Exercise: Categorical feature encoding

Dataset: Ames Housing dataset

1. Download the dataset and load it into a pandas DataFrame.
2. Encode the categorical features using the OrdinalEncoder and OneHotEncoder using the fit and transform methods

Consider the following columns:

• SalePrice: The property’s sale price in dollars. This is the target variable.
• LotArea: Lot size in square feet
• MSZoning: Identifies the general zoning classification of the sale.
  • Categories:
    • A Agriculture
    • C Commercial
    • FV Floating Village Residential
    • I Industrial
    • RH Residential High Density
    • RL Residential Low Density
    • RP Residential Low Density Park
    • RM Residential Medium Density
• LandSlope: Slope of property
  • Categories:
    • Gtl Gentle slope
    • Mod Moderate Slope
      
    • Sev Severe Slope

Optional: Split the data into training and testing sets using the train_test_split function, and use the fit on the training set and transform on the testing set.

from sklearn.datasets import fetch_openml

ames_housing = fetch_openml(name="house_prices", as_frame=True)
data = ames_housing.data
target = ames_housing.target

data.head()

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	1	60	RL	65.0	8450	Pave	NaN	Reg	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	2	2008	WD	Normal
1	2	20	RL	80.0	9600	Pave	NaN	Reg	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	5	2007	WD	Normal
2	3	60	RL	68.0	11250	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	9	2008	WD	Normal
3	4	70	RL	60.0	9550	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	2	2006	WD	Abnorml
4	5	60	RL	84.0	14260	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	12	2008	WD	Normal

5 rows × 80 columns

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