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Feature Engineering

Lesson 3: Feature Engineering

Feature engineering is the process of creating new meaningful variables from existing data to improve model performance and interpretability.

In this churn prediction project, new features were created to better capture customer behavior patterns. Examples include grouping tenure into categories, calculating average monthly spend, and defining payment stability based on payment method.

Feature engineering helps:

  • Enhance model learning by providing more informative inputs.
  • Capture behavioral patterns that raw variables may not directly reveal.
  • Improve prediction accuracy and business interpretability.

Well-designed features transform raw telecom data into structured inputs that improve churn prediction performance.