2426 lines
408 KiB
Text
2426 lines
408 KiB
Text
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Pair-wise Correlations\n",
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"\n",
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"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",
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"\n",
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"Furthermore, Box-Cox transformations and linear combinations of variables are added where applicable or useful."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## \"Housekeeping\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"import warnings\n",
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"import json\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"import pandas as pd\n",
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"import seaborn as sns\n",
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"\n",
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"from sklearn.preprocessing import PowerTransformer\n",
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"from tabulate import tabulate\n",
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"\n",
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"from utils import (\n",
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" ALL_VARIABLES,\n",
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" CONTINUOUS_VARIABLES,\n",
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" DISCRETE_VARIABLES,\n",
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" NUMERIC_VARIABLES,\n",
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" ORDINAL_VARIABLES,\n",
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" TARGET_VARIABLES,\n",
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" encode_ordinals,\n",
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" load_clean_data,\n",
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" print_column_list,\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"pd.set_option(\"display.max_columns\", 100)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"sns.set_style(\"white\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Load the Data\n",
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"\n",
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"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",
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"\n",
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"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",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"cleaned_df = load_clean_data()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = cleaned_df[NUMERIC_VARIABLES + ORDINAL_VARIABLES + TARGET_VARIABLES]\n",
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"df = encode_ordinals(df)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th>1st Flr SF</th>\n",
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" <th>2nd Flr SF</th>\n",
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" <th>3Ssn Porch</th>\n",
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" <th>Bedroom AbvGr</th>\n",
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" <th>Bsmt Full Bath</th>\n",
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" <th>Bsmt Half Bath</th>\n",
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" <th>Bsmt Unf SF</th>\n",
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" <th>BsmtFin SF 1</th>\n",
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" <th>BsmtFin SF 2</th>\n",
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" <th>Enclosed Porch</th>\n",
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" <th>Fireplaces</th>\n",
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" <th>Full Bath</th>\n",
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" <th>Garage Area</th>\n",
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" <th>Garage Cars</th>\n",
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" <th>Gr Liv Area</th>\n",
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" <th>Half Bath</th>\n",
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" <th>Kitchen AbvGr</th>\n",
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" <th>Lot Area</th>\n",
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" <th>Low Qual Fin SF</th>\n",
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" <th>Mas Vnr Area</th>\n",
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" <th>Misc Val</th>\n",
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" <th>Mo Sold</th>\n",
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" <th>Open Porch SF</th>\n",
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" <th>Pool Area</th>\n",
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" <th>Screen Porch</th>\n",
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" <th>TotRms AbvGrd</th>\n",
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" <th>Total Bsmt SF</th>\n",
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" <th>Wood Deck SF</th>\n",
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" <th>Year Built</th>\n",
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" <th>Year Remod/Add</th>\n",
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" <th>Yr Sold</th>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>Order</th>\n",
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" <th>PID</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <th>526301100</th>\n",
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" <td>1656.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>3</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>441.0</td>\n",
|
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" <td>639.0</td>\n",
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" <td>0.0</td>\n",
|
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" <td>0.0</td>\n",
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" <td>2</td>\n",
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|
" <td>1</td>\n",
|
|
" <td>528.0</td>\n",
|
|
" <td>2</td>\n",
|
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" <td>1656.0</td>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>31770.0</td>\n",
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" <td>0.0</td>\n",
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" <td>112.0</td>\n",
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" <td>0.0</td>\n",
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" <td>5</td>\n",
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" <td>62.0</td>\n",
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" <td>0.0</td>\n",
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" <td>0.0</td>\n",
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" <td>7</td>\n",
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" <td>1080.0</td>\n",
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" <td>210.0</td>\n",
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" <td>1960</td>\n",
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" <td>1960</td>\n",
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" <td>2010</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <th>526350040</th>\n",
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" <td>896.0</td>\n",
|
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" <td>0.0</td>\n",
|
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" <td>0.0</td>\n",
|
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" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>270.0</td>\n",
|
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" <td>468.0</td>\n",
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" <td>144.0</td>\n",
|
|
" <td>0.0</td>\n",
|
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" <td>0</td>\n",
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" <td>1</td>\n",
|
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" <td>730.0</td>\n",
|
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" <td>1</td>\n",
|
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" <td>896.0</td>\n",
|
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" <td>0</td>\n",
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" <td>1</td>\n",
|
|
" <td>11622.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>120.0</td>\n",
|
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" <td>5</td>\n",
|
|
" <td>882.0</td>\n",
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" <td>140.0</td>\n",
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" <td>1961</td>\n",
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" <td>1961</td>\n",
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" <td>2010</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
|
|
" <th>526351010</th>\n",
|
|
" <td>1329.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>406.0</td>\n",
|
|
" <td>923.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>312.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1329.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>14267.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>108.0</td>\n",
|
|
" <td>12500.0</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>36.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>1329.0</td>\n",
|
|
" <td>393.0</td>\n",
|
|
" <td>1958</td>\n",
|
|
" <td>1958</td>\n",
|
|
" <td>2010</td>\n",
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" </tr>\n",
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" <tr>\n",
|
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" <th>4</th>\n",
|
|
" <th>526353030</th>\n",
|
|
" <td>2110.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>1045.0</td>\n",
|
|
" <td>1065.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>522.0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>2110.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>11160.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>4</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>8</td>\n",
|
|
" <td>2110.0</td>\n",
|
|
" <td>0.0</td>\n",
|
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" <td>1968</td>\n",
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" <td>1968</td>\n",
|
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" <td>2010</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <th>527105010</th>\n",
|
|
" <td>928.0</td>\n",
|
|
" <td>701.0</td>\n",
|
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" <td>0.0</td>\n",
|
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" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>137.0</td>\n",
|
|
" <td>791.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>482.0</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>1629.0</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>13830.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>34.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>0.0</td>\n",
|
|
" <td>6</td>\n",
|
|
" <td>928.0</td>\n",
|
|
" <td>212.0</td>\n",
|
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" <td>1997</td>\n",
|
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" <td>1998</td>\n",
|
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" <td>2010</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" 1st Flr SF 2nd Flr SF 3Ssn Porch Bedroom AbvGr \\\n",
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"Order PID \n",
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"1 526301100 1656.0 0.0 0.0 3 \n",
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"2 526350040 896.0 0.0 0.0 2 \n",
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"3 526351010 1329.0 0.0 0.0 3 \n",
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"4 526353030 2110.0 0.0 0.0 3 \n",
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"5 527105010 928.0 701.0 0.0 3 \n",
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"\n",
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" Bsmt Full Bath Bsmt Half Bath Bsmt Unf SF BsmtFin SF 1 \\\n",
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"Order PID \n",
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"1 526301100 1 0 441.0 639.0 \n",
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"2 526350040 0 0 270.0 468.0 \n",
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"3 526351010 0 0 406.0 923.0 \n",
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"4 526353030 1 0 1045.0 1065.0 \n",
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"5 527105010 0 0 137.0 791.0 \n",
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"\n",
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" BsmtFin SF 2 Enclosed Porch Fireplaces Full Bath \\\n",
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"Order PID \n",
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"1 526301100 0.0 0.0 2 1 \n",
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"2 526350040 144.0 0.0 0 1 \n",
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"3 526351010 0.0 0.0 0 1 \n",
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"4 526353030 0.0 0.0 2 2 \n",
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"5 527105010 0.0 0.0 1 2 \n",
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"\n",
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" Garage Area Garage Cars Gr Liv Area Half Bath \\\n",
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"Order PID \n",
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"1 526301100 528.0 2 1656.0 0 \n",
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"2 526350040 730.0 1 896.0 0 \n",
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"3 526351010 312.0 1 1329.0 1 \n",
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"4 526353030 522.0 2 2110.0 1 \n",
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"5 527105010 482.0 2 1629.0 1 \n",
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"\n",
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" Kitchen AbvGr Lot Area Low Qual Fin SF Mas Vnr Area \\\n",
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"Order PID \n",
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"1 526301100 1 31770.0 0.0 112.0 \n",
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"2 526350040 1 11622.0 0.0 0.0 \n",
|
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"3 526351010 1 14267.0 0.0 108.0 \n",
|
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"4 526353030 1 11160.0 0.0 0.0 \n",
|
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"5 527105010 1 13830.0 0.0 0.0 \n",
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"\n",
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" Misc Val Mo Sold Open Porch SF Pool Area Screen Porch \\\n",
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"Order PID \n",
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"1 526301100 0.0 5 62.0 0.0 0.0 \n",
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"2 526350040 0.0 6 0.0 0.0 120.0 \n",
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"3 526351010 12500.0 6 36.0 0.0 0.0 \n",
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"4 526353030 0.0 4 0.0 0.0 0.0 \n",
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"5 527105010 0.0 3 34.0 0.0 0.0 \n",
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"\n",
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" TotRms AbvGrd Total Bsmt SF Wood Deck SF Year Built \\\n",
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"Order PID \n",
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"1 526301100 7 1080.0 210.0 1960 \n",
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"2 526350040 5 882.0 140.0 1961 \n",
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"3 526351010 6 1329.0 393.0 1958 \n",
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"4 526353030 8 2110.0 0.0 1968 \n",
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"5 527105010 6 928.0 212.0 1997 \n",
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"\n",
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" Year Remod/Add Yr Sold \n",
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"Order PID \n",
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"1 526301100 1960 2010 \n",
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"2 526350040 1961 2010 \n",
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"3 526351010 1958 2010 \n",
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"4 526353030 1968 2010 \n",
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"5 527105010 1998 2010 "
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]
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},
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|
"execution_count": 6,
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"metadata": {},
|
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"output_type": "execute_result"
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}
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],
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"source": [
|
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"df[NUMERIC_VARIABLES].head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
|
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
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" <th></th>\n",
|
|
" <th>Bsmt Cond</th>\n",
|
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" <th>Bsmt Exposure</th>\n",
|
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" <th>Bsmt Qual</th>\n",
|
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" <th>BsmtFin Type 1</th>\n",
|
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" <th>BsmtFin Type 2</th>\n",
|
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" <th>Electrical</th>\n",
|
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" <th>Exter Cond</th>\n",
|
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" <th>Exter Qual</th>\n",
|
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" <th>Fence</th>\n",
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" <th>Fireplace Qu</th>\n",
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" <th>Functional</th>\n",
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" <th>Garage Cond</th>\n",
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" <th>Garage Finish</th>\n",
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" <th>Garage Qual</th>\n",
|
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" <th>Heating QC</th>\n",
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" <th>Kitchen Qual</th>\n",
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" <th>Land Slope</th>\n",
|
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" <th>Lot Shape</th>\n",
|
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" <th>Overall Cond</th>\n",
|
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" <th>Overall Qual</th>\n",
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" <th>Paved Drive</th>\n",
|
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" <th>Pool QC</th>\n",
|
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" <th>Utilities</th>\n",
|
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" </tr>\n",
|
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" <tr>\n",
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" <th>Order</th>\n",
|
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" <th>PID</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
|
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" <th>1</th>\n",
|
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" <th>526301100</th>\n",
|
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" <td>4</td>\n",
|
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" <td>4</td>\n",
|
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" <td>3</td>\n",
|
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" <td>4</td>\n",
|
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" <td>1</td>\n",
|
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" <td>4</td>\n",
|
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" <td>2</td>\n",
|
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" <td>2</td>\n",
|
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" <td>0</td>\n",
|
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" <td>4</td>\n",
|
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" <td>7</td>\n",
|
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" <td>3</td>\n",
|
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" <td>3</td>\n",
|
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" <td>3</td>\n",
|
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" <td>1</td>\n",
|
|
" <td>2</td>\n",
|
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" <td>2</td>\n",
|
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" <td>2</td>\n",
|
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" <td>4</td>\n",
|
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" <td>5</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>0</td>\n",
|
|
" <td>3</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
|
" <th>2</th>\n",
|
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" <th>526350040</th>\n",
|
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" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>4</td>\n",
|
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" <td>2</td>\n",
|
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" <td>2</td>\n",
|
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" <td>3</td>\n",
|
|
" <td>0</td>\n",
|
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" <td>7</td>\n",
|
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" <td>3</td>\n",
|
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" <td>1</td>\n",
|
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" <td>3</td>\n",
|
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" <td>2</td>\n",
|
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" <td>2</td>\n",
|
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" <td>2</td>\n",
|
|
" <td>3</td>\n",
|
|
" <td>5</td>\n",
|
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" <td>4</td>\n",
|
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" <td>2</td>\n",
|
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" <td>0</td>\n",
|
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" <td>3</td>\n",
|
|
" </tr>\n",
|
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" <tr>\n",
|
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" <th>3</th>\n",
|
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" <th>526351010</th>\n",
|
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" <td>3</td>\n",
|
|
" <td>1</td>\n",
|
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" <td>3</td>\n",
|
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" <td>5</td>\n",
|
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" <td>1</td>\n",
|
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" <td>4</td>\n",
|
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" <td>2</td>\n",
|
|
" <td>2</td>\n",
|
|
" <td>0</td>\n",
|
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" <td>0</td>\n",
|
|
" <td>7</td>\n",
|
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" <td>3</td>\n",
|
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" <td>1</td>\n",
|
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" <td>3</td>\n",
|
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" <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 "
|
|
]
|
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},
|
|
"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": {
|
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"text/html": [
|
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"<div>\n",
|
|
"<style scoped>\n",
|
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" .dataframe tbody tr th:only-of-type {\n",
|
|
" vertical-align: middle;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe tbody tr th {\n",
|
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" vertical-align: top;\n",
|
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" }\n",
|
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"\n",
|
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" .dataframe thead th {\n",
|
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" text-align: right;\n",
|
|
" }\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>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_xticks(range(len(labels)), labels=labels, fontsize=10)\n",
|
|
" ax.set_yticks(range(len(labels)), labels=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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",
|
|
"text/plain": [
|
|
"<Figure size 1200x1200 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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",
|
|
"text/plain": [
|
|
"<Figure size 1200x1200 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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 33,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"(2898, 86)"
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]
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},
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"execution_count": 33,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"cleaned_df.shape"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 34,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th>1st Flr SF</th>\n",
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" <th>1st Flr SF (box-cox-0)</th>\n",
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" <th>2nd Flr SF</th>\n",
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" <th>3Ssn Porch</th>\n",
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" <th>Alley</th>\n",
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" <th>Bedroom AbvGr</th>\n",
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" <th>Bldg Type</th>\n",
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" <th>Bsmt Cond</th>\n",
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" <th>Bsmt Exposure</th>\n",
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" <th>Bsmt Full Bath</th>\n",
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" <th>Bsmt Half Bath</th>\n",
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" <th>Bsmt Qual</th>\n",
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" <th>Bsmt Unf SF</th>\n",
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" <th>BsmtFin SF 1</th>\n",
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" <th>BsmtFin SF 2</th>\n",
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" <th>BsmtFin Type 1</th>\n",
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" <th>BsmtFin Type 2</th>\n",
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" <th>Central Air</th>\n",
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" <th>Condition 1</th>\n",
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" <th>Condition 2</th>\n",
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" <th>Electrical</th>\n",
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" <th>Enclosed Porch</th>\n",
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" <th>Exter Cond</th>\n",
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" <th>Exter Qual</th>\n",
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" <th>Exterior 1st</th>\n",
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" <th>Exterior 2nd</th>\n",
|
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" <th>Fence</th>\n",
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" <th>Fireplace Qu</th>\n",
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" <th>Fireplaces</th>\n",
|
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" <th>Foundation</th>\n",
|
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" <th>Full Bath</th>\n",
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" <th>Functional</th>\n",
|
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" <th>Garage Area</th>\n",
|
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" <th>Garage Cars</th>\n",
|
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" <th>Garage Cond</th>\n",
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" <th>Garage Finish</th>\n",
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" <th>Garage Qual</th>\n",
|
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" <th>Garage Type</th>\n",
|
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" <th>Gr Liv Area</th>\n",
|
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" <th>Gr Liv Area (box-cox-0)</th>\n",
|
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" <th>Half Bath</th>\n",
|
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" <th>Heating</th>\n",
|
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" <th>Heating QC</th>\n",
|
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" <th>House Style</th>\n",
|
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" <th>Kitchen AbvGr</th>\n",
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" <th>Kitchen Qual</th>\n",
|
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" <th>Land Contour</th>\n",
|
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" <th>Land Slope</th>\n",
|
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" <th>Lot Area</th>\n",
|
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" <th>Lot Area (box-cox-0.1)</th>\n",
|
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" <th>Lot Config</th>\n",
|
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" <th>Lot Shape</th>\n",
|
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" <th>Low Qual Fin SF</th>\n",
|
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" <th>MS SubClass</th>\n",
|
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" <th>MS Zoning</th>\n",
|
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" <th>Mas Vnr Area</th>\n",
|
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" <th>Mas Vnr Type</th>\n",
|
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" <th>Misc Feature</th>\n",
|
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" <th>Misc Val</th>\n",
|
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" <th>Mo Sold</th>\n",
|
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" <th>Neighborhood</th>\n",
|
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" <th>Open Porch SF</th>\n",
|
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" <th>Overall Cond</th>\n",
|
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" <th>Overall Qual</th>\n",
|
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" <th>Paved Drive</th>\n",
|
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" <th>Pool Area</th>\n",
|
|
" <th>Pool QC</th>\n",
|
|
" <th>Roof Matl</th>\n",
|
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" <th>Roof Style</th>\n",
|
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" <th>Sale Condition</th>\n",
|
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" <th>Sale Type</th>\n",
|
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" <th>Screen Porch</th>\n",
|
|
" <th>Street</th>\n",
|
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" <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",
|
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" <th>Total SF (box-cox-0.2)</th>\n",
|
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" <th>Utilities</th>\n",
|
|
" <th>Wood Deck SF</th>\n",
|
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" <th>Year Built</th>\n",
|
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" <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",
|
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" <th>Order</th>\n",
|
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" <th>PID</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <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": [],
|
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"source": [
|
|
"cleaned_df.to_csv(\"data/data_clean_with_transformations.csv\")"
|
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]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "ames-housing",
|
|
"language": "python",
|
|
"name": "ames-housing"
|
|
},
|
|
"language_info": {
|
|
"codemirror_mode": {
|
|
"name": "ipython",
|
|
"version": 3
|
|
},
|
|
"file_extension": ".py",
|
|
"mimetype": "text/x-python",
|
|
"name": "python",
|
|
"nbconvert_exporter": "python",
|
|
"pygments_lexer": "ipython3",
|
|
"version": "3.12.4"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|