Learn from your neighbors.

What is KNN?

"Tell me who your friends are, I'll tell you who you are"

Classify based on K nearest similar examples.

Simple Example

Classifying new house in Denver:

Your house: 1500 sq ft, $400k

**3 Nearest Neighbors**: - House A: 1450 sq ft, $390k → Affordable - House B: 1520 sq ft, $410k → Affordable - House C: 1480 sq ft, $395k → Affordable

**Result**: Your house is "Affordable" (3/3 agree)

How It Works

1. Calculate distance to all points 2. Find K nearest neighbors 3. Take majority vote 4. Assign category

Python Code

```python from sklearn.neighbors import KNeighborsClassifier

Data: [size_sqft, price_k] X = [[1000, 300], [1500, 400], [2000, 600], [2500, 800]] y = ['affordable', 'affordable', 'expensive', 'expensive']

K=3 means check 3 nearest neighbors model = KNeighborsClassifier(n_neighbors=3) model.fit(X, y)

Predict new_house = [[1800, 550]] result = model.predict(new_house) print(result) # 'expensive' ```

Choosing K

**K=1**: Too sensitive to noise **K=100**: Too general **K=3 to 7**: Usually good starting point

Distance Metrics

**Euclidean**: Straight line distance (most common) **Manhattan**: Grid-like distance **Cosine**: Angle-based

Advantages

- Simple to understand - No training needed - Works for classification and regression

Disadvantages

- Slow for large datasets - Sensitive to feature scaling - Doesn't work well in high dimensions

Remember

- Scale your features! - Start with K=5 - Fast to implement, slow to predict