(Brute-force) k-Nearest Neighbors Classifier is implemented and used in wine-white dataset. All the required helper functions and performance metrics are also implemented. This custom implementation is cross checked with scikit learn.

• knn_classifier.py: KNeighborsClassifier class. Methods: fit, predict, predict_proba
• helper_functions.py: train_test_split, scale_normal - feature normalization.
• metrics.py: Performance metrics functions - accuracy_score, precision_score, recall, f1-score, roc_curve, precision_recall etc.
• distances.py: manhattan_distance, euclidean_distance etc.
• cross_validation.py: kfold_cross_validation.
• testing_knn_model.py: Using the custom implementation, we explore the wine-white dataset.