k-Nearest Neighbors

k-Nearest Neighbors (KNN) is a simple and widely used supervised machine learning algorithm for classification and regression tasks. It works by identifying the ‘k’ closest data points to a given input and making predictions based on their labels or values. This training explains how distance metrics such as Euclidean and Manhattan distance are used to measure similarity between data points. It also covers choosing the optimal value of k, feature scaling, and model evaluation techniques. You will learn how KNN is applied in pattern recognition, recommendation systems, and anomaly detection. The course also highlights best practices for improving accuracy and performance in KNN-based models.