This project report details the implementation and evaluation of K-Nearest Neighbor (KNN) and Radial Basis Function (RBF) neural networks for both classification and regression tasks in machine learning. The project follows the assignment brief provided, focusing on nonparametric algorithms and employing 5-fold cross-validation to assess the performance of each algorithm. The report includes detailed experimental approaches, statistical results, and analysis of the performance metrics, specifically Mean Squared Error (MSE) for regression and accuracy for classification. The logistic activation function is used in the RBF network. The results are presented in tables, showing the performance of each algorithm across different datasets and cross-validation folds. The conclusion highlights the comparative performance of the algorithms, emphasizing the superior performance of the KNN-RBFNN combination. The report adheres to the project guidelines, providing source code, trace runs, and a comprehensive description of the experimental outcomes.
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