# K-Nearest Neighbors (KNN)

**KNN** is a simple, "Lazy" learning algorithm.

## How it Works
1. Store all training data.
2. When a new item arrives, find the **K** closest items (neighbors) to it.
3. Check the class of those neighbors.
4. Assign the most common class to the new item.

## Key Concepts
- **Lazy Learner**: It doesn't build a model during training. It waits until it needs to classify.
- **Distance Measure**: How do we measure "closeness"?
  - **Euclidean Distance**: Straight line distance (most common).
  - **Manhattan Distance**: Grid-like distance.
- **Choosing K**:
  - If K is too small (e.g., K=1), it's sensitive to noise.
  - If K is too large, it might include points from other classes.
  - Usually, K is an odd number (like 3, 5) to avoid ties.

## Example
- New Point: Green Circle.
- K = 3.
- Neighbors: 2 Red Triangles, 1 Blue Square.
- Result: Green Circle is classified as **Red Triangle**.