Feature Engineering

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# Feature Engineering ## Encoding Categorical Data ## Feature Scaling ## Feature Selection Method Description When to Use Filter (correlation) Remove highly correlated features Quick preprocessing Wrapper (RFE) Recursive feature elimination Small to medium data Embedded (Lasso) L1 regularization High-dimensional dat...

Feature Engineering

Encoding Categorical Data


python
import pandas as pd
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
# One-hot encoding
df = pd.DataFrame({'color': ['red', 'blue', 'green', 'blue']})
encoded = pd.get_dummies(df, columns=['color'])
print(encoded)

Feature Scaling


python
from sklearn.preprocessing import StandardScaler, MinMaxScaler
scaler = StandardScaler()
X_scaled = scaler.fit_transform(X)

Feature Selection

Method	Description	When to Use
Filter (correlation)	Remove highly correlated features	Quick preprocessing
Wrapper (RFE)	Recursive feature elimination	Small to medium data
Embedded (Lasso)	L1 regularization	High-dimensional data
Tree-based importance	Random Forest feature importance	Non-linear relationships

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What this note is really saying

Feature Engineering

Encoding Categorical Data

Feature Scaling

Feature Selection

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