",
       "0              0.0              1.0              0.0              0.0   \n",
       "1              0.0              0.0              0.0              0.0   \n",
       "2              0.0              0.0              0.0              0.0   \n",
       "3              0.0              0.0              0.0              0.0   \n",
       "4              0.0              0.0              0.0              0.0   \n",
       "\n",
       "   Condition2_Artery  Condition2_Feedr  Condition2_Norm  Condition2_PosA  \\\n",
       "0                0.0               0.0              1.0              0.0   \n",
       "1                0.0               0.0              1.0              0.0   \n",
       "2                0.0               0.0              1.0              0.0   \n",
       "3                0.0               0.0              1.0              0.0   \n",
       "4                0.0               0.0              1.0              0.0   \n",
       "\n",
       "   Condition2_PosN  Condition2_RRNn  BldgType_1Fam  BldgType_2fmCon  \\\n",
       "0              0.0              0.0            1.0              0.0   \n",
       "1              0.0              0.0            1.0              0.0   \n",
       "2              0.0              0.0            1.0              0.0   \n",
       "3              0.0              0.0            1.0              0.0   \n",
       "4              0.0              0.0            0.0              0.0   \n",
       "\n",
       "   BldgType_Duplex  BldgType_Twnhs  BldgType_TwnhsE  HouseStyle_1.5Fin  \\\n",
       "0              0.0             0.0              0.0                0.0   \n",
       "1              0.0             0.0              0.0                0.0   \n",
       "2              0.0             0.0              0.0                0.0   \n",
       "3              0.0             0.0              0.0                0.0   \n",
       "4              0.0             0.0              1.0                0.0   \n",
       "\n",
       "   HouseStyle_1.5Unf  HouseStyle_1Story  HouseStyle_2.5Fin  HouseStyle_2.5Unf  \\\n",
       "0                0.0                0.0                0.0                0.0   \n",
       "1                0.0                0.0                0.0                0.0   \n",
       "2                0.0                1.0                0.0                0.0   \n",
       "3                0.0                0.0                0.0                0.0   \n",
       "4                0.0                1.0                0.0                0.0   \n",
       "\n",
       "   HouseStyle_2Story  HouseStyle_SFoyer  HouseStyle_SLvl  RoofStyle_Flat  \\\n",
       "0                1.0                0.0              0.0             0.0   \n",
       "1                1.0                0.0              0.0             0.0   \n",
       "2                0.0                0.0              0.0             0.0   \n",
       "3                1.0                0.0              0.0             0.0   \n",
       "4                0.0                0.0              0.0             0.0   \n",
       "\n",
       "   RoofStyle_Gable  RoofStyle_Gambrel  RoofStyle_Hip  RoofStyle_Mansard  \\\n",
       "0              1.0                0.0            0.0                0.0   \n",
       "1              1.0                0.0            0.0                0.0   \n",
       "2              1.0                0.0            0.0                0.0   \n",
       "3              1.0                0.0            0.0                0.0   \n",
       "4              0.0                0.0            1.0                0.0   \n",
       "\n",
       "   RoofMatl_ClyTile  RoofMatl_CompShg  RoofMatl_Roll  RoofMatl_Tar&Grv  \\\n",
       "0               0.0               1.0            0.0               0.0   \n",
       "1               0.0               1.0            0.0               0.0   \n",
       "2               0.0               1.0            0.0               0.0   \n",
       "3               0.0               1.0            0.0               0.0   \n",
       "4               0.0               1.0            0.0               0.0   \n",
       "\n",
       "   RoofMatl_WdShake  RoofMatl_WdShngl  Exterior1st_AsbShng  \\\n",
       "0               0.0               0.0                  0.0   \n",
       "1               0.0               0.0                  0.0   \n",
       "2               0.0               0.0                  0.0   \n",
       "3               0.0               0.0                  0.0   \n",
       "4               0.0               0.0                  0.0   \n",
       "\n",
       "   Exterior1st_BrkComm  Exterior1st_BrkFace  Exterior1st_CBlock  \\\n",
       "0                  0.0                  0.0                 0.0   \n",
       "1                  0.0                  0.0                 0.0   \n",
       "2                  0.0                  0.0                 0.0   \n",
       "3                  0.0                  0.0                 0.0   \n",
       "4                  0.0                  0.0                 0.0   \n",
       "\n",
       "   Exterior1st_CemntBd  Exterior1st_HdBoard  Exterior1st_ImStucc  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  0.0                  1.0                  0.0   \n",
       "2                  0.0                  1.0                  0.0   \n",
       "3                  1.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "   Exterior1st_MetalSd  Exterior1st_Plywood  Exterior1st_Stone  \\\n",
       "0                  0.0                  0.0                0.0   \n",
       "1                  0.0                  0.0                0.0   \n",
       "2                  0.0                  0.0                0.0   \n",
       "3                  0.0                  0.0                0.0   \n",
       "4                  1.0                  0.0                0.0   \n",
       "\n",
       "   Exterior1st_Stucco  Exterior1st_VinylSd  Exterior1st_Wd Sdng  \\\n",
       "0                 0.0                  1.0                  0.0   \n",
       "1                 0.0                  0.0                  0.0   \n",
       "2                 0.0                  0.0                  0.0   \n",
       "3                 0.0                  0.0                  0.0   \n",
       "4                 0.0                  0.0                  0.0   \n",
       "\n",
       "   Exterior1st_WdShing  Exterior2nd_AsbShng  Exterior2nd_AsphShn  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  0.0                  0.0                  0.0   \n",
       "2                  0.0                  0.0                  0.0   \n",
       "3                  0.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "   Exterior2nd_Brk Cmn  Exterior2nd_BrkFace  Exterior2nd_CBlock  \\\n",
       "0                  0.0                  0.0                 0.0   \n",
       "1                  0.0                  0.0                 0.0   \n",
       "2                  0.0                  0.0                 0.0   \n",
       "3                  0.0                  0.0                 0.0   \n",
       "4                  0.0                  0.0                 0.0   \n",
       "\n",
       "   Exterior2nd_CmentBd  Exterior2nd_HdBoard  Exterior2nd_ImStucc  \\\n",
       "0                  0.0                  0.0                  0.0   \n",
       "1                  0.0                  1.0                  0.0   \n",
       "2                  0.0                  1.0                  0.0   \n",
       "3                  1.0                  0.0                  0.0   \n",
       "4                  0.0                  0.0                  0.0   \n",
       "\n",
       "   Exterior2nd_MetalSd  Exterior2nd_Other  Exterior2nd_Plywood  \\\n",
       "0                  0.0                0.0                  0.0   \n",
       "1                  0.0                0.0                  0.0   \n",
       "2                  0.0                0.0                  0.0   \n",
       "3                  0.0                0.0                  0.0   \n",
       "4                  1.0                0.0                  0.0   \n",
       "\n",
       "   Exterior2nd_Stone  Exterior2nd_Stucco  Exterior2nd_VinylSd  \\\n",
       "0                0.0                 0.0                  1.0   \n",
       "1                0.0                 0.0                  0.0   \n",
       "2                0.0                 0.0                  0.0   \n",
       "3                0.0                 0.0                  0.0   \n",
       "4                0.0                 0.0                  0.0   \n",
       "\n",
       "   Exterior2nd_Wd Sdng  Exterior2nd_Wd Shng  MasVnrType_BrkCmn  \\\n",
       "0                  0.0                  0.0                0.0   \n",
       "1                  0.0                  0.0                0.0   \n",
       "2                  0.0                  0.0                0.0   \n",
       "3                  0.0                  0.0                0.0   \n",
       "4                  0.0                  0.0                0.0   \n",
       "\n",
       "   MasVnrType_BrkFace  MasVnrType_None  MasVnrType_Stone  ExterQual_Ex  \\\n",
       "0                 0.0              1.0               0.0           0.0   \n",
       "1                 0.0              1.0               0.0           0.0   \n",
       "2                 0.0              1.0               0.0           0.0   \n",
       "3                 0.0              1.0               0.0           0.0   \n",
       "4                 1.0              0.0               0.0           1.0   \n",
       "\n",
       "   ExterQual_Fa  ExterQual_Gd  ExterQual_TA  ExterCond_Ex  ExterCond_Fa  \\\n",
       "0           0.0           1.0           0.0           0.0           0.0   \n",
       "1           0.0           0.0           1.0           0.0           0.0   \n",
       "2           0.0           0.0           1.0           0.0           0.0   \n",
       "3           0.0           1.0           0.0           0.0           0.0   \n",
       "4           0.0           0.0           0.0           0.0           0.0   \n",
       "\n",
       "   ExterCond_Gd  ExterCond_TA  Foundation_BrkTil  Foundation_CBlock  \\\n",
       "0           0.0           1.0                0.0                0.0   \n",
       "1           0.0           1.0                0.0                1.0   \n",
       "2           0.0           1.0                0.0                1.0   \n",
       "3           0.0           1.0                0.0                0.0   \n",
       "4           0.0           1.0                0.0                0.0   \n",
       "\n",
       "   Foundation_PConc  Foundation_Stone  Foundation_Wood  BsmtQual_Ex  \\\n",
       "0               1.0               0.0              0.0          0.0   \n",
       "1               0.0               0.0              0.0          0.0   \n",
       "2               0.0               0.0              0.0          0.0   \n",
       "3               1.0               0.0              0.0          0.0   \n",
       "4               1.0               0.0              0.0          1.0   \n",
       "\n",
       "   BsmtQual_Fa  BsmtQual_Gd  BsmtQual_TA  BsmtCond_Fa  BsmtCond_Gd  \\\n",
       "0          0.0          1.0          0.0          0.0          0.0   \n",
       "1          0.0          0.0          1.0          0.0          0.0   \n",
       "2          0.0          1.0          0.0          0.0          0.0   \n",
       "3          0.0          1.0          0.0          0.0          0.0   \n",
       "4          0.0          0.0          0.0          0.0          0.0   \n",
       "\n",
       "   BsmtCond_Po  BsmtCond_TA  BsmtExposure_Av  BsmtExposure_Gd  \\\n",
       "0          0.0          1.0              0.0              0.0   \n",
       "1          0.0          1.0              0.0              0.0   \n",
       "2          0.0          1.0              0.0              0.0   \n",
       "3          0.0          1.0              0.0              0.0   \n",
       "4          0.0          1.0              0.0              0.0   \n",
       "\n",
       "   BsmtExposure_Mn  BsmtExposure_No  BsmtFinType1_ALQ  BsmtFinType1_BLQ  \\\n",
       "0              0.0              1.0               0.0               0.0   \n",
       "1              0.0              1.0               1.0               0.0   \n",
       "2              1.0              0.0               0.0               0.0   \n",
       "3              0.0              1.0               0.0               0.0   \n",
       "4              0.0              1.0               0.0               0.0   \n",
       "\n",
       "   BsmtFinType1_GLQ  BsmtFinType1_LwQ  BsmtFinType1_Rec  BsmtFinType1_Unf  \\\n",
       "0               0.0               0.0               0.0               1.0   \n",
       "1               0.0               0.0               0.0               0.0   \n",
       "2               1.0               0.0               0.0               0.0   \n",
       "3               0.0               0.0               0.0               1.0   \n",
       "4               1.0               0.0               0.0               0.0   \n",
       "\n",
       "   BsmtFinType2_ALQ  BsmtFinType2_BLQ  BsmtFinType2_GLQ  BsmtFinType2_LwQ  \\\n",
       "0               0.0               0.0               0.0               0.0   \n",
       "1               0.0               0.0               0.0               0.0   \n",
       "2               0.0               0.0               0.0               0.0   \n",
       "3               0.0               0.0               0.0               0.0   \n",
       "4               0.0               0.0               0.0               0.0   \n",
       "\n",
       "   BsmtFinType2_Rec  BsmtFinType2_Unf  Heating_GasA  Heating_GasW  \\\n",
       "0               0.0               1.0           1.0           0.0   \n",
       "1               1.0               0.0           1.0           0.0   \n",
       "2               0.0               1.0           1.0           0.0   \n",
       "3               0.0               1.0           1.0           0.0   \n",
       "4               0.0               1.0           1.0           0.0   \n",
       "\n",
       "   Heating_Grav  Heating_OthW  HeatingQC_Ex  HeatingQC_Fa  HeatingQC_Gd  \\\n",
       "0           0.0           0.0           1.0           0.0           0.0   \n",
       "1           0.0           0.0           0.0           0.0           0.0   \n",
       "2           0.0           0.0           0.0           0.0           0.0   \n",
       "3           0.0           0.0           1.0           0.0           0.0   \n",
       "4           0.0           0.0           1.0           0.0           0.0   \n",
       "\n",
       "   HeatingQC_Po  HeatingQC_TA  CentralAir_N  CentralAir_Y  Electrical_FuseA  \\\n",
       "0           0.0           0.0           0.0           1.0               0.0   \n",
       "1           0.0           1.0           0.0           1.0               0.0   \n",
       "2           0.0           1.0           0.0           1.0               0.0   \n",
       "3           0.0           0.0           0.0           1.0               0.0   \n",
       "4           0.0           0.0           0.0           1.0               0.0   \n",
       "\n",
       "   Electrical_FuseF  Electrical_FuseP  Electrical_Mix  Electrical_SBrkr  \\\n",
       "0               0.0               0.0             0.0               1.0   \n",
       "1               0.0               0.0             0.0               1.0   \n",
       "2               0.0               0.0             0.0               1.0   \n",
       "3               0.0               0.0             0.0               1.0   \n",
       "4               0.0               0.0             0.0               1.0   \n",
       "\n",
       "   KitchenQual_Ex  KitchenQual_Fa  KitchenQual_Gd  KitchenQual_TA  \\\n",
       "0             0.0             0.0             1.0             0.0   \n",
       "1             0.0             0.0             0.0             1.0   \n",
       "2             0.0             0.0             1.0             0.0   \n",
       "3             0.0             0.0             1.0             0.0   \n",
       "4             1.0             0.0             0.0             0.0   \n",
       "\n",
       "   Functional_Maj1  Functional_Maj2  Functional_Min1  Functional_Min2  \\\n",
       "0              0.0              0.0              0.0              0.0   \n",
       "1              0.0              0.0              0.0              0.0   \n",
       "2              0.0              0.0              0.0              0.0   \n",
       "3              0.0              0.0              0.0              0.0   \n",
       "4              0.0              0.0              0.0              0.0   \n",
       "\n",
       "   Functional_Mod  Functional_Typ  GarageType_2Types  GarageType_Attchd  \\\n",
       "0             0.0             1.0                0.0                1.0   \n",
       "1             0.0             1.0                0.0                1.0   \n",
       "2             0.0             1.0                0.0                1.0   \n",
       "3             0.0             1.0                0.0                1.0   \n",
       "4             0.0             1.0                0.0                1.0   \n",
       "\n",
       "   GarageType_Basment  GarageType_BuiltIn  GarageType_CarPort  \\\n",
       "0                 0.0                 0.0                 0.0   \n",
       "1                 0.0                 0.0                 0.0   \n",
       "2                 0.0                 0.0                 0.0   \n",
       "3                 0.0                 0.0                 0.0   \n",
       "4                 0.0                 0.0                 0.0   \n",
       "\n",
       "   GarageType_Detchd  GarageFinish_Fin  GarageFinish_RFn  GarageFinish_Unf  \\\n",
       "0                0.0               1.0               0.0               0.0   \n",
       "1                0.0               0.0               1.0               0.0   \n",
       "2                0.0               0.0               0.0               1.0   \n",
       "3                0.0               1.0               0.0               0.0   \n",
       "4                0.0               0.0               1.0               0.0   \n",
       "\n",
       "   GarageQual_Ex  GarageQual_Fa  GarageQual_Gd  GarageQual_Po  GarageQual_TA  \\\n",
       "0            0.0            0.0            0.0            0.0            1.0   \n",
       "1            0.0            0.0            0.0            0.0            1.0   \n",
       "2            0.0            0.0            0.0            0.0            1.0   \n",
       "3            0.0            0.0            0.0            0.0            1.0   \n",
       "4            0.0            0.0            0.0            0.0            1.0   \n",
       "\n",
       "   GarageCond_Ex  GarageCond_Fa  GarageCond_Gd  GarageCond_Po  GarageCond_TA  \\\n",
       "0            0.0            0.0            0.0            0.0            1.0   \n",
       "1            0.0            0.0            0.0            0.0            1.0   \n",
       "2            0.0            0.0            0.0            0.0            1.0   \n",
       "3            0.0            0.0            0.0            0.0            1.0   \n",
       "4            0.0            0.0            0.0            0.0            1.0   \n",
       "\n",
       "   PavedDrive_N  PavedDrive_P  PavedDrive_Y  SaleType_COD  SaleType_CWD  \\\n",
       "0           0.0           0.0           1.0           0.0           0.0   \n",
       "1           0.0           0.0           1.0           0.0           0.0   \n",
       "2           0.0           0.0           1.0           0.0           0.0   \n",
       "3           0.0           0.0           1.0           0.0           0.0   \n",
       "4           0.0           0.0           1.0           0.0           0.0   \n",
       "\n",
       "   SaleType_Con  SaleType_ConLD  SaleType_ConLI  SaleType_ConLw  SaleType_New  \\\n",
       "0           0.0             0.0             0.0             0.0           1.0   \n",
       "1           0.0             0.0             0.0             0.0           0.0   \n",
       "2           0.0             0.0             0.0             0.0           0.0   \n",
       "3           0.0             0.0             0.0             0.0           0.0   \n",
       "4           0.0             0.0             0.0             0.0           0.0   \n",
       "\n",
       "   SaleType_Oth  SaleType_WD  SaleCondition_Abnorml  SaleCondition_AdjLand  \\\n",
       "0           0.0          0.0                    0.0                    0.0   \n",
       "1           0.0          1.0                    0.0                    0.0   \n",
       "2           0.0          1.0                    0.0                    0.0   \n",
       "3           0.0          1.0                    0.0                    0.0   \n",
       "4           0.0          1.0                    0.0                    0.0   \n",
       "\n",
       "   SaleCondition_Alloca  SaleCondition_Family  SaleCondition_Normal  \\\n",
       "0                   0.0                   0.0                   0.0   \n",
       "1                   0.0                   0.0                   1.0   \n",
       "2                   0.0                   0.0                   1.0   \n",
       "3                   0.0                   0.0                   1.0   \n",
       "4                   0.0                   0.0                   1.0   \n",
       "\n",
       "   SaleCondition_Partial  \n",
       "0                    1.0  \n",
       "1                    0.0  \n",
       "2                    0.0  \n",
       "3                    0.0  \n",
       "4                    0.0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ce = CathgoricalEncoder().fit_transform(X_train)\n",
    "ce.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(878, 75)\n"
     ]
    }
   ],
   "source": [
    "oe = OrdinalEncoder()\n",
    "print(X_train.shape)\n",
    "encoded = oe.fit_transform(X_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tsne = TSNE(n_components=2)\n",
    "X_reduced_tsne = tsne.fit_transform(encoded)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.scatter(X_reduced_tsne[:, 0], X_reduced_tsne[:, 1], c=y_train, cmap=\"jet\")\n",
    "plt.colorbar()\n",
    "plt.show()\n",
    "                                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "pca = PCA(n_components=2)\n",
    "X_reduced_pca = pca.fit_transform(encoded)\n",
    "\n",
    "plt.figure(figsize=(12, 12))\n",
    "plt.scatter(X_reduced_pca[:, 0], X_reduced_pca[:, 1], c=y_train, cmap=\"jet\")\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Many vectors of expensive houses concentrated near. The farther from the core of concentration, then home is more expensive. This fact confirm correlation. (For numerical attributes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, y_train = None, None\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    (\"cleaner\", DataCleaner(drop_na, drop_columns)),\n",
    "    (\"labels_splitter\", DataColumnSplitter(\"SalePrice\")),\n",
    "    (\"encoder\", CathgoricalEncoder()),\n",
    "    (\"scaler\", StandardScaler())\n",
    "])\n",
    "\n",
    "X_train = pipeline.fit_transform(train)\n",
    "y_train = pipeline.named_steps[\"labels_splitter\"].column_\n",
    "\n",
    "X_test = pipeline.fit_transform(test)\n",
    "y_test = pipeline.named_steps[\"labels_splitter\"].column_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(878, 259)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(216, 219)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Oh no, we are losing some cathegorical attributes in test set. We have 3 ways: \n",
    "1) Hardcode some magical constant (list with all cathegorical attributes, and fill empty columns zeros)\n",
    "\n",
    "2) Delete cahtegorical attributes with many cathegories. \n",
    "\n",
    "3) Try OriginalEncoder\n",
    "\n",
    "We have wery small dataset and too many attributes. So, at the first lets try to change OneHotEncoder. Many algorithms are sensetives to attributes, for example, a neighbor with a good attribute will also be considered good. So, OneHotEncoder will be better, but, they create many atributes...... So we not have a choise.... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_column(data, column_name):\n",
    "    \"\"\"\n",
    "    Remove column by name\n",
    "    \n",
    "    Parameters\n",
    "    data : list\n",
    "        Column names\n",
    "    column_name : str\n",
    "        Column name that must be deleted \n",
    "        \n",
    "    Returns \n",
    "    list \n",
    "        data without selected name\n",
    "    \"\"\"\n",
    "    if column_name in data:\n",
    "        data.remove(column_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "X_train, y_train = None, None\n",
    "\n",
    "column_names = None\n",
    "\n",
    "best_attributes = get_better_numeric_columns(corr_matrix[\"SalePrice\"], 0.3)\n",
    "remove_column(best_attributes, \"SalePrice\")\n",
    "remove_column(best_attributes, \"Id\")\n",
    "# best_attributes += [\"Neighborhood\", \"GarageType\", \"BsmtQual\", \"WoodDeckSF\", \"MasVnrArea\"]\n",
    "\n",
    "best_attributes += [\"Neighborhood\", \"GarageType\", \"BsmtQual\"]\n",
    "\n",
    "pipeline = Pipeline([\n",
    "    (\"cleaner\", DataCleaner(drop_na, drop_columns)),\n",
    "    (\"labels_splitter\", DataColumnSplitter(\"SalePrice\")),\n",
    "    (\"id_splitter\", DataColumnSplitter(\"Id\")),\n",
    "    (\"best_selecter\", BestAttributesSelector(best_attributes)),\n",
    "    (\"column_names_saver\", ColumnNamesSaver()),\n",
    "    (\"encoder\", OrdinalEncoder()),\n",
    "    (\"scaler\", StandardScaler())\n",
    "])\n",
    "\n",
    "X_train = pipeline.fit_transform(train)\n",
    "y_train = pipeline.named_steps[\"labels_splitter\"].column_\n",
    "\n",
    "X_test, y_test = None, None\n",
    "X_test = pipeline.fit_transform(test)\n",
    "y_test = pipeline.named_steps[\"labels_splitter\"].column_\n",
    "\n",
    "column_names = pipeline.named_steps[\"column_names_saver\"].column_names_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((878, 21), (878,))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((216, 21), (216,))"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Model selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In our dataset we have small amount of instances and attributes, so all models will be fast."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have regression problem, so we need to calculate distance between prediction and label vectors. For this goal we can use Mean Absolute Error, or Root Mean Squared Error. This two functions most useful in regression problems.\n",
    "\n",
    "If we use RMSE, then we must minimize this function, because RMSE return heigher value for large error value (use Euclidian norm).\n",
    "If we know, that dataset have many outlier, that we can use MAE (use Manhettan norm). RMSE more sensitive to outlies taht MAE. \n",
    "\n",
    "Looking at plot with best correlationg features, we can see, that we have some outlies, but not more. So, we will use RMSE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "39404.84690783668"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lin_reg = LinearRegression()\n",
    "lin_reg.fit(X_train, y_train)\n",
    "lin_predictions = lin_reg.predict(X_test)\n",
    "\n",
    "lin_mse = mean_squared_error(lin_predictions, y_test)\n",
    "lin_rmse = np.sqrt(lin_mse)\n",
    "lin_rmse\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, estimator=SGDRegressor(), n_jobs=-1,\n",
       "             param_grid={'eta0': array([0.001 , 0.0011, 0.0012, 0.0013, 0.0014, 0.0015, 0.0016, 0.0017,\n",
       "       0.0018, 0.0019, 0.002 , 0.0021, 0.0022, 0.0023, 0.0024, 0.0025,\n",
       "       0.0026, 0.0027, 0.0028, 0.0029, 0.003 , 0.0031, 0.0032, 0.0033,\n",
       "       0.0034, 0.0035, 0.0036, 0.0037, 0.0038, 0.0039])},\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_reg = SGDRegressor()\n",
    "\n",
    "sgd_params = {\n",
    "    \"eta0\": np.arange(0.001, 0.004, 0.0001)\n",
    "}\n",
    "\n",
    "sgd_grid_search = GridSearchCV(sgd_reg, sgd_params, scoring=\"neg_mean_squared_error\", cv=4, \n",
    "                             return_train_score=True, n_jobs=-1)\n",
    "sgd_grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SGDRegressor(eta0=0.002700000000000001)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sgd_grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36994.72631333194 {'eta0': 0.002700000000000001}\n",
      "37004.31821847448 {'eta0': 0.003200000000000001}\n",
      "37016.111822182225 {'eta0': 0.002800000000000001}\n",
      "37017.06071801622 {'eta0': 0.0039000000000000016}\n",
      "37017.20925029402 {'eta0': 0.003100000000000001}\n",
      "37019.82404425921 {'eta0': 0.0018000000000000004}\n",
      "37022.103826862054 {'eta0': 0.0016000000000000003}\n",
      "37024.38324526432 {'eta0': 0.0022000000000000006}\n",
      "37029.87101654565 {'eta0': 0.0014000000000000002}\n",
      "37030.3860922398 {'eta0': 0.002300000000000001}\n"
     ]
    }
   ],
   "source": [
    "sgd_results = sgd_grid_search.cv_results_\n",
    "for mean, params in sorted(zip(sgd_results[\"mean_test_score\"], sgd_results[\"params\"]), reverse=True, key=lambda x: x[0])[:10]:\n",
    "    print(np.sqrt(-mean), params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "33645.06927132211"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(X_train, y_train)\n",
    "forest_predictions = forest_reg.predict(X_test)\n",
    "\n",
    "forest_mse = mean_squared_error(forest_predictions, y_test)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.58823225, 0.11004969, 0.0082771 , 0.01660683, 0.02723705,\n",
       "       0.02355151, 0.04106352, 0.02031146, 0.01715481, 0.01422024,\n",
       "       0.00875766, 0.01160133, 0.0046584 , 0.03395242, 0.01675045,\n",
       "       0.00652791, 0.02578713, 0.00791443, 0.00830762, 0.00389306,\n",
       "       0.00514512])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64\tOverallQual \t-> 0.5882322514896561\n",
      "int64\tGrLivArea \t-> 0.11004968985362837\n",
      "int64\tFullBath \t-> 0.04106352286343453\n",
      "int64\tBsmtFinSF1 \t-> 0.03395241955154119\n",
      "int64\tTotalBsmtSF \t-> 0.02723705469307356\n",
      "int64\t2ndFlrSF \t-> 0.025787129794985555\n",
      "int64\t1stFlrSF \t-> 0.0235515055510849\n",
      "int64\tTotRmsAbvGrd \t-> 0.02031145953124797\n",
      "int64\tYearBuilt \t-> 0.01715481344790636\n",
      "float64\tLotFrontage \t-> 0.016750445812492843\n",
      "int64\tGarageArea \t-> 0.01660683072203185\n",
      "int64\tYearRemodAdd \t-> 0.014220236499537879\n",
      "float64\tMasVnrArea \t-> 0.011601326974580495\n",
      "float64\tGarageYrBlt \t-> 0.008757660976443107\n",
      "object\tNeighborhood \t-> 0.008307619852309819\n",
      "int64\tGarageCars \t-> 0.008277100861151668\n",
      "int64\tOpenPorchSF \t-> 0.007914431991969203\n",
      "int64\tWoodDeckSF \t-> 0.006527912649930745\n",
      "object\tBsmtQual \t-> 0.005145120543566297\n",
      "int64\tFireplaces \t-> 0.00465840283317669\n",
      "object\tGarageType \t-> 0.0038930635062509166\n"
     ]
    }
   ],
   "source": [
    "dtypes = [str(name) for name in data[column_names].dtypes] \n",
    "\n",
    "for (dtype, name), importance in sorted(\n",
    "                                zip(zip(dtypes, column_names), forest_reg.feature_importances_), \n",
    "                                reverse=True, \n",
    "                                key=lambda x: x[1]):\n",
    "    print(f\"{dtype}\\t{name} \\t-> {importance}\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SalePrice        1.000000\n",
       "OverallQual      0.790982\n",
       "GrLivArea        0.708624\n",
       "GarageCars       0.640409\n",
       "GarageArea       0.623431\n",
       "TotalBsmtSF      0.613581\n",
       "1stFlrSF         0.605852\n",
       "FullBath         0.560664\n",
       "TotRmsAbvGrd     0.533723\n",
       "YearBuilt        0.522897\n",
       "YearRemodAdd     0.507101\n",
       "GarageYrBlt      0.486362\n",
       "MasVnrArea       0.477493\n",
       "Fireplaces       0.466929\n",
       "BsmtFinSF1       0.386420\n",
       "LotFrontage      0.351799\n",
       "WoodDeckSF       0.324413\n",
       "2ndFlrSF         0.319334\n",
       "OpenPorchSF      0.315856\n",
       "HalfBath         0.284108\n",
       "LotArea          0.263843\n",
       "BsmtFullBath     0.227122\n",
       "BsmtUnfSF        0.214479\n",
       "BedroomAbvGr     0.168213\n",
       "ScreenPorch      0.111447\n",
       "PoolArea         0.092404\n",
       "MoSold           0.046432\n",
       "3SsnPorch        0.044584\n",
       "BsmtFinSF2      -0.011378\n",
       "BsmtHalfBath    -0.016844\n",
       "MiscVal         -0.021190\n",
       "Id              -0.021917\n",
       "LowQualFinSF    -0.025606\n",
       "YrSold          -0.028923\n",
       "OverallCond     -0.077856\n",
       "MSSubClass      -0.084284\n",
       "EnclosedPorch   -0.128578\n",
       "KitchenAbvGr    -0.135907\n",
       "Name: SalePrice, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr_matrix[\"SalePrice\"].sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "55807.300247393956"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg = DecisionTreeRegressor()\n",
    "tree_reg.fit(X_train, y_train)\n",
    "tree_predictions = tree_reg.predict(X_test)\n",
    "\n",
    "tree_mse = mean_squared_error(tree_predictions, y_test)\n",
    "tree_rmse = np.sqrt(tree_mse)\n",
    "tree_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, estimator=DecisionTreeRegressor(), n_jobs=-1,\n",
       "             param_grid={'max_depth': array([ 5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),\n",
       "                         'max_features': array([1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       "                         'max_leaf_nodes': array([50, 52, 54, 56, 58, 60, 62, 64, 66, 68, 70, 72, 74, 76, 78, 80, 82,\n",
       "       84, 86, 88, 90, 92, 94, 96, 98]),\n",
       "                         'min_samples_leaf': array([1, 2, 3, 4, 5, 6, 7, 8, 9])},\n",
       "             return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree_reg = DecisionTreeRegressor()\n",
    "\n",
    "tree_params = {\n",
    "    \"min_samples_leaf\": np.arange(1, 10, 1),\n",
    "    \"max_leaf_nodes\": np.arange(50, 100, 2),\n",
    "    \"max_depth\": np.arange(5, 15, 1),\n",
    "    \"max_features\": np.arange(1, 10, 1),\n",
    "    \n",
    "}\n",
    "\n",
    "tree_search = GridSearchCV(tree_reg, tree_params, scoring=\"neg_mean_squared_error\", cv=4, \n",
    "                           return_train_score=True, n_jobs=-1)\n",
    "tree_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "38606.112750110624 {'max_depth': 11, 'max_features': 9, 'max_leaf_nodes': 76, 'min_samples_leaf': 6}\n",
      "38644.470198613475 {'max_depth': 10, 'max_features': 6, 'max_leaf_nodes': 82, 'min_samples_leaf': 8}\n",
      "38718.16504550667 {'max_depth': 12, 'max_features': 9, 'max_leaf_nodes': 76, 'min_samples_leaf': 2}\n",
      "38753.61181033075 {'max_depth': 12, 'max_features': 7, 'max_leaf_nodes': 78, 'min_samples_leaf': 8}\n",
      "38857.20067171636 {'max_depth': 11, 'max_features': 7, 'max_leaf_nodes': 88, 'min_samples_leaf': 4}\n",
      "39003.50273130473 {'max_depth': 7, 'max_features': 9, 'max_leaf_nodes': 52, 'min_samples_leaf': 7}\n",
      "39007.33269771466 {'max_depth': 6, 'max_features': 9, 'max_leaf_nodes': 82, 'min_samples_leaf': 4}\n",
      "39018.18211249786 {'max_depth': 13, 'max_features': 9, 'max_leaf_nodes': 72, 'min_samples_leaf': 9}\n",
      "39074.179146271956 {'max_depth': 7, 'max_features': 8, 'max_leaf_nodes': 94, 'min_samples_leaf': 7}\n",
      "39142.51799601221 {'max_depth': 6, 'max_features': 7, 'max_leaf_nodes': 50, 'min_samples_leaf': 2}\n"
     ]
    }
   ],
   "source": [
    "tree_results = tree_search.cv_results_\n",
    "for neg_mean, params in sorted(zip(tree_results[\"mean_test_score\"], tree_results[\"params\"]), key=lambda x: x[0], reverse=True)[:10]:\n",
    "    print(np.sqrt(-neg_mean), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35542.43865830903"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg = RandomForestRegressor()\n",
    "forest_reg.fit(X_train, y_train)\n",
    "forest_predictions = forest_reg.predict(X_test)\n",
    "\n",
    "forest_mse = mean_squared_error(forest_predictions, y_test)\n",
    "forest_rmse = np.sqrt(forest_mse)\n",
    "forest_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=4, estimator=RandomForestRegressor(), n_iter=1000,\n",
       "                   n_jobs=-1,\n",
       "                   param_distributions={'max_features': array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14]),\n",
       "                                        'max_leaf_nodes': array([ 2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
       "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52,\n",
       "       53, 54, 55, 56, 57, 58, 59, 60...\n",
       "       87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99]),\n",
       "                                        'min_samples_leaf': array([1, 2, 3, 4, 5, 6, 7, 8, 9]),\n",
       "                                        'n_estimators': array([  1,   6,  11,  16,  21,  26,  31,  36,  41,  46,  51,  56,  61,\n",
       "        66,  71,  76,  81,  86,  91,  96, 101, 106, 111, 116, 121, 126,\n",
       "       131, 136, 141, 146, 151, 156, 161, 166, 171, 176, 181, 186, 191,\n",
       "       196, 201, 206, 211, 216, 221, 226, 231, 236, 241, 246, 251, 256,\n",
       "       261, 266, 271, 276, 281, 286, 291, 296])},\n",
       "                   return_train_score=True, scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest_reg = RandomForestRegressor()\n",
    "\n",
    "forest_params = {\n",
    "    \"n_estimators\": np.arange(1, 300, 5),\n",
    "    \"max_features\": np.arange(1, 15, 1),\n",
    "    \"min_samples_leaf\": np.arange(1, 10, 1),\n",
    "    \"max_leaf_nodes\": np.arange(2, 100, 1),\n",
    "}\n",
    "\n",
    "forest_search = RandomizedSearchCV(forest_reg, forest_params, n_iter=1000, scoring=\"neg_mean_squared_error\", cv=4, \n",
    "                             return_train_score=True, n_jobs=-1)\n",
    "forest_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32332.883909712382 {'n_estimators': 211, 'min_samples_leaf': 1, 'max_leaf_nodes': 82, 'max_features': 13}\n",
      "32374.79879517793 {'n_estimators': 36, 'min_samples_leaf': 1, 'max_leaf_nodes': 85, 'max_features': 11}\n",
      "32445.063016777378 {'n_estimators': 71, 'min_samples_leaf': 1, 'max_leaf_nodes': 88, 'max_features': 12}\n",
      "32458.666252668456 {'n_estimators': 166, 'min_samples_leaf': 1, 'max_leaf_nodes': 57, 'max_features': 9}\n",
      "32485.758419087906 {'n_estimators': 266, 'min_samples_leaf': 1, 'max_leaf_nodes': 97, 'max_features': 5}\n",
      "32586.466475484063 {'n_estimators': 91, 'min_samples_leaf': 1, 'max_leaf_nodes': 95, 'max_features': 8}\n",
      "32607.29412467637 {'n_estimators': 246, 'min_samples_leaf': 1, 'max_leaf_nodes': 52, 'max_features': 14}\n",
      "32630.50291754739 {'n_estimators': 131, 'min_samples_leaf': 1, 'max_leaf_nodes': 72, 'max_features': 9}\n",
      "32644.38933035375 {'n_estimators': 66, 'min_samples_leaf': 1, 'max_leaf_nodes': 40, 'max_features': 7}\n",
      "32687.17644824022 {'n_estimators': 261, 'min_samples_leaf': 1, 'max_leaf_nodes': 77, 'max_features': 9}\n"
     ]
    }
   ],
   "source": [
    "forest_results = forest_search.cv_results_\n",
    "for mean, params in sorted(zip(forest_results[\"mean_test_score\"], forest_results[\"params\"]), key=lambda x: x[0], reverse=True)[:10]:\n",
    "    print(np.sqrt(-mean), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "87023.67584778993"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_reg = SVR()\n",
    "svm_reg.fit(X_train, y_train)\n",
    "svm_predictions = svm_reg.predict(X_test)\n",
    "\n",
    "svm_mse = mean_squared_error(svm_predictions, y_test)\n",
    "svm_rmse = np.sqrt(svm_mse)\n",
    "svm_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, estimator=SVR(), n_jobs=-1,\n",
       "             param_grid=[{'C': array([35000, 35010, 35020, 35030, 35040, 35050, 35060, 35070, 35080,\n",
       "       35090, 35100, 35110, 35120, 35130, 35140, 35150, 35160, 35170,\n",
       "       35180, 35190, 35200, 35210, 35220, 35230, 35240, 35250, 35260,\n",
       "       35270, 35280, 35290, 35300, 35310, 35320, 35330, 35340, 35350,\n",
       "       35360, 35370, 35380, 35390, 35400, 35410, 35420, 35430, 35440,\n",
       "       35450, 35460,...\n",
       "       36800, 36810, 36820, 36830, 36840, 36850, 36860, 36870, 36880,\n",
       "       36890, 36900, 36910, 36920, 36930, 36940, 36950, 36960, 36970,\n",
       "       36980, 36990]),\n",
       "                          'kernel': ['linear']},\n",
       "                         {'C': array([9000, 9050, 9100, 9150, 9200, 9250, 9300, 9350, 9400, 9450, 9500,\n",
       "       9550, 9600, 9650, 9700, 9750, 9800, 9850, 9900, 9950]),\n",
       "                          'gamma': array([0.001, 0.006, 0.011, 0.016, 0.021, 0.026]),\n",
       "                          'kernel': ['rbf']}],\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_reg = SVR()\n",
    "\n",
    "svm_params = [\n",
    "        \n",
    "    {\n",
    "        'kernel': ['linear'], \n",
    "        'C': np.arange(35000, 37000, 10)\n",
    "    },\n",
    "        \n",
    "    {\n",
    "        'kernel': ['rbf'], \n",
    "        'C': np.arange(9000, 10000, 50),\n",
    "        'gamma': np.arange(0.001, 0.03, 0.005)\n",
    "    },\n",
    "]\n",
    "\n",
    "svm_search = GridSearchCV(svm_reg, svm_params, cv=4, scoring='neg_mean_squared_error', n_jobs=-1)\n",
    "svm_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "37830.43817857638 {'C': 36990, 'kernel': 'linear'}\n",
      "37830.43822357741 {'C': 36960, 'kernel': 'linear'}\n",
      "37830.438236216556 {'C': 36940, 'kernel': 'linear'}\n",
      "37830.438241229196 {'C': 36950, 'kernel': 'linear'}\n",
      "37830.438280162816 {'C': 36920, 'kernel': 'linear'}\n",
      "37830.43830373701 {'C': 36900, 'kernel': 'linear'}\n",
      "37830.43830694255 {'C': 36880, 'kernel': 'linear'}\n",
      "37830.43834921379 {'C': 36980, 'kernel': 'linear'}\n",
      "37830.438349719756 {'C': 36860, 'kernel': 'linear'}\n",
      "37830.43835137828 {'C': 36970, 'kernel': 'linear'}\n"
     ]
    }
   ],
   "source": [
    "svm_results = svm_search.cv_results_\n",
    "for neg_mean, params in sorted(zip(svm_results[\"mean_test_score\"], svm_results[\"params\"]), reverse=True, key=lambda x: x[0])[:10]:\n",
    "    print(np.sqrt(-neg_mean), params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36360.63444781546"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "svm_reg = SVR(**svm_search.best_params_)\n",
    "tree_reg = DecisionTreeRegressor(**tree_search.best_params_)\n",
    "\n",
    "voting_reg = VotingRegressor(\n",
    "    estimators=[(\"svr\", svm_reg), (\"tree\", tree_reg)]\n",
    ")\n",
    "\n",
    "voting_reg.fit(X_train, y_train)\n",
    "\n",
    "voting_predictions = voting_reg.predict(X_test)\n",
    "voting_mse = mean_squared_error(voting_predictions, y_test)\n",
    "voting_rmse = np.sqrt(voting_mse)\n",
    "voting_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4,\n",
       "             estimator=GradientBoostingRegressor(max_depth=11, max_features=9,\n",
       "                                                 max_leaf_nodes=76,\n",
       "                                                 min_samples_leaf=6),\n",
       "             n_jobs=-1,\n",
       "             param_grid={'learning_rate': array([0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       "                         'n_estimators': array([ 1,  6, 11, 16])},\n",
       "             scoring='neg_mean_squared_error')"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gbrt = GradientBoostingRegressor(**tree_search.best_params_)\n",
    "\n",
    "gbrt_params = {\n",
    "    \"n_estimators\": np.arange(1, 20, 5),\n",
    "    \"learning_rate\": np.arange(0.1, 1.1, 0.1)\n",
    "}\n",
    "\n",
    "gbrt_search = GridSearchCV(gbrt, gbrt_params, cv=4, scoring='neg_mean_squared_error', n_jobs=-1)\n",
    "gbrt_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "33537.89400598131 {'learning_rate': 0.2, 'n_estimators': 16}\n",
      "34344.60063157056 {'learning_rate': 0.2, 'n_estimators': 11}\n",
      "34660.10611490735 {'learning_rate': 0.30000000000000004, 'n_estimators': 11}\n",
      "34728.67668878038 {'learning_rate': 0.4, 'n_estimators': 16}\n",
      "35156.68872817922 {'learning_rate': 0.4, 'n_estimators': 6}\n",
      "35510.783950863624 {'learning_rate': 0.30000000000000004, 'n_estimators': 16}\n",
      "35921.18230331537 {'learning_rate': 0.30000000000000004, 'n_estimators': 6}\n",
      "36614.38298823303 {'learning_rate': 0.1, 'n_estimators': 16}\n",
      "37188.29115126182 {'learning_rate': 0.4, 'n_estimators': 11}\n",
      "37259.31791068092 {'learning_rate': 0.5, 'n_estimators': 6}\n"
     ]
    }
   ],
   "source": [
    "gbrt_results = gbrt_search.cv_results_\n",
    "for neg_mean, params in sorted(zip(gbrt_results[\"mean_test_score\"], gbrt_results[\"params\"]), reverse=True, key=lambda x: x[0])[:10]:\n",
    "    print(np.sqrt(-neg_mean), params)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So, random forest is better estimator."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "best_estimator = forest_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21382.912401353307"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test, y_test = None, None\n",
    "X_test = pipeline.fit_transform(data)\n",
    "y_test = pipeline.named_steps[\"labels_splitter\"].column_\n",
    "\n",
    "best_estimator.fit(X_train, y_train)\n",
    "best_estimator_predictions = best_estimator.predict(X_test)\n",
    "\n",
    "best_estimator_mse = mean_squared_error(best_estimator_predictions, y_test)\n",
    "best_estimator_rmse = np.sqrt(best_estimator_mse)\n",
    "best_estimator_rmse"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       1461\n",
      "1       1462\n",
      "2       1463\n",
      "3       1464\n",
      "4       1465\n",
      "        ... \n",
      "1451    2912\n",
      "1452    2913\n",
      "1455    2916\n",
      "1456    2917\n",
      "1458    2919\n",
      "Name: Id, Length: 1111, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "id_column = None\n",
    "\n",
    "X_final_prepared = pipeline.fit_transform(X_final)\n",
    "id_column = pipeline.named_steps[\"id_splitter\"].column_\n",
    "\n",
    "print(id_column)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([127358.1140728 , 153112.6896274 , 180095.17425648, ...,\n",
       "       108303.41865008, 153866.85982141, 256508.31296077])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_final_pred = best_estimator.predict(X_final_prepared)\n",
    "y_final_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1111,)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_final_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SalePrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1461</td>\n",
       "      <td>127358.114073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1462</td>\n",
       "      <td>153112.689627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1463</td>\n",
       "      <td>180095.174256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1464</td>\n",
       "      <td>181726.435847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1465</td>\n",
       "      <td>216642.003449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1451</th>\n",
       "      <td>2912</td>\n",
       "      <td>152384.004743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1452</th>\n",
       "      <td>2913</td>\n",
       "      <td>110421.163725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1455</th>\n",
       "      <td>2916</td>\n",
       "      <td>108303.418650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1456</th>\n",
       "      <td>2917</td>\n",
       "      <td>153866.859821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1458</th>\n",
       "      <td>2919</td>\n",
       "      <td>256508.312961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1111 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        Id      SalePrice\n",
       "0     1461  127358.114073\n",
       "1     1462  153112.689627\n",
       "2     1463  180095.174256\n",
       "3     1464  181726.435847\n",
       "4     1465  216642.003449\n",
       "...    ...            ...\n",
       "1451  2912  152384.004743\n",
       "1452  2913  110421.163725\n",
       "1455  2916  108303.418650\n",
       "1456  2917  153866.859821\n",
       "1458  2919  256508.312961\n",
       "\n",
       "[1111 rows x 2 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "results = pd.DataFrame({\n",
    "    \"Id\": id_column,\n",
    "    \"SalePrice\": y_final_pred\n",
    "})\n",
    "results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "results.to_csv(\"prediction.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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