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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pair-wise Correlations\n",
"\n",
"The purpose is to identify 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."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## \"Housekeeping\""
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2018-09-02 23:23:32 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"
]
}
],
"source": [
"% load_ext watermark\n",
"% watermark -d -t -v -z -p matplotlib,numpy,pandas,seaborn"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"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 utils import (\n",
" CONTINUOUS_VARIABLES,\n",
" DISCRETE_VARIABLES,\n",
" NUMERIC_VARIABLES,\n",
" ORDINAL_VARIABLES,\n",
" TARGET_VARIABLE,\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(\n",
" subset=CONTINUOUS_VARIABLES + DISCRETE_VARIABLES + ORDINAL_VARIABLES,\n",
" ordinal_encoded=True,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
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" .dataframe thead th {\n",
" text-align: right;\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <th>1st Flr SF</th>\n",
" <th>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",
" <th>Order</th>\n",
" <th>PID</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
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" <tbody>\n",
" <tr>\n",
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" <td>1656.0</td>\n",
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" <td>1080.0</td>\n",
" <td>210.0</td>\n",
" <td>1960</td>\n",
" <td>1960</td>\n",
" <td>2010</td>\n",
" </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>2</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>270.0</td>\n",
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" <td>140.0</td>\n",
" <td>1961</td>\n",
" <td>1961</td>\n",
" <td>2010</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>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",
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" <td>1</td>\n",
" <td>14267.0</td>\n",
" <td>0.0</td>\n",
" <td>108.0</td>\n",
" <td>12500.0</td>\n",
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" <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",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <th>526353030</th>\n",
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" <td>1</td>\n",
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" <td>1045.0</td>\n",
" <td>1065.0</td>\n",
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" <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",
" <td>1968</td>\n",
" <td>1968</td>\n",
" <td>2010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <th>527105010</th>\n",
" <td>928.0</td>\n",
" <td>701.0</td>\n",
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" <td>0</td>\n",
" <td>137.0</td>\n",
" <td>791.0</td>\n",
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" <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",
" <td>1997</td>\n",
" <td>1998</td>\n",
" <td>2010</td>\n",
" </tr>\n",
" </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",
"3 526351010 0 0 406.0 923.0 \n",
"4 526353030 1 0 1045.0 1065.0 \n",
"5 527105010 0 0 137.0 791.0 \n",
"\n",
" BsmtFin SF 2 Enclosed Porch Fireplaces Full Bath \\\n",
"Order PID \n",
"1 526301100 0.0 0.0 2 1 \n",
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"3 526351010 0.0 0.0 0 1 \n",
"4 526353030 0.0 0.0 2 2 \n",
"5 527105010 0.0 0.0 1 2 \n",
"\n",
" Garage Area Garage Cars Gr Liv Area Half Bath \\\n",
"Order PID \n",
"1 526301100 528.0 2 1656.0 0 \n",
"2 526350040 730.0 1 896.0 0 \n",
"3 526351010 312.0 1 1329.0 1 \n",
"4 526353030 522.0 2 2110.0 1 \n",
"5 527105010 482.0 2 1629.0 1 \n",
"\n",
" Kitchen AbvGr Lot Area Low Qual Fin SF Mas Vnr Area \\\n",
"Order PID \n",
"1 526301100 1 31770.0 0.0 112.0 \n",
"2 526350040 1 11622.0 0.0 0.0 \n",
"3 526351010 1 14267.0 0.0 108.0 \n",
"4 526353030 1 11160.0 0.0 0.0 \n",
"5 527105010 1 13830.0 0.0 0.0 \n",
"\n",
" Misc Val Mo Sold Open Porch SF Pool Area Screen Porch \\\n",
"Order PID \n",
"1 526301100 0.0 5 62.0 0.0 0.0 \n",
"2 526350040 0.0 6 0.0 0.0 120.0 \n",
"3 526351010 12500.0 6 36.0 0.0 0.0 \n",
"4 526353030 0.0 4 0.0 0.0 0.0 \n",
"5 527105010 0.0 3 34.0 0.0 0.0 \n",
"\n",
" TotRms AbvGrd Total Bsmt SF Wood Deck SF Year Built \\\n",
"Order PID \n",
"1 526301100 7 1080.0 210.0 1960 \n",
"2 526350040 5 882.0 140.0 1961 \n",
"3 526351010 6 1329.0 393.0 1958 \n",
"4 526353030 8 2110.0 0.0 1968 \n",
"5 527105010 6 928.0 212.0 1997 \n",
"\n",
" Year Remod/Add Yr Sold \n",
"Order PID \n",
"1 526301100 1960 2010 \n",
"2 526350040 1961 2010 \n",
"3 526351010 1958 2010 \n",
"4 526353030 1968 2010 \n",
"5 527105010 1998 2010 "
]
},
"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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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th></th>\n",
" <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",
" <th>Functional</th>\n",
" <th>Garage Cond</th>\n",
" <th>Garage Finish</th>\n",
" <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",
" <th>Utilities</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": [
"## 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: **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: **Spearman's rank correlation coefficient**\n",
"\n",
"Furthermore, a \"rule of thumb\" classification in *weak* and *strong* correlation is applied to the 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*."
]
},
{
"cell_type": "code",
"execution_count": 9,
"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": 10,
"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": 11,
"metadata": {},
"outputs": [],
"source": [
"pearson = df[CONTINUOUS_VARIABLES + TARGET_VARIABLE].corr(method=\"pearson\")"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
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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": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"corrs = pearson.loc['SalePrice'].drop('SalePrice')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Show the continuous variables that are weakly and strongly correlated with the sales price."
]
},
{
"cell_type": "code",
"execution_count": 14,
"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"
]
}
],
"source": [
"pearson_weakly_correlated = list(corrs[(weak < corrs) & (corrs <= strong)].index)\n",
"print_column_list(pearson_weakly_correlated)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Gr Liv Area Above grade (ground) living area square feet\n"
]
}
],
"source": [
"pearson_strongly_correlated = list(corrs[(strong < corrs)].index)\n",
"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": 16,
"metadata": {},
"outputs": [],
"source": [
"spearman = df[sorted(DISCRETE_VARIABLES + ORDINAL_VARIABLES) + TARGET_VARIABLE].corr(method=\"spearman\")"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"corrs = spearman.loc['SalePrice'].drop('SalePrice')"
]
},
{
"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": 19,
"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": [
"spearman_weakly_correlated = list(corrs[(weak < corrs) & (corrs <= strong)].index)\n",
"print_column_list(spearman_weakly_correlated)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"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",
"Year Built Original construction date\n"
]
}
],
"source": [
"spearman_strongly_correlated = list(corrs[(strong < corrs)].index)\n",
"print_column_list(spearman_strongly_correlated)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Save the weakly and strongly correlated Variables"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"with open(\"weakly_and_strongly_correlated_variables.json\", \"w\") as file:\n",
" file.write(json.dumps({\n",
" \"weakly_correlated\": sorted(\n",
" pearson_weakly_correlated + spearman_weakly_correlated\n",
" ),\n",
" \"strongly_correlated\": sorted(\n",
" pearson_strongly_correlated + spearman_strongly_correlated\n",
" ),\n",
" }))"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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