Categorical Variables

To use some variables that are categorical we need to transform them into numerical values. This process is known as encoding or feature encoding.

Label Encoding

Label encoding is a technique used to convert categorical variables into numerical values. It assigns a unique integer to each category in the variable.

from sklearn.preprocessing import LabelEncoder

# Create a label encoder object
label_encoder = LabelEncoder()

# Fit the encoder to the data
data['category'] = label_encoder.fit_transform(data['category'])

One-Hot Encoding

This technique is used to convert categorical variables into binary vectors. It creates a new binary column for each category in the variable.

from sklearn.preprocessing import OneHotEncoder
from sklearn.compose import ColumnTransformer

# Create a column transformer object
column_transformer = ColumnTransformer(
    transformers=[
        ('encoder', OneHotEncoder(), ['category', 'category2'])
    ],
    remainder='passthrough' # Keep the remaining columns
)

# Fit the transformer to the data
data = column_transformer.fit_transform(data)