Phase 3: Production ML Practices · ~90 minutes · Python

Handling Imbalanced Data

When 99% of your data is "normal," accuracy is a lie.

Hiring signal: Understanding of handling imbalanced data internals

What you will learn

Implement SMOTE from scratch and explain how synthetic oversampling differs from random duplication
Evaluate imbalanced classifiers using F1, AUPRC, and Matthews Correlation Coefficient instead of accuracy
Compare class weighting, threshold tuning, and resampling strategies and select the right approach for a given imbalance ratio
Build a complete imbalanced data pipeline that combines SMOTE, class weights, and threshold optimization

Introduction

Type: Build Language: Python Prerequisites: Phase 2, Lessons 01-09 (especially evaluation metrics) Time: ~90 minutes

Learning Objectives

Implement SMOTE from scratch and explain how synthetic oversampling differs from random duplication
Evaluate imbalanced classifiers using F1, AUPRC, and Matthews Correlation Coefficient instead of accuracy
Compare class weighting, threshold tuning, and resampling strategies and select the right approach for a given imbalance ratio
Build a complete imbalanced data pipeline that combines SMOTE, class weights, and threshold optimization

Unlock the full lesson

You've read the first 2 sections. The rest of this lesson covers The Problem, The Concept, Build It, Use It, Ship It, Exercises, Key Terms, Further Reading — plus a hands-on lab, quiz, and project artifact.

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