ames-housing/01_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>3Ssn Porch</th>\n",
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       "      <th>Gr Liv Area</th>\n",
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       "      <td>31770.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>0.0</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",
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       "      <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",
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       "</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",
       "    </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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