ames-housing/2_pairwise_correlations.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pair-wise Correlations\n",
    "\n",
    "The purpose is to identify predictor variables strongly correlated with the sales price and with each other to get an idea of what variables could be good predictors and potential issues with collinearity.\n",
    "\n",
    "Furthermore, Box-Cox transformations and linear combinations of variables are added where applicable or useful."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## \"Housekeeping\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "import json\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.preprocessing import PowerTransformer\n",
    "from tabulate import tabulate\n",
    "\n",
    "from utils import (\n",
    "    ALL_VARIABLES,\n",
    "    CONTINUOUS_VARIABLES,\n",
    "    DISCRETE_VARIABLES,\n",
    "    NUMERIC_VARIABLES,\n",
    "    ORDINAL_VARIABLES,\n",
    "    TARGET_VARIABLES,\n",
    "    encode_ordinals,\n",
    "    load_clean_data,\n",
    "    print_column_list,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option(\"display.max_columns\", 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style(\"white\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the Data\n",
    "\n",
    "Only a subset of the previously cleaned data is used in this analysis. In particular, it does not make sense to calculate correlations involving nominal variables.\n",
    "\n",
    "Furthermore, ordinal variables are encoded as integers (with greater values indicating a higher sales price by \"guts feeling\"; refer to the [data documentation](https://www.amstat.org/publications/jse/v19n3/decock/DataDocumentation.txt) to see the un-encoded values) and take part in the analysis.\n",
    "\n",
    "A `cleaned_df` DataFrame with the original data from the previous notebook is kept so as to restore the encoded ordinal labels again at the end of this notebook for correct storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_df = load_clean_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = cleaned_df[NUMERIC_VARIABLES + ORDINAL_VARIABLES + TARGET_VARIABLES]\n",
    "df = encode_ordinals(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1st Flr SF</th>\n",
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       "      <th>Gr Liv Area</th>\n",
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       "      <th>Lot Area</th>\n",
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       "      <td>31770.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
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       "      <td>62.0</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>1080.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1960</td>\n",
       "      <td>1960</td>\n",
       "      <td>2010</td>\n",
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       "      <td>1968</td>\n",
       "      <td>2010</td>\n",
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      "text/plain": [
       "                 1st Flr SF  2nd Flr SF  3Ssn Porch  Bedroom AbvGr  \\\n",
       "Order PID                                                            \n",
       "1     526301100      1656.0         0.0         0.0              3   \n",
       "2     526350040       896.0         0.0         0.0              2   \n",
       "3     526351010      1329.0         0.0         0.0              3   \n",
       "4     526353030      2110.0         0.0         0.0              3   \n",
       "5     527105010       928.0       701.0         0.0              3   \n",
       "\n",
       "                 Bsmt Full Bath  Bsmt Half Bath  Bsmt Unf SF  BsmtFin SF 1  \\\n",
       "Order PID                                                                    \n",
       "1     526301100               1               0        441.0         639.0   \n",
       "2     526350040               0               0        270.0         468.0   \n",
       "3     526351010               0               0        406.0         923.0   \n",
       "4     526353030               1               0       1045.0        1065.0   \n",
       "5     527105010               0               0        137.0         791.0   \n",
       "\n",
       "                 BsmtFin SF 2  Enclosed Porch  Fireplaces  Full Bath  \\\n",
       "Order PID                                                              \n",
       "1     526301100           0.0             0.0           2          1   \n",
       "2     526350040         144.0             0.0           0          1   \n",
       "3     526351010           0.0             0.0           0          1   \n",
       "4     526353030           0.0             0.0           2          2   \n",
       "5     527105010           0.0             0.0           1          2   \n",
       "\n",
       "                 Garage Area  Garage Cars  Gr Liv Area  Half Bath  \\\n",
       "Order PID                                                           \n",
       "1     526301100        528.0            2       1656.0          0   \n",
       "2     526350040        730.0            1        896.0          0   \n",
       "3     526351010        312.0            1       1329.0          1   \n",
       "4     526353030        522.0            2       2110.0          1   \n",
       "5     527105010        482.0            2       1629.0          1   \n",
       "\n",
       "                 Kitchen AbvGr  Lot Area  Low Qual Fin SF  Mas Vnr Area  \\\n",
       "Order PID                                                                 \n",
       "1     526301100              1   31770.0              0.0         112.0   \n",
       "2     526350040              1   11622.0              0.0           0.0   \n",
       "3     526351010              1   14267.0              0.0         108.0   \n",
       "4     526353030              1   11160.0              0.0           0.0   \n",
       "5     527105010              1   13830.0              0.0           0.0   \n",
       "\n",
       "                 Misc Val  Mo Sold  Open Porch SF  Pool Area  Screen Porch  \\\n",
       "Order PID                                                                    \n",
       "1     526301100       0.0        5           62.0        0.0           0.0   \n",
       "2     526350040       0.0        6            0.0        0.0         120.0   \n",
       "3     526351010   12500.0        6           36.0        0.0           0.0   \n",
       "4     526353030       0.0        4            0.0        0.0           0.0   \n",
       "5     527105010       0.0        3           34.0        0.0           0.0   \n",
       "\n",
       "                 TotRms AbvGrd  Total Bsmt SF  Wood Deck SF  Year Built  \\\n",
       "Order PID                                                                 \n",
       "1     526301100              7         1080.0         210.0        1960   \n",
       "2     526350040              5          882.0         140.0        1961   \n",
       "3     526351010              6         1329.0         393.0        1958   \n",
       "4     526353030              8         2110.0           0.0        1968   \n",
       "5     527105010              6          928.0         212.0        1997   \n",
       "\n",
       "                 Year Remod/Add  Yr Sold  \n",
       "Order PID                                 \n",
       "1     526301100            1960     2010  \n",
       "2     526350040            1961     2010  \n",
       "3     526351010            1958     2010  \n",
       "4     526353030            1968     2010  \n",
       "5     527105010            1998     2010  "
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     "execution_count": 6,
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    "df[NUMERIC_VARIABLES].head()"
   ]
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  {
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       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>526351010</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>526353030</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>527105010</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Bsmt Cond  Bsmt Exposure  Bsmt Qual  BsmtFin Type 1  \\\n",
       "Order PID                                                              \n",
       "1     526301100          4              4          3               4   \n",
       "2     526350040          3              1          3               3   \n",
       "3     526351010          3              1          3               5   \n",
       "4     526353030          3              1          3               5   \n",
       "5     527105010          3              1          4               6   \n",
       "\n",
       "                 BsmtFin Type 2  Electrical  Exter Cond  Exter Qual  Fence  \\\n",
       "Order PID                                                                    \n",
       "1     526301100               1           4           2           2      0   \n",
       "2     526350040               2           4           2           2      3   \n",
       "3     526351010               1           4           2           2      0   \n",
       "4     526353030               1           4           2           3      0   \n",
       "5     527105010               1           4           2           2      3   \n",
       "\n",
       "                 Fireplace Qu  Functional  Garage Cond  Garage Finish  \\\n",
       "Order PID                                                               \n",
       "1     526301100             4           7            3              3   \n",
       "2     526350040             0           7            3              1   \n",
       "3     526351010             0           7            3              1   \n",
       "4     526353030             3           7            3              3   \n",
       "5     527105010             3           7            3              3   \n",
       "\n",
       "                 Garage Qual  Heating QC  Kitchen Qual  Land Slope  Lot Shape  \\\n",
       "Order PID                                                                       \n",
       "1     526301100            3           1             2           2          2   \n",
       "2     526350040            3           2             2           2          3   \n",
       "3     526351010            3           2             3           2          2   \n",
       "4     526353030            3           4             4           2          3   \n",
       "5     527105010            3           3             2           2          2   \n",
       "\n",
       "                 Overall Cond  Overall Qual  Paved Drive  Pool QC  Utilities  \n",
       "Order PID                                                                     \n",
       "1     526301100             4             5            1        0          3  \n",
       "2     526350040             5             4            2        0          3  \n",
       "3     526351010             5             5            2        0          3  \n",
       "4     526353030             4             6            2        0          3  \n",
       "5     527105010             4             4            2        0          3  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[ORDINAL_VARIABLES].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Linearly \"dependent\" Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The \"above grade (ground) living area\" (= *Gr Liv Area*) can be split into 1st and 2nd floor living area plus some undefined rest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert not (\n",
    "    df[\"Gr Liv Area\"]\n",
    "    != (df[\"1st Flr SF\"] + df[\"2nd Flr SF\"] + df[\"Low Qual Fin SF\"])\n",
    ").any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The various basement areas also add up."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "assert not (\n",
    "    df[\"Total Bsmt SF\"]\n",
    "    != (df[\"BsmtFin SF 1\"] + df[\"BsmtFin SF 2\"] + df[\"Bsmt Unf SF\"])\n",
    ").any()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate a variable for the total living area *Total SF* as this is the number communicated most often in housing ads."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Total SF\"] = df[\"Gr Liv Area\"] + df[\"Total Bsmt SF\"]\n",
    "new_variables = [\"Total SF\"]\n",
    "CONTINUOUS_VARIABLES.append(\"Total SF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The different porch areas are unified into a new variable *Total Porch SF*. This potentially helps making the presence of a porch in general relevant in the prediction."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Total Porch SF\"] = (\n",
    "    df[\"3Ssn Porch\"] + df[\"Enclosed Porch\"] + df[\"Open Porch SF\"]\n",
    "    + df[\"Screen Porch\"] + df[\"Wood Deck SF\"]\n",
    ")\n",
    "new_variables.append(\"Total Porch SF\")\n",
    "CONTINUOUS_VARIABLES.append(\"Total Porch SF\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The various types of rooms \"above grade\" (i.e., *TotRms AbvGrd*, *Bedroom AbvGr*, *Kitchen AbvGr*, and *Full Bath*) do not add up (only in 29% of the cases they do). Therefore, no single unified variable can be used as a predictor."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "29"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(\n",
    "    100\n",
    "    * (\n",
    "        df[\"TotRms AbvGrd\"]\n",
    "        == (df[\"Bedroom AbvGr\"] + df[\"Kitchen AbvGr\"] + df[\"Full Bath\"])\n",
    "    ).sum()\n",
    "    / df.shape[0]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Unify the number of various types of bathrooms into a single variable. Note that \"half\" bathrooms are counted as such."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Total Bath\"] = (\n",
    "    df[\"Full Bath\"] + 0.5 * df[\"Half Bath\"]\n",
    "    + df[\"Bsmt Full Bath\"] + 0.5 * df[\"Bsmt Half Bath\"]\n",
    ")\n",
    "new_variables.append(\"Total Bath\")\n",
    "DISCRETE_VARIABLES.append(\"Total Bath\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Box-Cox Transformations\n",
    "\n",
    "Only numeric columns with non-negative values are eligable for a Box-Cox transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st Flr SF   First Floor square feet\n",
      "Gr Liv Area  Above grade (ground) living area square feet\n",
      "Lot Area     Lot size in square feet\n",
      "SalePrice\n",
      "Total SF\n"
     ]
    }
   ],
   "source": [
    "columns = CONTINUOUS_VARIABLES + TARGET_VARIABLES\n",
    "transforms = df[columns].describe().T\n",
    "transforms = list(transforms[transforms['min'] > 0].index)\n",
    "print_column_list(transforms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common convention is to use Box-Cox transformations only if the found lambda value (estimated with Maximum Likelyhood Estimation) is in the range from -3 to +3.\n",
    "\n",
    "Consequently, the only applicable transformation are for *SalePrice* and the new variable *Total SF*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st Flr SF:   use lambda of -0.0\n",
      "Gr Liv Area:  use lambda of -0.0\n",
      "Lot Area:     use lambda of 0.1\n",
      "SalePrice:    use lambda of 0.0\n",
      "Total SF:     use lambda of 0.2\n"
     ]
    }
   ],
   "source": [
    "# Check the Box-Cox tranformations for each column seperately\n",
    "# to decide if the optimal lambda value is in an acceptable range.\n",
    "output = []\n",
    "transformed_columns = []\n",
    "for column in transforms:\n",
    "    X = df[[column]]  # 2D array needed!\n",
    "    pt = PowerTransformer(method=\"box-cox\", standardize=False)\n",
    "    # Suppress a weird but harmless warning from scipy\n",
    "    with warnings.catch_warnings():\n",
    "        warnings.simplefilter(\"ignore\")\n",
    "        pt.fit(X)\n",
    "    # Check if the optimal lambda is ok.\n",
    "    lambda_ = pt.lambdas_[0].round(1)\n",
    "    if -3 <= lambda_ <= 3:\n",
    "        lambda_label = 0 if lambda_ <= 0.01 else lambda_  # to avoid -0.0\n",
    "        new_column = f\"{column} (box-cox-{lambda_label})\"\n",
    "        df[new_column] = (\n",
    "            np.log(X) if lambda_ <= 0.001 else (((X ** lambda_) - 1) / lambda_)\n",
    "        )\n",
    "        # Track the new column in the appropiate list.\n",
    "        new_variables.append(new_column)\n",
    "        if column in TARGET_VARIABLES:\n",
    "            TARGET_VARIABLES.append(new_column)\n",
    "        else:\n",
    "            CONTINUOUS_VARIABLES.append(new_column)\n",
    "        # To show only the transformed columns below.\n",
    "        transformed_columns.append(column)\n",
    "        transformed_columns.append(new_column)\n",
    "        output.append((\n",
    "            f\"{column}:\",\n",
    "            f\"use lambda of {lambda_}\",\n",
    "        ))\n",
    "    else:\n",
    "        output.append((\n",
    "            f\"{column}:\",\n",
    "            f\"lambda of {lambda_} not in realistic range\",\n",
    "        ))\n",
    "print(tabulate(sorted(output), tablefmt=\"plain\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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></th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>1st Flr SF (box-cox-0)</th>\n",
       "      <th>Gr Liv Area</th>\n",
       "      <th>Gr Liv Area (box-cox-0)</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Lot Area (box-cox-0.1)</th>\n",
       "      <th>Total SF</th>\n",
       "      <th>Total SF (box-cox-0.2)</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>SalePrice (box-cox-0)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order</th>\n",
       "      <th>PID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>526301100</th>\n",
       "      <td>1656.0</td>\n",
       "      <td>7.412160</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>7.412160</td>\n",
       "      <td>31770.0</td>\n",
       "      <td>18.196923</td>\n",
       "      <td>2736.0</td>\n",
       "      <td>19.344072</td>\n",
       "      <td>215000.0</td>\n",
       "      <td>12.278393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>526350040</th>\n",
       "      <td>896.0</td>\n",
       "      <td>6.797940</td>\n",
       "      <td>896.0</td>\n",
       "      <td>6.797940</td>\n",
       "      <td>11622.0</td>\n",
       "      <td>15.499290</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>17.333478</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>11.561716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>526351010</th>\n",
       "      <td>1329.0</td>\n",
       "      <td>7.192182</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>7.192182</td>\n",
       "      <td>14267.0</td>\n",
       "      <td>16.027549</td>\n",
       "      <td>2658.0</td>\n",
       "      <td>19.203658</td>\n",
       "      <td>172000.0</td>\n",
       "      <td>12.055250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>526353030</th>\n",
       "      <td>2110.0</td>\n",
       "      <td>7.654443</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>7.654443</td>\n",
       "      <td>11160.0</td>\n",
       "      <td>15.396064</td>\n",
       "      <td>4220.0</td>\n",
       "      <td>21.548042</td>\n",
       "      <td>244000.0</td>\n",
       "      <td>12.404924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>527105010</th>\n",
       "      <td>928.0</td>\n",
       "      <td>6.833032</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>7.395722</td>\n",
       "      <td>13830.0</td>\n",
       "      <td>15.946705</td>\n",
       "      <td>2557.0</td>\n",
       "      <td>19.016856</td>\n",
       "      <td>189900.0</td>\n",
       "      <td>12.154253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 1st Flr SF  1st Flr SF (box-cox-0)  Gr Liv Area  \\\n",
       "Order PID                                                          \n",
       "1     526301100      1656.0                7.412160       1656.0   \n",
       "2     526350040       896.0                6.797940        896.0   \n",
       "3     526351010      1329.0                7.192182       1329.0   \n",
       "4     526353030      2110.0                7.654443       2110.0   \n",
       "5     527105010       928.0                6.833032       1629.0   \n",
       "\n",
       "                 Gr Liv Area (box-cox-0)  Lot Area  Lot Area (box-cox-0.1)  \\\n",
       "Order PID                                                                    \n",
       "1     526301100                 7.412160   31770.0               18.196923   \n",
       "2     526350040                 6.797940   11622.0               15.499290   \n",
       "3     526351010                 7.192182   14267.0               16.027549   \n",
       "4     526353030                 7.654443   11160.0               15.396064   \n",
       "5     527105010                 7.395722   13830.0               15.946705   \n",
       "\n",
       "                 Total SF  Total SF (box-cox-0.2)  SalePrice  \\\n",
       "Order PID                                                      \n",
       "1     526301100    2736.0               19.344072   215000.0   \n",
       "2     526350040    1778.0               17.333478   105000.0   \n",
       "3     526351010    2658.0               19.203658   172000.0   \n",
       "4     526353030    4220.0               21.548042   244000.0   \n",
       "5     527105010    2557.0               19.016856   189900.0   \n",
       "\n",
       "                 SalePrice (box-cox-0)  \n",
       "Order PID                               \n",
       "1     526301100              12.278393  \n",
       "2     526350040              11.561716  \n",
       "3     526351010              12.055250  \n",
       "4     526353030              12.404924  \n",
       "5     527105010              12.154253  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[transformed_columns].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlations\n",
    "\n",
    "The pair-wise correlations are calculated based on the type of the variables:\n",
    "- **continuous** variables are assumed to be linearly related with the target and each other or not: use **Pearson's correlation coefficient**\n",
    "- **discrete** (because of the low number of distinct realizations as seen in the data cleaning notebook) and **ordinal** (low number of distinct realizations as well) variables are assumed to be related in a monotonic way with the target and each other or not: use **Spearman's rank correlation coefficient**\n",
    "\n",
    "Furthermore, for a **naive feature selection** a \"rule of thumb\" classification in *weak* and *strong* correlation is applied to the predictor variables. The identified variables will be used in the prediction modelling part to speed up the feature selection. A correlation between 0.33 and 0.66 is considered *weak* while a correlation above 0.66 is considered *strong* (these thresholds refer to the absolute value of the correlation). Correlations are calculated for **each** target variable (i.e., raw \"SalePrice\" and Box-Cox transformation thereof). Correlations below 0.1 are considered \"uncorrelated\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "strong = 0.66\n",
    "weak = 0.33\n",
    "uncorrelated = 0.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two heatmaps below (implemented in the reusable `plot_correlation` function) help visualize the correlations.\n",
    "\n",
    "Obviously, many variables are pair-wise correlated. This could yield regression coefficients *inprecise* and not usable / interpretable. At the same time, this does not lower the predictive power of a model as a whole. In contrast to the pair-wise correlations, *multi-collinearity* is not checked here."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_correlation(data, title):\n",
    "    \"\"\"Visualize a correlation matrix in a nice heatmap.\"\"\"\n",
    "    fig, ax = plt.subplots(figsize=(12, 12))\n",
    "    ax.set_title(title, fontsize=24)\n",
    "    # Blank out the upper triangular part of the matrix.\n",
    "    mask = np.zeros_like(data, dtype=np.bool)\n",
    "    mask[np.triu_indices_from(mask)] = True\n",
    "    # Use a diverging color map.\n",
    "    cmap = sns.diverging_palette(240, 0, as_cmap=True)\n",
    "    # Adjust the labels' font size.\n",
    "    labels = data.columns\n",
    "    ax.set_xticklabels(labels, fontsize=10)\n",
    "    ax.set_yticklabels(labels, fontsize=10)\n",
    "    # Plot it.\n",
    "    sns.heatmap(\n",
    "        data, vmin=-1, vmax=1, cmap=cmap, center=0, linewidths=.5,\n",
    "        cbar_kws={\"shrink\": .5}, square=True, mask=mask, ax=ax\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pearson\n",
    "\n",
    "Pearson's correlation coefficient shows a linear relationship between two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = CONTINUOUS_VARIABLES + TARGET_VARIABLES\n",
    "pearson = df[columns].corr(method=\"pearson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_correlation(pearson, \"Pearson's Correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictors weakly or strongly correlated with a target variable are collected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "pearson_weakly_correlated = set()\n",
    "pearson_strongly_correlated = set()\n",
    "pearson_uncorrelated = set()\n",
    "# Iterate over the raw and transformed target.\n",
    "for target in TARGET_VARIABLES:\n",
    "    corrs = pearson.loc[target].drop(TARGET_VARIABLES).abs()\n",
    "    pearson_weakly_correlated |= set(corrs[(weak < corrs) & (corrs <= strong)].index)\n",
    "    pearson_strongly_correlated |= set(corrs[(strong < corrs)].index)\n",
    "    pearson_uncorrelated |= set(corrs[(corrs < uncorrelated)].index)\n",
    "# Show that no contradiction exists between the classifications.\n",
    "assert pearson_weakly_correlated & pearson_strongly_correlated == set()\n",
    "assert pearson_weakly_correlated & pearson_uncorrelated == set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the continuous variables that are weakly and strongly correlated with the sales price or uncorrelated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3Ssn Porch       Three season porch area in square feet\n",
      "BsmtFin SF 2     Type 2 finished square feet\n",
      "Low Qual Fin SF  Low quality finished square feet (all floors)\n",
      "Misc Val         $Value of miscellaneous feature\n",
      "Pool Area        Pool area in square feet\n"
     ]
    }
   ],
   "source": [
    "print_column_list(pearson_uncorrelated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1st Flr SF              First Floor square feet\n",
      "1st Flr SF (box-cox-0)\n",
      "BsmtFin SF 1            Type 1 finished square feet\n",
      "Garage Area             Size of garage in square feet\n",
      "Lot Area (box-cox-0.1)\n",
      "Mas Vnr Area            Masonry veneer area in square feet\n",
      "Total Bsmt SF           Total square feet of basement area\n",
      "Total Porch SF\n",
      "Wood Deck SF            Wood deck area in square feet\n"
     ]
    }
   ],
   "source": [
    "print_column_list(pearson_weakly_correlated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gr Liv Area              Above grade (ground) living area square feet\n",
      "Gr Liv Area (box-cox-0)\n",
      "Total SF\n",
      "Total SF (box-cox-0.2)\n"
     ]
    }
   ],
   "source": [
    "print_column_list(pearson_strongly_correlated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Spearman\n",
    "\n",
    "Spearman's correlation coefficient shows an ordinal rank relationship between two variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = sorted(DISCRETE_VARIABLES + ORDINAL_VARIABLES) + TARGET_VARIABLES\n",
    "spearman = df[columns].corr(method=\"spearman\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_correlation(spearman, \"Spearman's Rank Correlation\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Predictors weakly or strongly correlated with a target variable are collected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "spearman_weakly_correlated = set()\n",
    "spearman_strongly_correlated = set()\n",
    "spearman_uncorrelated = set()\n",
    "# Iterate over the raw and transformed target.\n",
    "for target in TARGET_VARIABLES:\n",
    "    corrs = spearman.loc[target].drop(TARGET_VARIABLES).abs()\n",
    "    spearman_weakly_correlated |= set(corrs[(weak < corrs) & (corrs <= strong)].index)\n",
    "    spearman_strongly_correlated |= set(corrs[(strong < corrs)].index)\n",
    "    spearman_uncorrelated |= set(corrs[(corrs < uncorrelated)].index)\n",
    "# Show that no contradiction exists between the classifications.\n",
    "assert spearman_weakly_correlated & spearman_strongly_correlated == set()\n",
    "assert spearman_weakly_correlated & spearman_uncorrelated == set()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Show the discrete and ordinal variables that are weakly and strongly correlated with the sales price or uncorrelated."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bsmt Half Bath  Basement half bathrooms\n",
      "BsmtFin Type 2  Rating of basement finished area (if multiple types)\n",
      "Exter Cond      Evaluates the present condition of the material on the exterior\n",
      "Land Slope      Slope of property\n",
      "Mo Sold         Month Sold (MM)\n",
      "Pool QC         Pool quality\n",
      "Utilities       Type of utilities available\n",
      "Yr Sold         Year Sold (YYYY)\n"
     ]
    }
   ],
   "source": [
    "print_column_list(spearman_uncorrelated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bsmt Exposure   Refers to walkout or garden level walls\n",
      "BsmtFin Type 1  Rating of basement finished area\n",
      "Fireplace Qu    Fireplace quality\n",
      "Fireplaces      Number of fireplaces\n",
      "Full Bath       Full bathrooms above grade\n",
      "Garage Cond     Garage condition\n",
      "Garage Finish   Interior finish of the garage\n",
      "Garage Qual     Garage quality\n",
      "Half Bath       Half baths above grade\n",
      "Heating QC      Heating quality and condition\n",
      "Lot Shape       General shape of property\n",
      "Paved Drive     Paved driveway\n",
      "TotRms AbvGrd   Total rooms above grade (does not include bathrooms)\n",
      "Year Remod/Add  Remodel date (same as construction date if no remodeling or additions)\n"
     ]
    }
   ],
   "source": [
    "print_column_list(spearman_weakly_correlated)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bsmt Qual     Evaluates the height of the basement\n",
      "Exter Qual    Evaluates the quality of the material on the exterior\n",
      "Garage Cars   Size of garage in car capacity\n",
      "Kitchen Qual  Kitchen quality\n",
      "Overall Qual  Rates the overall material and finish of the house\n",
      "Total Bath\n",
      "Year Built    Original construction date\n"
     ]
    }
   ],
   "source": [
    "print_column_list(spearman_strongly_correlated)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the Results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the weakly and strongly correlated Variables\n",
    "\n",
    "The subset of variables that have a correlation with the house price are saved in a simple JSON file for easy re-use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\"data/correlated_variables.json\", \"w\") as file:\n",
    "    file.write(json.dumps({\n",
    "        \"uncorrelated\": sorted(\n",
    "            list(pearson_uncorrelated) + list(spearman_uncorrelated)\n",
    "        ),\n",
    "        \"weakly_correlated\": sorted(\n",
    "            list(pearson_weakly_correlated) + list(spearman_weakly_correlated)\n",
    "        ),\n",
    "        \"strongly_correlated\": sorted(\n",
    "            list(pearson_strongly_correlated) + list(spearman_strongly_correlated)\n",
    "        ),\n",
    "    }))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save the Data\n",
    "\n",
    "Sort the new variables into the unprocessed `cleaned_df` DataFrame with the targets at the end. This \"restores\" the ordinal labels again for storage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "for column in new_variables:\n",
    "    cleaned_df[column] = df[column]\n",
    "for target in set(TARGET_VARIABLES) & set(new_variables):\n",
    "    new_variables.remove(target)\n",
    "cleaned_df = cleaned_df[sorted(ALL_VARIABLES + new_variables) + TARGET_VARIABLES]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In totality, this notebook added two new linear combinations and one Box-Cox transformation to the previous 78 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2898, 86)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>1st Flr SF</th>\n",
       "      <th>1st Flr SF (box-cox-0)</th>\n",
       "      <th>2nd Flr SF</th>\n",
       "      <th>3Ssn Porch</th>\n",
       "      <th>Alley</th>\n",
       "      <th>Bedroom AbvGr</th>\n",
       "      <th>Bldg Type</th>\n",
       "      <th>Bsmt Cond</th>\n",
       "      <th>Bsmt Exposure</th>\n",
       "      <th>Bsmt Full Bath</th>\n",
       "      <th>Bsmt Half Bath</th>\n",
       "      <th>Bsmt Qual</th>\n",
       "      <th>Bsmt Unf SF</th>\n",
       "      <th>BsmtFin SF 1</th>\n",
       "      <th>BsmtFin SF 2</th>\n",
       "      <th>BsmtFin Type 1</th>\n",
       "      <th>BsmtFin Type 2</th>\n",
       "      <th>Central Air</th>\n",
       "      <th>Condition 1</th>\n",
       "      <th>Condition 2</th>\n",
       "      <th>Electrical</th>\n",
       "      <th>Enclosed Porch</th>\n",
       "      <th>Exter Cond</th>\n",
       "      <th>Exter Qual</th>\n",
       "      <th>Exterior 1st</th>\n",
       "      <th>Exterior 2nd</th>\n",
       "      <th>Fence</th>\n",
       "      <th>Fireplace Qu</th>\n",
       "      <th>Fireplaces</th>\n",
       "      <th>Foundation</th>\n",
       "      <th>Full Bath</th>\n",
       "      <th>Functional</th>\n",
       "      <th>Garage Area</th>\n",
       "      <th>Garage Cars</th>\n",
       "      <th>Garage Cond</th>\n",
       "      <th>Garage Finish</th>\n",
       "      <th>Garage Qual</th>\n",
       "      <th>Garage Type</th>\n",
       "      <th>Gr Liv Area</th>\n",
       "      <th>Gr Liv Area (box-cox-0)</th>\n",
       "      <th>Half Bath</th>\n",
       "      <th>Heating</th>\n",
       "      <th>Heating QC</th>\n",
       "      <th>House Style</th>\n",
       "      <th>Kitchen AbvGr</th>\n",
       "      <th>Kitchen Qual</th>\n",
       "      <th>Land Contour</th>\n",
       "      <th>Land Slope</th>\n",
       "      <th>Lot Area</th>\n",
       "      <th>Lot Area (box-cox-0.1)</th>\n",
       "      <th>Lot Config</th>\n",
       "      <th>Lot Shape</th>\n",
       "      <th>Low Qual Fin SF</th>\n",
       "      <th>MS SubClass</th>\n",
       "      <th>MS Zoning</th>\n",
       "      <th>Mas Vnr Area</th>\n",
       "      <th>Mas Vnr Type</th>\n",
       "      <th>Misc Feature</th>\n",
       "      <th>Misc Val</th>\n",
       "      <th>Mo Sold</th>\n",
       "      <th>Neighborhood</th>\n",
       "      <th>Open Porch SF</th>\n",
       "      <th>Overall Cond</th>\n",
       "      <th>Overall Qual</th>\n",
       "      <th>Paved Drive</th>\n",
       "      <th>Pool Area</th>\n",
       "      <th>Pool QC</th>\n",
       "      <th>Roof Matl</th>\n",
       "      <th>Roof Style</th>\n",
       "      <th>Sale Condition</th>\n",
       "      <th>Sale Type</th>\n",
       "      <th>Screen Porch</th>\n",
       "      <th>Street</th>\n",
       "      <th>TotRms AbvGrd</th>\n",
       "      <th>Total Bath</th>\n",
       "      <th>Total Bsmt SF</th>\n",
       "      <th>Total Porch SF</th>\n",
       "      <th>Total SF</th>\n",
       "      <th>Total SF (box-cox-0.2)</th>\n",
       "      <th>Utilities</th>\n",
       "      <th>Wood Deck SF</th>\n",
       "      <th>Year Built</th>\n",
       "      <th>Year Remod/Add</th>\n",
       "      <th>Yr Sold</th>\n",
       "      <th>SalePrice</th>\n",
       "      <th>SalePrice (box-cox-0)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Order</th>\n",
       "      <th>PID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <th>526301100</th>\n",
       "      <td>1656.0</td>\n",
       "      <td>7.412160</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Gd</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>441.0</td>\n",
       "      <td>639.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>BLQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>Y</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>Plywood</td>\n",
       "      <td>NA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>2</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>1</td>\n",
       "      <td>Typ</td>\n",
       "      <td>528.0</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Fin</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1656.0</td>\n",
       "      <td>7.412160</td>\n",
       "      <td>0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Fa</td>\n",
       "      <td>1Story</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>31770.0</td>\n",
       "      <td>18.196923</td>\n",
       "      <td>Corner</td>\n",
       "      <td>IR1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>020</td>\n",
       "      <td>RL</td>\n",
       "      <td>112.0</td>\n",
       "      <td>Stone</td>\n",
       "      <td>NA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>Names</td>\n",
       "      <td>62.0</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>P</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Hip</td>\n",
       "      <td>Normal</td>\n",
       "      <td>WD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1080.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>2736.0</td>\n",
       "      <td>19.344072</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>210.0</td>\n",
       "      <td>1960</td>\n",
       "      <td>1960</td>\n",
       "      <td>2010</td>\n",
       "      <td>215000.0</td>\n",
       "      <td>12.278393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>526350040</th>\n",
       "      <td>896.0</td>\n",
       "      <td>6.797940</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>2</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>270.0</td>\n",
       "      <td>468.0</td>\n",
       "      <td>144.0</td>\n",
       "      <td>Rec</td>\n",
       "      <td>LwQ</td>\n",
       "      <td>Y</td>\n",
       "      <td>Feedr</td>\n",
       "      <td>Norm</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>NA</td>\n",
       "      <td>0</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>1</td>\n",
       "      <td>Typ</td>\n",
       "      <td>730.0</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>896.0</td>\n",
       "      <td>6.797940</td>\n",
       "      <td>0</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>1Story</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>11622.0</td>\n",
       "      <td>15.499290</td>\n",
       "      <td>Inside</td>\n",
       "      <td>Reg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>020</td>\n",
       "      <td>RH</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Names</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Gable</td>\n",
       "      <td>Normal</td>\n",
       "      <td>WD</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>5</td>\n",
       "      <td>1.0</td>\n",
       "      <td>882.0</td>\n",
       "      <td>260.0</td>\n",
       "      <td>1778.0</td>\n",
       "      <td>17.333478</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>140.0</td>\n",
       "      <td>1961</td>\n",
       "      <td>1961</td>\n",
       "      <td>2010</td>\n",
       "      <td>105000.0</td>\n",
       "      <td>11.561716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>526351010</th>\n",
       "      <td>1329.0</td>\n",
       "      <td>7.192182</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>406.0</td>\n",
       "      <td>923.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>Y</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>Wd Sdng</td>\n",
       "      <td>NA</td>\n",
       "      <td>NA</td>\n",
       "      <td>0</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>1</td>\n",
       "      <td>Typ</td>\n",
       "      <td>312.0</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Unf</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>7.192182</td>\n",
       "      <td>1</td>\n",
       "      <td>GasA</td>\n",
       "      <td>TA</td>\n",
       "      <td>1Story</td>\n",
       "      <td>1</td>\n",
       "      <td>Gd</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>14267.0</td>\n",
       "      <td>16.027549</td>\n",
       "      <td>Corner</td>\n",
       "      <td>IR1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>020</td>\n",
       "      <td>RL</td>\n",
       "      <td>108.0</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>Gar2</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>6</td>\n",
       "      <td>Names</td>\n",
       "      <td>36.0</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Hip</td>\n",
       "      <td>Normal</td>\n",
       "      <td>WD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>6</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1329.0</td>\n",
       "      <td>429.0</td>\n",
       "      <td>2658.0</td>\n",
       "      <td>19.203658</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>393.0</td>\n",
       "      <td>1958</td>\n",
       "      <td>1958</td>\n",
       "      <td>2010</td>\n",
       "      <td>172000.0</td>\n",
       "      <td>12.055250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>526353030</th>\n",
       "      <td>2110.0</td>\n",
       "      <td>7.654443</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>TA</td>\n",
       "      <td>1045.0</td>\n",
       "      <td>1065.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>ALQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>Y</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>BrkFace</td>\n",
       "      <td>NA</td>\n",
       "      <td>TA</td>\n",
       "      <td>2</td>\n",
       "      <td>CBlock</td>\n",
       "      <td>2</td>\n",
       "      <td>Typ</td>\n",
       "      <td>522.0</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Fin</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>7.654443</td>\n",
       "      <td>1</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Ex</td>\n",
       "      <td>1Story</td>\n",
       "      <td>1</td>\n",
       "      <td>Ex</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>11160.0</td>\n",
       "      <td>15.396064</td>\n",
       "      <td>Corner</td>\n",
       "      <td>Reg</td>\n",
       "      <td>0.0</td>\n",
       "      <td>020</td>\n",
       "      <td>RL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4</td>\n",
       "      <td>Names</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Hip</td>\n",
       "      <td>Normal</td>\n",
       "      <td>WD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>8</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2110.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4220.0</td>\n",
       "      <td>21.548042</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1968</td>\n",
       "      <td>1968</td>\n",
       "      <td>2010</td>\n",
       "      <td>244000.0</td>\n",
       "      <td>12.404924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>527105010</th>\n",
       "      <td>928.0</td>\n",
       "      <td>6.833032</td>\n",
       "      <td>701.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>3</td>\n",
       "      <td>1Fam</td>\n",
       "      <td>TA</td>\n",
       "      <td>No</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Gd</td>\n",
       "      <td>137.0</td>\n",
       "      <td>791.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>GLQ</td>\n",
       "      <td>Unf</td>\n",
       "      <td>Y</td>\n",
       "      <td>Norm</td>\n",
       "      <td>Norm</td>\n",
       "      <td>SBrkr</td>\n",
       "      <td>0.0</td>\n",
       "      <td>TA</td>\n",
       "      <td>TA</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>VinylSd</td>\n",
       "      <td>MnPrv</td>\n",
       "      <td>TA</td>\n",
       "      <td>1</td>\n",
       "      <td>PConc</td>\n",
       "      <td>2</td>\n",
       "      <td>Typ</td>\n",
       "      <td>482.0</td>\n",
       "      <td>2</td>\n",
       "      <td>TA</td>\n",
       "      <td>Fin</td>\n",
       "      <td>TA</td>\n",
       "      <td>Attchd</td>\n",
       "      <td>1629.0</td>\n",
       "      <td>7.395722</td>\n",
       "      <td>1</td>\n",
       "      <td>GasA</td>\n",
       "      <td>Gd</td>\n",
       "      <td>2Story</td>\n",
       "      <td>1</td>\n",
       "      <td>TA</td>\n",
       "      <td>Lvl</td>\n",
       "      <td>Gtl</td>\n",
       "      <td>13830.0</td>\n",
       "      <td>15.946705</td>\n",
       "      <td>Inside</td>\n",
       "      <td>IR1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>060</td>\n",
       "      <td>RL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>None</td>\n",
       "      <td>NA</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>Gilbert</td>\n",
       "      <td>34.0</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>Y</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NA</td>\n",
       "      <td>CompShg</td>\n",
       "      <td>Gable</td>\n",
       "      <td>Normal</td>\n",
       "      <td>WD</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Pave</td>\n",
       "      <td>6</td>\n",
       "      <td>2.5</td>\n",
       "      <td>928.0</td>\n",
       "      <td>246.0</td>\n",
       "      <td>2557.0</td>\n",
       "      <td>19.016856</td>\n",
       "      <td>AllPub</td>\n",
       "      <td>212.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>1998</td>\n",
       "      <td>2010</td>\n",
       "      <td>189900.0</td>\n",
       "      <td>12.154253</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 1st Flr SF  1st Flr SF (box-cox-0)  2nd Flr SF  3Ssn Porch  \\\n",
       "Order PID                                                                     \n",
       "1     526301100      1656.0                7.412160         0.0         0.0   \n",
       "2     526350040       896.0                6.797940         0.0         0.0   \n",
       "3     526351010      1329.0                7.192182         0.0         0.0   \n",
       "4     526353030      2110.0                7.654443         0.0         0.0   \n",
       "5     527105010       928.0                6.833032       701.0         0.0   \n",
       "\n",
       "                Alley  Bedroom AbvGr Bldg Type Bsmt Cond Bsmt Exposure  \\\n",
       "Order PID                                                                \n",
       "1     526301100    NA              3      1Fam        Gd            Gd   \n",
       "2     526350040    NA              2      1Fam        TA            No   \n",
       "3     526351010    NA              3      1Fam        TA            No   \n",
       "4     526353030    NA              3      1Fam        TA            No   \n",
       "5     527105010    NA              3      1Fam        TA            No   \n",
       "\n",
       "                 Bsmt Full Bath  Bsmt Half Bath Bsmt Qual  Bsmt Unf SF  \\\n",
       "Order PID                                                                \n",
       "1     526301100               1               0        TA        441.0   \n",
       "2     526350040               0               0        TA        270.0   \n",
       "3     526351010               0               0        TA        406.0   \n",
       "4     526353030               1               0        TA       1045.0   \n",
       "5     527105010               0               0        Gd        137.0   \n",
       "\n",
       "                 BsmtFin SF 1  BsmtFin SF 2 BsmtFin Type 1 BsmtFin Type 2  \\\n",
       "Order PID                                                                   \n",
       "1     526301100         639.0           0.0            BLQ            Unf   \n",
       "2     526350040         468.0         144.0            Rec            LwQ   \n",
       "3     526351010         923.0           0.0            ALQ            Unf   \n",
       "4     526353030        1065.0           0.0            ALQ            Unf   \n",
       "5     527105010         791.0           0.0            GLQ            Unf   \n",
       "\n",
       "                Central Air Condition 1 Condition 2 Electrical  \\\n",
       "Order PID                                                        \n",
       "1     526301100           Y        Norm        Norm      SBrkr   \n",
       "2     526350040           Y       Feedr        Norm      SBrkr   \n",
       "3     526351010           Y        Norm        Norm      SBrkr   \n",
       "4     526353030           Y        Norm        Norm      SBrkr   \n",
       "5     527105010           Y        Norm        Norm      SBrkr   \n",
       "\n",
       "                 Enclosed Porch Exter Cond Exter Qual Exterior 1st  \\\n",
       "Order PID                                                            \n",
       "1     526301100             0.0         TA         TA      BrkFace   \n",
       "2     526350040             0.0         TA         TA      VinylSd   \n",
       "3     526351010             0.0         TA         TA      Wd Sdng   \n",
       "4     526353030             0.0         TA         Gd      BrkFace   \n",
       "5     527105010             0.0         TA         TA      VinylSd   \n",
       "\n",
       "                Exterior 2nd  Fence Fireplace Qu  Fireplaces Foundation  \\\n",
       "Order PID                                                                 \n",
       "1     526301100      Plywood     NA           Gd           2     CBlock   \n",
       "2     526350040      VinylSd  MnPrv           NA           0     CBlock   \n",
       "3     526351010      Wd Sdng     NA           NA           0     CBlock   \n",
       "4     526353030      BrkFace     NA           TA           2     CBlock   \n",
       "5     527105010      VinylSd  MnPrv           TA           1      PConc   \n",
       "\n",
       "                 Full Bath Functional  Garage Area  Garage Cars Garage Cond  \\\n",
       "Order PID                                                                     \n",
       "1     526301100          1        Typ        528.0            2          TA   \n",
       "2     526350040          1        Typ        730.0            1          TA   \n",
       "3     526351010          1        Typ        312.0            1          TA   \n",
       "4     526353030          2        Typ        522.0            2          TA   \n",
       "5     527105010          2        Typ        482.0            2          TA   \n",
       "\n",
       "                Garage Finish Garage Qual Garage Type  Gr Liv Area  \\\n",
       "Order PID                                                            \n",
       "1     526301100           Fin          TA      Attchd       1656.0   \n",
       "2     526350040           Unf          TA      Attchd        896.0   \n",
       "3     526351010           Unf          TA      Attchd       1329.0   \n",
       "4     526353030           Fin          TA      Attchd       2110.0   \n",
       "5     527105010           Fin          TA      Attchd       1629.0   \n",
       "\n",
       "                 Gr Liv Area (box-cox-0)  Half Bath Heating Heating QC  \\\n",
       "Order PID                                                                \n",
       "1     526301100                 7.412160          0    GasA         Fa   \n",
       "2     526350040                 6.797940          0    GasA         TA   \n",
       "3     526351010                 7.192182          1    GasA         TA   \n",
       "4     526353030                 7.654443          1    GasA         Ex   \n",
       "5     527105010                 7.395722          1    GasA         Gd   \n",
       "\n",
       "                House Style  Kitchen AbvGr Kitchen Qual Land Contour  \\\n",
       "Order PID                                                              \n",
       "1     526301100      1Story              1           TA          Lvl   \n",
       "2     526350040      1Story              1           TA          Lvl   \n",
       "3     526351010      1Story              1           Gd          Lvl   \n",
       "4     526353030      1Story              1           Ex          Lvl   \n",
       "5     527105010      2Story              1           TA          Lvl   \n",
       "\n",
       "                Land Slope  Lot Area  Lot Area (box-cox-0.1) Lot Config  \\\n",
       "Order PID                                                                 \n",
       "1     526301100        Gtl   31770.0               18.196923     Corner   \n",
       "2     526350040        Gtl   11622.0               15.499290     Inside   \n",
       "3     526351010        Gtl   14267.0               16.027549     Corner   \n",
       "4     526353030        Gtl   11160.0               15.396064     Corner   \n",
       "5     527105010        Gtl   13830.0               15.946705     Inside   \n",
       "\n",
       "                Lot Shape  Low Qual Fin SF MS SubClass MS Zoning  \\\n",
       "Order PID                                                          \n",
       "1     526301100       IR1              0.0         020        RL   \n",
       "2     526350040       Reg              0.0         020        RH   \n",
       "3     526351010       IR1              0.0         020        RL   \n",
       "4     526353030       Reg              0.0         020        RL   \n",
       "5     527105010       IR1              0.0         060        RL   \n",
       "\n",
       "                 Mas Vnr Area Mas Vnr Type Misc Feature  Misc Val  Mo Sold  \\\n",
       "Order PID                                                                    \n",
       "1     526301100         112.0        Stone           NA       0.0        5   \n",
       "2     526350040           0.0         None           NA       0.0        6   \n",
       "3     526351010         108.0      BrkFace         Gar2   12500.0        6   \n",
       "4     526353030           0.0         None           NA       0.0        4   \n",
       "5     527105010           0.0         None           NA       0.0        3   \n",
       "\n",
       "                Neighborhood  Open Porch SF Overall Cond Overall Qual  \\\n",
       "Order PID                                                               \n",
       "1     526301100        Names           62.0            5            6   \n",
       "2     526350040        Names            0.0            6            5   \n",
       "3     526351010        Names           36.0            6            6   \n",
       "4     526353030        Names            0.0            5            7   \n",
       "5     527105010      Gilbert           34.0            5            5   \n",
       "\n",
       "                Paved Drive  Pool Area Pool QC Roof Matl Roof Style  \\\n",
       "Order PID                                                             \n",
       "1     526301100           P        0.0      NA   CompShg        Hip   \n",
       "2     526350040           Y        0.0      NA   CompShg      Gable   \n",
       "3     526351010           Y        0.0      NA   CompShg        Hip   \n",
       "4     526353030           Y        0.0      NA   CompShg        Hip   \n",
       "5     527105010           Y        0.0      NA   CompShg      Gable   \n",
       "\n",
       "                Sale Condition Sale Type  Screen Porch Street  TotRms AbvGrd  \\\n",
       "Order PID                                                                      \n",
       "1     526301100         Normal        WD           0.0   Pave              7   \n",
       "2     526350040         Normal        WD         120.0   Pave              5   \n",
       "3     526351010         Normal        WD           0.0   Pave              6   \n",
       "4     526353030         Normal        WD           0.0   Pave              8   \n",
       "5     527105010         Normal        WD           0.0   Pave              6   \n",
       "\n",
       "                 Total Bath  Total Bsmt SF  Total Porch SF  Total SF  \\\n",
       "Order PID                                                              \n",
       "1     526301100         2.0         1080.0           272.0    2736.0   \n",
       "2     526350040         1.0          882.0           260.0    1778.0   \n",
       "3     526351010         1.5         1329.0           429.0    2658.0   \n",
       "4     526353030         3.5         2110.0             0.0    4220.0   \n",
       "5     527105010         2.5          928.0           246.0    2557.0   \n",
       "\n",
       "                 Total SF (box-cox-0.2) Utilities  Wood Deck SF  Year Built  \\\n",
       "Order PID                                                                     \n",
       "1     526301100               19.344072    AllPub         210.0        1960   \n",
       "2     526350040               17.333478    AllPub         140.0        1961   \n",
       "3     526351010               19.203658    AllPub         393.0        1958   \n",
       "4     526353030               21.548042    AllPub           0.0        1968   \n",
       "5     527105010               19.016856    AllPub         212.0        1997   \n",
       "\n",
       "                 Year Remod/Add  Yr Sold  SalePrice  SalePrice (box-cox-0)  \n",
       "Order PID                                                                   \n",
       "1     526301100            1960     2010   215000.0              12.278393  \n",
       "2     526350040            1961     2010   105000.0              11.561716  \n",
       "3     526351010            1958     2010   172000.0              12.055250  \n",
       "4     526353030            1968     2010   244000.0              12.404924  \n",
       "5     527105010            1998     2010   189900.0              12.154253  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cleaned_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "cleaned_df.to_csv(\"data/data_clean_with_transformations.csv\")"
   ]
  }
 ],
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