text_plain_output_1.png image_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sb

train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv', index_col='Id')
test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv', index_col='Id')

train.shape

trainNum = train.select_dtypes(include=['int64', 'float64'])
corrMat = trainNum.corr()
corrY = corrMat[['SalePrice']].sort_values(by = 'SalePrice')

cmap = sb.diverging_palette(20, 220, n=10)

fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(20, 8))
sb.heatmap(corrMat, cmap = cmap, ax = axes[0])
sb.heatmap(corrY, cmap = cmap, ax = axes[1])

sb.boxplot(x='OverallQual', y='SalePrice', data=train)