Feature Engineering

Turn raw columns into leakage-free representations that models can actually learn from

Learn the feature engineering toolkit data scientists are tested on: scaling and transforms per model family, categorical encodings including leakage-safe target encoding, missing-value strategies, cyclical time features, point-in-time aggregations, feature selection, and sklearn pipelines that prevent training/serving skew.

Practice questions (6)

• Spotting the Leak in a Target Encoding Pipeline

  Intermediate · Free
  View →

• Point-in-Time Correctness for a Churn Model

  Advanced
  View →

• Choosing Encodings by Cardinality and Model Family

  Intermediate
  View →

• Cyclical Encoding for a Ride-Demand Model

  Beginner
  View →

• Missing Values: Indicator, Impute, or Let the Model Handle It

  Intermediate
  View →

Quizzes (1)