text_plain_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

data = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')

important = list(data.corr()['SalePrice'][(data.corr()['SalePrice'] > 0.5) | (data.corr()['SalePrice'] < -0.5)].index)
cat_columns = ['MSZoning', 'Utilities', 'BldgType', 'Heating', 'KitchenQual', 'SaleCondition', 'LandSlope']
important_columns = important + cat_columns
data = data[important_columns]

print('Missing Values by Column')
print('-' * 30)
print(data.isna().sum())
print('-' * 30)
print('Numer of missing values:', data.isna().sum().sum())