Handling categorical variables in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to convert the 'color' column to categorical type.

ML Python

df['color'] = df['color'].[1]()

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Ato_numeric

Bastype('category')

Cfillna

Ddropna

Attempts:

3 left

2fill in blank

medium

Complete the code to create one-hot encoded columns for the 'color' categorical variable.

ML Python

encoded_df = pd.get_dummies(df['color'], [1]=False)

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Adrop_first

Bprefix

Ccolumns

Ddummy_na

Attempts:

3 left

3fill in blank

hard

Fix the error in encoding the 'size' column using LabelEncoder.

ML Python

from sklearn.preprocessing import LabelEncoder
le = LabelEncoder()
df['size_encoded'] = le.[1](df['size'])

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Afit

Btransform

Cfit_transform

Dencode

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary mapping categories to numbers using pandas.

ML Python

mapping = {cat: num for num, cat in enumerate(df['category'].[1]())}
encoded = df['category'].[2](mapping)

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Aunique

Bmap

Cvalue_counts

Dreplace

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to create a pipeline that imputes missing values, encodes categories, and fits a model.

ML Python

from sklearn.pipeline import Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder
pipeline = Pipeline([
    ('imputer', SimpleImputer(strategy=[1])),
    ('encoder', OneHotEncoder(handle_unknown=[2])),
    ('model', [3]())
])

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A'most_frequent'

B'ignore'

CLogisticRegression

D'mean'

Attempts:

3 left