text_html_output_1.png
import matplotlib.pyplot as plt

import pandas as pd

import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)

import seaborn as sns

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

null_in_train_csv = df.isnull().sum()
null_in_train_csv = null_in_train_csv[null_in_train_csv > 0]
null_in_train_csv.sort_values(inplace=True)

data = df.drop(['Alley', 'FireplaceQu', 'PoolQC', 'MiscFeature', 'Fence'], axis=1)

data.shape

arr_train_cor = data.corr()['SalePrice']
idx_train_cor_gt0 = arr_train_cor[arr_train_cor > 0].sort_values(ascending=False).index.tolist()

d1 = idx_train_cor_gt0
print(d1)