Nearest Neighbor and K-Neighbor Classifier

I have developed Nearest Neighbor and K-Nearest Neighbors classifiers using MATLAB, featuring a well-documented implementation that serves as an excellent educational tool particularly suitable for beginners. The code includes both distance calculation functions (such as Euclidean distance) and classification algorithms that demonstrate how to find the closest training samples for pattern recognition. This implementation not only helps you understand the core concepts behind these fundamental machine learning algorithms but also provides hands-on practice opportunities. Since the classifier is built in MATLAB, you can easily modify and extend the code to meet your specific requirements - for instance, by implementing different distance metrics or optimizing the k-value selection process. The main functions include data normalization routines, neighbor search algorithms, and majority voting mechanisms for classification decisions. Additionally, I can provide supplementary learning resources to help you better comprehend and apply these algorithms effectively. In summary, this classifier serves as an outstanding starting point for delving deeper into the world of machine learning, offering a solid foundation for your future machine learning projects while demonstrating practical implementation techniques for instance-based learning algorithms.