KNN algorithm

Idea: Implemented the K-Nearest Neighbors (KNN) algorithm to classify data points based on similarity.

Method:

For a new input, the algorithm finds the k closest neighbors using distance metrics (e.g., Euclidean distance).

Predicts the class based on the majority vote of neighbors.

Tools: Python, NumPy, Scikit-learn, Matplotlib.

Outcome: Achieved good accuracy on sample datasets, demonstrating the use of distance-based learning in classification.

Importance: Introduced me to supervised learning and the role of distance metrics in AI.