text_plain_output_1.png
import matplotlib.pyplot as plt

import numpy as np

import os

import pandas as pd

import seaborn as sns

dataset_path = '/kaggle/input/house-prices-advanced-regression-techniques/'
train = pd.read_csv(os.path.join(dataset_path, 'train.csv'))

train.drop_duplicates(inplace=True)

[col for col in train.columns if train[col].isnull().sum() != 0]

missing_val_count_by_column = train.isnull().sum()

train.drop(['Id', 'Alley', 'PoolQC', 'Fence', 'MiscFeature', 'FireplaceQu'], axis=1, inplace=True)
train.shape

sns.set_theme(style='white')
corr = train.corr()
f, ax = plt.subplots(figsize=(30, 30))
mask = np.triu(np.ones_like(corr, dtype=bool))
cmap = sns.diverging_palette(230, 20, as_cmap=True)
sns.heatmap(corr, annot=True, mask=mask, cmap=cmap)
plt.show()