Aniruddha Bhandari aniruddha27

Data Engineer

aniruddha27 / House_Price_4.py

Created March 16, 2020 09:28

train['SalePrice'].describe()

aniruddha27 / House_Price_5.py

Created March 16, 2020 09:32

	import seaborn as sns
	sns.distplot(train['SalePrice'])
	plt.xticks(rotation=30);

aniruddha27 / House_Price_6.py

Created March 16, 2020 09:34

print('Skewness = ',train['SalePrice'].skew())

aniruddha27 / House_Price_7.py

Created March 16, 2020 09:35

	target = np.log(train['SalePrice'])
	print('Skewness = ',target.skew())
	sns.distplot(target);

aniruddha27 / House_Price_8.py

Created March 16, 2020 09:37

	corr = train.corr()
	corr['SalePrice'].sort_values(ascending=False).head(10)

aniruddha27 / House_Price_9.py

Created March 16, 2020 09:38

	table = pd.pivot_table(train,index='OverallQual',values='SalePrice',aggfunc=np.mean)
	table

aniruddha27 / House_Price_10.py

Last active March 16, 2020 09:43

	plt.scatter(x=train['GrLivArea'], y=train['SalePrice'])
	plt.ylabel('Sale Price')
	plt.xlabel('GrLivArea')
	plt.show();

aniruddha27 / House_Price_11.py

Created March 16, 2020 09:45

	# dropping outlier values from the dataset
	train = train[train['GrLivArea']<4500]

aniruddha27 / House_Price_12.py

Created March 16, 2020 09:46

	#train rows
	ntrain = train.shape[0]

	#save log transform of target feature
	target = np.log(train['SalePrice'])

	#drop Id and SalePrice from train dataframe
	train.drop(['Id','SalePrice'],inplace=True,axis=1)

	#store test Id

aniruddha27 / House_Price_13.py

Last active March 16, 2020 09:51

	#Null values
	train.isna().sum().sort_values(ascending=False).head(20)