Module 20: Handling Imbalanced Data

Learn to handle imbalanced datasets effectively.

What You'll Learn

• Understanding Imbalanced Data
• Resampling Techniques (SMOTE, Undersampling)
• Algorithm-Level Solutions (Class Weights, Threshold Tuning)
• Appropriate Evaluation Metrics
• Best Practices

Topics Covered

1. Understanding the Problem

• What is imbalanced data?
• Why algorithms fail
• Measuring imbalance

2. Resampling Techniques

• Oversampling (SMOTE, ADASYN)
• Undersampling (Tomek Links, ENN)
• Combined Methods

3. Algorithm-Level Solutions

• Class Weights
• Threshold Tuning
• Cost-Sensitive Learning

4. Evaluation Metrics

• Precision-Recall Curve
• F1-Score
• ROC-AUC vs PR-AUC

Learning Objectives

By the end of this module, you should be able to:

• Identify imbalanced datasets
• Apply appropriate resampling techniques
• Use class weights effectively
• Choose correct evaluation metrics
• Handle imbalanced data in production

Projects

1. Fraud Detection: Handle highly imbalanced fraud data
2. Medical Diagnosis: Classify rare diseases
3. Customer Churn: Predict churn with imbalanced classes

Key Concepts

• Imbalance Ratio: Measure of class imbalance
• SMOTE: Synthetic minority oversampling
• Class Weights: Penalize minority misclassification
• PR-AUC: Better than ROC-AUC for imbalanced data

Documentation & Learning Resources

Official Documentation:

• imbalanced-learn Documentation

Free Courses:

• Handling Imbalanced Data (Kaggle Learn)

Tutorials:

• SMOTE Explained

Complete Detailed Guide →

Additional Resources:

• Advanced Topics →: Advanced resampling, ensemble methods
• Project Tutorial →: Step-by-step fraud detection project
• Quick Reference →: Quick lookup guide

Previous (folder order): 19-sql-database-fundamentals
Next Module: 21-model-explainability

Recommended stage order: After Module 25 (GenAI) or after Module 04 (classification) when you hit imbalanced data, not necessarily after SQL on disk.