This document discusses classifying wine types using k-Nearest Neighbors (k-NN) machine learning on a 178 data point wine dataset from UCI with multi-class targets. It describes loading and preprocessing the data, including scaling, then training a k-NN model and evaluating accuracy on test data. The report should include dataset details, compare performance with different preprocessing and hyperparameter settings, and suggest optimal settings while discussing ways to improve performance.
Classification with kNN Dataset Wine httparchiveics.pdf
1. Classification with k-NN
Dataset
Wine (http://archive.ics.uci.edu/ml/datasets/Wine)
Multi-class classification problem, 178 number of data points
Dataset load: from sklearn.datasets import load_wine
Implementation
Data load
Data split (Training/Test)
Data preprocessing (Data Scaling)
Training (Learning algorithm: k-NN)
Evaluation (Metric: Accuracy)
What should be included in the report
Datasets Input (X) number of feature, number of target class
Comparison of performance according to data scaling and adjustment of various hyperparameters
of k-NN
Suggest optimal hyperparameter settings
Discuss ways to further improve performance
