text_plain_output_1.png
import matplotlib.pyplot as plt

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

import seaborn as sns

train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')

correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=.8, square=True);

numeric = [feature for feature in train.columns if train.dtypes[feature] != 'object']
numeric.remove('Id')
numreicMostCorr = ['LotFrontage', 'OverallQual', 'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', 'TotalBsmtSF', 'GrLivArea', 'FullBath', 'TotRmsAbvGrd', 'GarageArea', 'GarageCars']
for feature in numreicMostCorr:
    featureDF = pd.concat([train['SalePrice'], train[feature]], axis=1)
    featureDF.plot.scatter(x=feature, y='SalePrice', ylim=(0, 800000))