text_plain_output_1.png image_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_df = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')

train_df.columns

train_df.isnull().sum()

import matplotlib.pyplot as plt
plt.style.use('ggplot')

import seaborn as sns

features = ['LotArea', 'LotFrontage', 'HouseStyle', 'OverallCond', 'ExterCond', 'TotalBsmtSF', 'CentralAir', 'GarageArea', 'GrLivArea', 'PavedDrive']

X = train_df[features]

sns.heatmap(X.corr())