Label encoding in Data Analysis Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the LabelEncoder class from sklearn.

Data Analysis Python

from sklearn.preprocessing import [1]

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AMinMaxScaler

BStandardScaler

COneHotEncoder

DLabelEncoder

Attempts:

3 left

💡 Hint

Common Mistakes

Importing the wrong class like StandardScaler or OneHotEncoder.

Forgetting to import from sklearn.preprocessing.

The LabelEncoder class is used to convert categorical labels into numbers. It is found in sklearn.preprocessing.

2fill in blank

medium

Complete the code to create a LabelEncoder object named 'encoder'.

Data Analysis Python

encoder = [1]()

Drag options to blanks, or click blank then click option'

ALabelEncoder

BStandardScaler

COneHotEncoder

DMinMaxScaler

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to create an instance of a scaler or one-hot encoder instead.

Forgetting the parentheses to call the class.

To use label encoding, you create an instance of LabelEncoder by calling it like a function.

3fill in blank

hard

Fix the error in the code to fit the encoder to the list of labels 'colors'.

Data Analysis Python

encoder.[1](colors)

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Afit

Bfit_transform

Cencode

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform before fitting the encoder.

Using a non-existent method like encode.

The fit method learns the unique labels from the data. transform applies the encoding. fit_transform does both at once.

4fill in blank

hard

Fill both blanks to encode the list 'colors' and store the result in 'encoded_colors'.

Data Analysis Python

encoded_colors = encoder.[1](colors)
print([2])

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Afit_transform

Bencoded_colors

Ccolors

Dtransform

Attempts:

3 left

💡 Hint

Common Mistakes

Using transform without fitting first.

Printing the original list instead of the encoded one.

fit_transform fits the encoder and transforms the data in one step. The result is stored in encoded_colors, which we print.

5fill in blank

hard

Fill all three blanks to create a label encoder, fit it to 'fruits', and transform 'fruits' into 'encoded_fruits'.

Data Analysis Python

encoder = [1]()
encoder.[2](fruits)
encoded_fruits = encoder.[3](fruits)

Drag options to blanks, or click blank then click option'

ALabelEncoder

Bfit

Ctransform

Dfit_transform

Attempts:

3 left

💡 Hint

Common Mistakes

Using fit_transform and then transform again.

Not fitting the encoder before transforming.

First, create the encoder with LabelEncoder(). Then fit it to learn labels with fit. Finally, transform the data with transform.