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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pair-wise Correlations\n",
"\n",
"The purpose is to identify predictor variables strongly correlated with the sales price and with each other to get an idea of what variables could be good predictors and potential issues with collinearity.\n",
"\n",
"Furthermore, Box-Cox transformations and linear combinations of variables are added where applicable or useful."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## \"Housekeeping\""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-09-03 16:42:10 CEST\n",
"\n",
"CPython 3.6.5\n",
"IPython 6.5.0\n",
"\n",
"matplotlib 3.0.0rc2\n",
"numpy 1.15.1\n",
"pandas 0.23.4\n",
"seaborn 0.9.0\n",
"sklearn 0.20rc1\n"
]
}
],
"source": [
"% load_ext watermark\n",
"% watermark -d -t -v -z -p matplotlib,numpy,pandas,seaborn,sklearn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import warnings\n",
"import json\n",
"\n",
"import matplotlib.pyplot as plt\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"\n",
"from sklearn.preprocessing import PowerTransformer\n",
"from tabulate import tabulate\n",
"\n",
"from utils import (\n",
" ALL_VARIABLES,\n",
" CONTINUOUS_VARIABLES,\n",
" DISCRETE_VARIABLES,\n",
" NUMERIC_VARIABLES,\n",
" ORDINAL_VARIABLES,\n",
" TARGET_VARIABLES,\n",
" load_clean_data,\n",
" print_column_list,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"% matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"pd.set_option(\"display.max_columns\", 100)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"sns.set_style(\"white\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Load the Data\n",
"\n",
"A subset of the previously cleaned data is used in this analysis. It does not make sense to calculate correlations involving nominal variables."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"df = load_clean_data(ordinal_encoded=True)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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"<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>2nd Flr SF</th>\n",
" <th>3Ssn Porch</th>\n",
" <th>Bedroom AbvGr</th>\n",
" <th>Bsmt Full Bath</th>\n",
" <th>Bsmt Half Bath</th>\n",
" <th>Bsmt Unf SF</th>\n",
" <th>BsmtFin SF 1</th>\n",
" <th>BsmtFin SF 2</th>\n",
" <th>Enclosed Porch</th>\n",
" <th>Fireplaces</th>\n",
" <th>Full Bath</th>\n",
" <th>Garage Area</th>\n",
" <th>Garage Cars</th>\n",
" <th>Gr Liv Area</th>\n",
" <th>Half Bath</th>\n",
" <th>Kitchen AbvGr</th>\n",
" <th>Lot Area</th>\n",
" <th>Low Qual Fin SF</th>\n",
" <th>Mas Vnr Area</th>\n",
" <th>Misc Val</th>\n",
" <th>Mo Sold</th>\n",
" <th>Open Porch SF</th>\n",
" <th>Pool Area</th>\n",
" <th>Screen Porch</th>\n",
" <th>TotRms AbvGrd</th>\n",
" <th>Total Bsmt SF</th>\n",
" <th>Wood Deck SF</th>\n",
" <th>Year Built</th>\n",
" <th>Year Remod/Add</th>\n",
" <th>Yr Sold</th>\n",
" </tr>\n",
" <tr>\n",
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" <td>1997</td>\n",
" <td>1998</td>\n",
" <td>2010</td>\n",
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" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" 1st Flr SF 2nd Flr SF 3Ssn Porch Bedroom AbvGr \\\n",
"Order PID \n",
"1 526301100 1656.0 0.0 0.0 3 \n",
"2 526350040 896.0 0.0 0.0 2 \n",
"3 526351010 1329.0 0.0 0.0 3 \n",
"4 526353030 2110.0 0.0 0.0 3 \n",
"5 527105010 928.0 701.0 0.0 3 \n",
"\n",
" Bsmt Full Bath Bsmt Half Bath Bsmt Unf SF BsmtFin SF 1 \\\n",
"Order PID \n",
"1 526301100 1 0 441.0 639.0 \n",
"2 526350040 0 0 270.0 468.0 \n",
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"\n",
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"4 526353030 0.0 0.0 2 2 \n",
"5 527105010 0.0 0.0 1 2 \n",
"\n",
" Garage Area Garage Cars Gr Liv Area Half Bath \\\n",
"Order PID \n",
"1 526301100 528.0 2 1656.0 0 \n",
"2 526350040 730.0 1 896.0 0 \n",
"3 526351010 312.0 1 1329.0 1 \n",
"4 526353030 522.0 2 2110.0 1 \n",
"5 527105010 482.0 2 1629.0 1 \n",
"\n",
" Kitchen AbvGr Lot Area Low Qual Fin SF Mas Vnr Area \\\n",
"Order PID \n",
"1 526301100 1 31770.0 0.0 112.0 \n",
"2 526350040 1 11622.0 0.0 0.0 \n",
"3 526351010 1 14267.0 0.0 108.0 \n",
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"\n",
" Misc Val Mo Sold Open Porch SF Pool Area Screen Porch \\\n",
"Order PID \n",
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"4 526353030 0.0 4 0.0 0.0 0.0 \n",
"5 527105010 0.0 3 34.0 0.0 0.0 \n",
"\n",
" TotRms AbvGrd Total Bsmt SF Wood Deck SF Year Built \\\n",
"Order PID \n",
"1 526301100 7 1080.0 210.0 1960 \n",
"2 526350040 5 882.0 140.0 1961 \n",
"3 526351010 6 1329.0 393.0 1958 \n",
"4 526353030 8 2110.0 0.0 1968 \n",
"5 527105010 6 928.0 212.0 1997 \n",
"\n",
" Year Remod/Add Yr Sold \n",
"Order PID \n",
"1 526301100 1960 2010 \n",
"2 526350040 1961 2010 \n",
"3 526351010 1958 2010 \n",
"4 526353030 1968 2010 \n",
"5 527105010 1998 2010 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[NUMERIC_VARIABLES].head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"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."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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" <th>Bsmt Cond</th>\n",
" <th>Bsmt Exposure</th>\n",
" <th>Bsmt Qual</th>\n",
" <th>BsmtFin Type 1</th>\n",
" <th>BsmtFin Type 2</th>\n",
" <th>Electrical</th>\n",
" <th>Exter Cond</th>\n",
" <th>Exter Qual</th>\n",
" <th>Fence</th>\n",
" <th>Fireplace Qu</th>\n",
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" <th>Garage Cond</th>\n",
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" <th>Garage Qual</th>\n",
" <th>Heating QC</th>\n",
" <th>Kitchen Qual</th>\n",
" <th>Land Slope</th>\n",
" <th>Lot Shape</th>\n",
" <th>Overall Cond</th>\n",
" <th>Overall Qual</th>\n",
" <th>Paved Drive</th>\n",
" <th>Pool QC</th>\n",
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],
"text/plain": [
" Bsmt Cond Bsmt Exposure Bsmt Qual BsmtFin Type 1 \\\n",
"Order PID \n",
"1 526301100 4 4 3 4 \n",
"2 526350040 3 1 3 3 \n",
"3 526351010 3 1 3 5 \n",
"4 526353030 3 1 3 5 \n",
"5 527105010 3 1 4 6 \n",
"\n",
" BsmtFin Type 2 Electrical Exter Cond Exter Qual Fence \\\n",
"Order PID \n",
"1 526301100 1 4 2 2 0 \n",
"2 526350040 2 4 2 2 3 \n",
"3 526351010 1 4 2 2 0 \n",
"4 526353030 1 4 2 3 0 \n",
"5 527105010 1 4 2 2 3 \n",
"\n",
" Fireplace Qu Functional Garage Cond Garage Finish \\\n",
"Order PID \n",
"1 526301100 4 7 3 3 \n",
"2 526350040 0 7 3 1 \n",
"3 526351010 0 7 3 1 \n",
"4 526353030 3 7 3 3 \n",
"5 527105010 3 7 3 3 \n",
"\n",
" Garage Qual Heating QC Kitchen Qual Land Slope Lot Shape \\\n",
"Order PID \n",
"1 526301100 3 1 2 2 2 \n",
"2 526350040 3 2 2 2 3 \n",
"3 526351010 3 2 3 2 2 \n",
"4 526353030 3 4 4 2 3 \n",
"5 527105010 3 3 2 2 2 \n",
"\n",
" Overall Cond Overall Qual Paved Drive Pool QC Utilities \n",
"Order PID \n",
"1 526301100 4 5 1 0 3 \n",
"2 526350040 5 4 2 0 3 \n",
"3 526351010 5 5 2 0 3 \n",
"4 526353030 4 6 2 0 3 \n",
"5 527105010 4 4 2 0 3 "
]
},
"execution_count": 8,
"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": 9,
"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": 10,
"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": [
"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",
"\n",
"new_variables = [\"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": [
"29.0"
]
},
"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",
"\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"
]
}
],
"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. Also, use a lambda rounded to the next \"half\" integer.\n",
"\n",
"Consequently, the only applicable transformation is for \"SalePrice\"."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1st Flr SF: exact lambda of -8.398 not in realistic range\n",
"Gr Liv Area: exact lambda of -8.398 not in realistic range\n",
"Lot Area: exact lambda of -8.398 not in realistic range\n",
"SalePrice: use rounded lambda of 0.0 (exact is 0.004)\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",
" exact_lambda = pt.lambdas_[0]\n",
" used_lambda = 0.5 * np.round(2.0 * exact_lambda)\n",
" if -3 <= exact_lambda <= 3:\n",
" new_column = f\"{column} (box-cox-{used_lambda})\"\n",
" df[new_column] = (\n",
" np.log(X) if used_lambda == 0 else (((X ** used_lambda) - 1) / used_lambda)\n",
" )\n",
" # Show that only SalePrice has a useful transformation.\n",
" assert column in TARGET_VARIABLES\n",
" # Track the new column in the appropiate list.\n",
" TARGET_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 rounded lambda of {used_lambda} (exact is {exact_lambda:.3f})\",\n",
" ))\n",
" else:\n",
" output.append((\n",
" f\"{column}:\",\n",
" f\"exact lambda of {exact_lambda:.3f} not in realistic range\",\n",
" ))\n",
"print(tabulate(sorted(output), tablefmt=\"plain\"))"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>SalePrice</th>\n",
" <th>SalePrice (box-cox-0.0)</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Order</th>\n",
" <th>PID</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>215000.0</td>\n",
" <td>12.278393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <th>526350040</th>\n",
" <td>105000.0</td>\n",
" <td>11.561716</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <th>526351010</th>\n",
" <td>172000.0</td>\n",
" <td>12.055250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <th>526353030</th>\n",
" <td>244000.0</td>\n",
" <td>12.404924</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <th>527105010</th>\n",
" <td>189900.0</td>\n",
" <td>12.154253</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" SalePrice SalePrice (box-cox-0.0)\n",
"Order PID \n",
"1 526301100 215000.0 12.278393\n",
"2 526350040 105000.0 11.561716\n",
"3 526351010 172000.0 12.055250\n",
"4 526353030 244000.0 12.404924\n",
"5 527105010 189900.0 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*. Correlations are calculated for **each** target variable (i.e., raw \"SalePrice\" and Box-Cox transformation thereof)."
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"strong = 0.66\n",
"weak = 0.33"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Two heatmaps below (implemented in the reusable `plot_correlation` function) help visualize the correlations.\n",
"\n",
"Obviously, many variables are pair-wise correlated. This could yield regression coefficients *inprecise* and not usable / interpretable. At the same time, this does not lower the predictive power of a model as a whole. In contrast to the pair-wise correlations, *multi-collinearity* is not checked here."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"def plot_correlation(data, title):\n",
" \"\"\"Visualize a correlation matrix in a nice heatmap.\"\"\"\n",
" fig, ax = plt.subplots(figsize=(12, 12))\n",
" ax.set_title(title, fontsize=24)\n",
" # Blank out the upper triangular part of the matrix.\n",
" mask = np.zeros_like(data, dtype=np.bool)\n",
" mask[np.triu_indices_from(mask)] = True\n",
" # Use a diverging color map.\n",
" cmap = sns.diverging_palette(240, 0, as_cmap=True)\n",
" # Adjust the labels' font size.\n",
" labels = data.columns\n",
" ax.set_xticklabels(labels, fontsize=10)\n",
" ax.set_yticklabels(labels, fontsize=10)\n",
" # Plot it.\n",
" sns.heatmap(\n",
" data, vmin=-1, vmax=1, cmap=cmap, center=0, linewidths=.5,\n",
" cbar_kws={\"shrink\": .5}, square=True, mask=mask, ax=ax\n",
" )"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Pearson\n",
"\n",
"Pearson's correlation coefficient shows a linear relationship between two variables."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"columns = CONTINUOUS_VARIABLES + TARGET_VARIABLES\n",
"pearson = df[columns].corr(method=\"pearson\")"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_correlation(pearson, \"Pearson's Correlation\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Predictors weakly or strongly correlated with a target variable are collected."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"pearson_weakly_correlated = set()\n",
"pearson_strongly_correlated = set()\n",
"# Iterate over the raw and transformed target.\n",
"for target in TARGET_VARIABLES:\n",
" corrs = pearson.loc[target].drop(TARGET_VARIABLES)\n",
" pearson_weakly_correlated |= set(corrs[(weak < corrs) & (corrs <= strong)].index)\n",
" pearson_strongly_correlated |= set(corrs[(strong < corrs)].index)\n",
"# Show that no contradiction exists between weak and strong classification.\n",
"assert pearson_weakly_correlated & pearson_strongly_correlated == set()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the continuous variables that are weakly and strongly correlated with the sales price."
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"1st Flr SF First Floor square feet\n",
"BsmtFin SF 1 Type 1 finished square feet\n",
"Garage Area Size of garage in square feet\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": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gr Liv Area Above grade (ground) living area square feet\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": 24,
"metadata": {},
"outputs": [],
"source": [
"columns = sorted(DISCRETE_VARIABLES + ORDINAL_VARIABLES) + TARGET_VARIABLES\n",
"spearman = df[columns].corr(method=\"spearman\")"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 864x864 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plot_correlation(spearman, \"Spearman's Rank Correlation\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Predictors weakly or strongly correlated with a target variable are collected."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"spearman_weakly_correlated = set()\n",
"spearman_strongly_correlated = set()\n",
"# Iterate over the raw and transformed target.\n",
"for target in TARGET_VARIABLES:\n",
" corrs = spearman.loc[target].drop(TARGET_VARIABLES)\n",
" spearman_weakly_correlated |= set(corrs[(weak < corrs) & (corrs <= strong)].index)\n",
" spearman_strongly_correlated |= set(corrs[(strong < corrs)].index)\n",
"# Show that no contradiction exists between weak and strong classification.\n",
"assert spearman_weakly_correlated & spearman_strongly_correlated == set()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the discrete and ordinal variables that are weakly and strongly correlated with the sales price."
]
},
{
"cell_type": "code",
"execution_count": 27,
"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",
"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": 28,
"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": 29,
"metadata": {},
"outputs": [],
"source": [
"with open(\"data/weakly_and_strongly_correlated_variables.json\", \"w\") as file:\n",
" file.write(json.dumps({\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",
"For conveniene, sort the columns alphabetically with the targets at the end."
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"df = df[sorted(ALL_VARIABLES + new_variables) + TARGET_VARIABLES]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Added two new linear combinations and one Box-Cox transformation to the previous 78 columns."
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(2898, 81)"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>1st Flr SF</th>\n",
" <th>2nd Flr SF</th>\n",
" <th>3Ssn Porch</th>\n",
" <th>Alley</th>\n",
" <th>Bedroom AbvGr</th>\n",
" <th>Bldg Type</th>\n",
" <th>Bsmt Cond</th>\n",
" <th>Bsmt Exposure</th>\n",
" <th>Bsmt Full Bath</th>\n",
" <th>Bsmt Half Bath</th>\n",
" <th>Bsmt Qual</th>\n",
" <th>Bsmt Unf SF</th>\n",
" <th>BsmtFin SF 1</th>\n",
" <th>BsmtFin SF 2</th>\n",
" <th>BsmtFin Type 1</th>\n",
" <th>BsmtFin Type 2</th>\n",
" <th>Central Air</th>\n",
" <th>Condition 1</th>\n",
" <th>Condition 2</th>\n",
" <th>Electrical</th>\n",
" <th>Enclosed Porch</th>\n",
" <th>Exter Cond</th>\n",
" <th>Exter Qual</th>\n",
" <th>Exterior 1st</th>\n",
" <th>Exterior 2nd</th>\n",
" <th>Fence</th>\n",
" <th>Fireplace Qu</th>\n",
" <th>Fireplaces</th>\n",
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" <th>Full Bath</th>\n",
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" <th>Garage Cond</th>\n",
" <th>Garage Finish</th>\n",
" <th>Garage Qual</th>\n",
" <th>Garage Type</th>\n",
" <th>Gr Liv Area</th>\n",
" <th>Half Bath</th>\n",
" <th>Heating</th>\n",
" <th>Heating QC</th>\n",
" <th>House Style</th>\n",
" <th>Kitchen AbvGr</th>\n",
" <th>Kitchen Qual</th>\n",
" <th>Land Contour</th>\n",
" <th>Land Slope</th>\n",
" <th>Lot Area</th>\n",
" <th>Lot Config</th>\n",
" <th>Lot Shape</th>\n",
" <th>Low Qual Fin SF</th>\n",
" <th>MS SubClass</th>\n",
" <th>MS Zoning</th>\n",
" <th>Mas Vnr Area</th>\n",
" <th>Mas Vnr Type</th>\n",
" <th>Misc Feature</th>\n",
" <th>Misc Val</th>\n",
" <th>Mo Sold</th>\n",
" <th>Neighborhood</th>\n",
" <th>Open Porch SF</th>\n",
" <th>Overall Cond</th>\n",
" <th>Overall Qual</th>\n",
" <th>Paved Drive</th>\n",
" <th>Pool Area</th>\n",
" <th>Pool QC</th>\n",
" <th>Roof Matl</th>\n",
" <th>Roof Style</th>\n",
" <th>Sale Condition</th>\n",
" <th>Sale Type</th>\n",
" <th>Screen Porch</th>\n",
" <th>Street</th>\n",
" <th>TotRms AbvGrd</th>\n",
" <th>Total Bath</th>\n",
" <th>Total Bsmt SF</th>\n",
" <th>Total Porch SF</th>\n",
" <th>Utilities</th>\n",
" <th>Wood Deck SF</th>\n",
" <th>Year Built</th>\n",
" <th>Year Remod/Add</th>\n",
" <th>Yr Sold</th>\n",
" <th>SalePrice</th>\n",
" <th>SalePrice (box-cox-0.0)</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",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <th>526301100</th>\n",
" <td>1656.0</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>4</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>441.0</td>\n",
" <td>639.0</td>\n",
" <td>0.0</td>\n",
" <td>4</td>\n",
" <td>1</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>BrkFace</td>\n",
" <td>Plywood</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>CBlock</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>528.0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>Attchd</td>\n",
" <td>1656.0</td>\n",
" <td>0</td>\n",
" <td>GasA</td>\n",
" <td>1</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Lvl</td>\n",
" <td>2</td>\n",
" <td>31770.0</td>\n",
" <td>Corner</td>\n",
" <td>2</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>4</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>0.0</td>\n",
" <td>0</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>3</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>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>2</td>\n",
" <td>1Fam</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>270.0</td>\n",
" <td>468.0</td>\n",
" <td>144.0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>Y</td>\n",
" <td>Feedr</td>\n",
" <td>Norm</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>VinylSd</td>\n",
" <td>VinylSd</td>\n",
" <td>3</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>CBlock</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>730.0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Attchd</td>\n",
" <td>896.0</td>\n",
" <td>0</td>\n",
" <td>GasA</td>\n",
" <td>2</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Lvl</td>\n",
" <td>2</td>\n",
" <td>11622.0</td>\n",
" <td>Inside</td>\n",
" <td>3</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>5</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>0</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>3</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>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>406.0</td>\n",
" <td>923.0</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>Wd Sdng</td>\n",
" <td>Wd Sdng</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>CBlock</td>\n",
" <td>1</td>\n",
" <td>7</td>\n",
" <td>312.0</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Attchd</td>\n",
" <td>1329.0</td>\n",
" <td>1</td>\n",
" <td>GasA</td>\n",
" <td>2</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>3</td>\n",
" <td>Lvl</td>\n",
" <td>2</td>\n",
" <td>14267.0</td>\n",
" <td>Corner</td>\n",
" <td>2</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>5</td>\n",
" <td>5</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>0</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>3</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>0.0</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1045.0</td>\n",
" <td>1065.0</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>BrkFace</td>\n",
" <td>BrkFace</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>2</td>\n",
" <td>CBlock</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>522.0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>Attchd</td>\n",
" <td>2110.0</td>\n",
" <td>1</td>\n",
" <td>GasA</td>\n",
" <td>4</td>\n",
" <td>1Story</td>\n",
" <td>1</td>\n",
" <td>4</td>\n",
" <td>Lvl</td>\n",
" <td>2</td>\n",
" <td>11160.0</td>\n",
" <td>Corner</td>\n",
" <td>3</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>4</td>\n",
" <td>6</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>0</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>3</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>701.0</td>\n",
" <td>0.0</td>\n",
" <td>NA</td>\n",
" <td>3</td>\n",
" <td>1Fam</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>4</td>\n",
" <td>137.0</td>\n",
" <td>791.0</td>\n",
" <td>0.0</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" <td>Y</td>\n",
" <td>Norm</td>\n",
" <td>Norm</td>\n",
" <td>4</td>\n",
" <td>0.0</td>\n",
" <td>2</td>\n",
" <td>2</td>\n",
" <td>VinylSd</td>\n",
" <td>VinylSd</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>PConc</td>\n",
" <td>2</td>\n",
" <td>7</td>\n",
" <td>482.0</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>3</td>\n",
" <td>Attchd</td>\n",
" <td>1629.0</td>\n",
" <td>1</td>\n",
" <td>GasA</td>\n",
" <td>3</td>\n",
" <td>2Story</td>\n",
" <td>1</td>\n",
" <td>2</td>\n",
" <td>Lvl</td>\n",
" <td>2</td>\n",
" <td>13830.0</td>\n",
" <td>Inside</td>\n",
" <td>2</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>4</td>\n",
" <td>4</td>\n",
" <td>2</td>\n",
" <td>0.0</td>\n",
" <td>0</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>3</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 2nd Flr SF 3Ssn Porch Alley Bedroom AbvGr \\\n",
"Order PID \n",
"1 526301100 1656.0 0.0 0.0 NA 3 \n",
"2 526350040 896.0 0.0 0.0 NA 2 \n",
"3 526351010 1329.0 0.0 0.0 NA 3 \n",
"4 526353030 2110.0 0.0 0.0 NA 3 \n",
"5 527105010 928.0 701.0 0.0 NA 3 \n",
"\n",
" Bldg Type Bsmt Cond Bsmt Exposure Bsmt Full Bath \\\n",
"Order PID \n",
"1 526301100 1Fam 4 4 1 \n",
"2 526350040 1Fam 3 1 0 \n",
"3 526351010 1Fam 3 1 0 \n",
"4 526353030 1Fam 3 1 1 \n",
"5 527105010 1Fam 3 1 0 \n",
"\n",
" Bsmt Half Bath Bsmt Qual Bsmt Unf SF BsmtFin SF 1 \\\n",
"Order PID \n",
"1 526301100 0 3 441.0 639.0 \n",
"2 526350040 0 3 270.0 468.0 \n",
"3 526351010 0 3 406.0 923.0 \n",
"4 526353030 0 3 1045.0 1065.0 \n",
"5 527105010 0 4 137.0 791.0 \n",
"\n",
" BsmtFin SF 2 BsmtFin Type 1 BsmtFin Type 2 Central Air \\\n",
"Order PID \n",
"1 526301100 0.0 4 1 Y \n",
"2 526350040 144.0 3 2 Y \n",
"3 526351010 0.0 5 1 Y \n",
"4 526353030 0.0 5 1 Y \n",
"5 527105010 0.0 6 1 Y \n",
"\n",
" Condition 1 Condition 2 Electrical Enclosed Porch \\\n",
"Order PID \n",
"1 526301100 Norm Norm 4 0.0 \n",
"2 526350040 Feedr Norm 4 0.0 \n",
"3 526351010 Norm Norm 4 0.0 \n",
"4 526353030 Norm Norm 4 0.0 \n",
"5 527105010 Norm Norm 4 0.0 \n",
"\n",
" Exter Cond Exter Qual Exterior 1st Exterior 2nd Fence \\\n",
"Order PID \n",
"1 526301100 2 2 BrkFace Plywood 0 \n",
"2 526350040 2 2 VinylSd VinylSd 3 \n",
"3 526351010 2 2 Wd Sdng Wd Sdng 0 \n",
"4 526353030 2 3 BrkFace BrkFace 0 \n",
"5 527105010 2 2 VinylSd VinylSd 3 \n",
"\n",
" Fireplace Qu Fireplaces Foundation Full Bath Functional \\\n",
"Order PID \n",
"1 526301100 4 2 CBlock 1 7 \n",
"2 526350040 0 0 CBlock 1 7 \n",
"3 526351010 0 0 CBlock 1 7 \n",
"4 526353030 3 2 CBlock 2 7 \n",
"5 527105010 3 1 PConc 2 7 \n",
"\n",
" Garage Area Garage Cars Garage Cond Garage Finish \\\n",
"Order PID \n",
"1 526301100 528.0 2 3 3 \n",
"2 526350040 730.0 1 3 1 \n",
"3 526351010 312.0 1 3 1 \n",
"4 526353030 522.0 2 3 3 \n",
"5 527105010 482.0 2 3 3 \n",
"\n",
" Garage Qual Garage Type Gr Liv Area Half Bath Heating \\\n",
"Order PID \n",
"1 526301100 3 Attchd 1656.0 0 GasA \n",
"2 526350040 3 Attchd 896.0 0 GasA \n",
"3 526351010 3 Attchd 1329.0 1 GasA \n",
"4 526353030 3 Attchd 2110.0 1 GasA \n",
"5 527105010 3 Attchd 1629.0 1 GasA \n",
"\n",
" Heating QC House Style Kitchen AbvGr Kitchen Qual \\\n",
"Order PID \n",
"1 526301100 1 1Story 1 2 \n",
"2 526350040 2 1Story 1 2 \n",
"3 526351010 2 1Story 1 3 \n",
"4 526353030 4 1Story 1 4 \n",
"5 527105010 3 2Story 1 2 \n",
"\n",
" Land Contour Land Slope Lot Area Lot Config Lot Shape \\\n",
"Order PID \n",
"1 526301100 Lvl 2 31770.0 Corner 2 \n",
"2 526350040 Lvl 2 11622.0 Inside 3 \n",
"3 526351010 Lvl 2 14267.0 Corner 2 \n",
"4 526353030 Lvl 2 11160.0 Corner 3 \n",
"5 527105010 Lvl 2 13830.0 Inside 2 \n",
"\n",
" Low Qual Fin SF MS SubClass MS Zoning Mas Vnr Area \\\n",
"Order PID \n",
"1 526301100 0.0 020 RL 112.0 \n",
"2 526350040 0.0 020 RH 0.0 \n",
"3 526351010 0.0 020 RL 108.0 \n",
"4 526353030 0.0 020 RL 0.0 \n",
"5 527105010 0.0 060 RL 0.0 \n",
"\n",
" Mas Vnr Type Misc Feature Misc Val Mo Sold Neighborhood \\\n",
"Order PID \n",
"1 526301100 Stone NA 0.0 5 Names \n",
"2 526350040 None NA 0.0 6 Names \n",
"3 526351010 BrkFace Gar2 12500.0 6 Names \n",
"4 526353030 None NA 0.0 4 Names \n",
"5 527105010 None NA 0.0 3 Gilbert \n",
"\n",
" Open Porch SF Overall Cond Overall Qual Paved Drive \\\n",
"Order PID \n",
"1 526301100 62.0 4 5 1 \n",
"2 526350040 0.0 5 4 2 \n",
"3 526351010 36.0 5 5 2 \n",
"4 526353030 0.0 4 6 2 \n",
"5 527105010 34.0 4 4 2 \n",
"\n",
" Pool Area Pool QC Roof Matl Roof Style Sale Condition \\\n",
"Order PID \n",
"1 526301100 0.0 0 CompShg Hip Normal \n",
"2 526350040 0.0 0 CompShg Gable Normal \n",
"3 526351010 0.0 0 CompShg Hip Normal \n",
"4 526353030 0.0 0 CompShg Hip Normal \n",
"5 527105010 0.0 0 CompShg Gable Normal \n",
"\n",
" Sale Type Screen Porch Street TotRms AbvGrd Total Bath \\\n",
"Order PID \n",
"1 526301100 WD 0.0 Pave 7 2.0 \n",
"2 526350040 WD 120.0 Pave 5 1.0 \n",
"3 526351010 WD 0.0 Pave 6 1.5 \n",
"4 526353030 WD 0.0 Pave 8 3.5 \n",
"5 527105010 WD 0.0 Pave 6 2.5 \n",
"\n",
" Total Bsmt SF Total Porch SF Utilities Wood Deck SF \\\n",
"Order PID \n",
"1 526301100 1080.0 272.0 3 210.0 \n",
"2 526350040 882.0 260.0 3 140.0 \n",
"3 526351010 1329.0 429.0 3 393.0 \n",
"4 526353030 2110.0 0.0 3 0.0 \n",
"5 527105010 928.0 246.0 3 212.0 \n",
"\n",
" Year Built Year Remod/Add Yr Sold SalePrice \\\n",
"Order PID \n",
"1 526301100 1960 1960 2010 215000.0 \n",
"2 526350040 1961 1961 2010 105000.0 \n",
"3 526351010 1958 1958 2010 172000.0 \n",
"4 526353030 1968 1968 2010 244000.0 \n",
"5 527105010 1997 1998 2010 189900.0 \n",
"\n",
" SalePrice (box-cox-0.0) \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": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"data/data_clean_with_transformations.csv\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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