Supervised learning has labeled answers. Unsupervised learning doesn't—it finds patterns on its own.

Supervised Learning

You provide: Input data + correct answers (labels)

It learns: Mapping from input to output

Goal: Predict outcomes on new data

Examples:

• Email spam detection (emails labeled spam/not spam)
• House price prediction (features → price)
• Image classification (pictures labeled cat/dog)
• Fraud detection (transactions labeled fraud/legitimate)

Unsupervised Learning

You provide: Input data only (no labels)

It learns: Hidden patterns and structure

Goal: Discover groupings or reduce complexity

Examples:

• Customer segmentation (group similar customers)
• Anomaly detection (find unusual patterns)
• Topic modeling (find themes in documents)
• Compression (reduce data dimensions)

Which One to Use?

Use supervised when:

✅ You have labeled data
✅ Clear prediction goal
✅ Want specific outputs

Use unsupervised when:

✅ No labels available
✅ Exploring data
✅ Finding hidden structures