text_plain_output_1.png image_output_1.png
from subprocess import check_output

import numpy as np # linear algebra

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

import seaborn as sns

import numpy as np
import pandas as pd
import seaborn as sns
from subprocess import check_output
df_train = pd.read_csv('../input/train.csv')

df_train.colu

price = df_train['SalePrice']

correlation = df_train.corr()

k = 10
cols = correlation.nlargest(k, 'SalePrice')['SalePrice'].index
coef = np.corrcoef(df_train[cols].values.T)
sns.heatmap(coef, yticklabels=cols.values, annot=True, xticklabels=cols.values)