Performance Analysis of K-Nearest Neighbor and Naive Bayes Algorithms on the Wine Dataset
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Abstract
Advancements in computational technology have positioned classification as an essential tool for automated decision-making. This study presents a comparative analysis of the performance of two popular classification algorithms, K-Nearest Neighbor (KNN) and Naive Bayes, applied to the Wine Dataset. The research aims to determine the most optimal model in terms of accuracy and execution efficiency. A quantitative experimental method was employed, incorporating StandardScaler preprocessing and a 70% training–30% testing data split. The results show that Gaussian Naive Bayes (GNB) outperforms KNN with a perfect accuracy of 100% and the fastest execution time (0.0037 seconds). In contrast, KNN (k = 5) achieved an accuracy of 94.44% with slower computational performance. The strong performance of GNB is supported by the characteristics of the Wine Dataset, which align well with the feature independence assumption. The contribution of this study lies in providing empirical evidence and practical guidance for selecting efficient algorithms for small to medium-sized structured datasets, serving as a reference for researchers and practitioners when determining appropriate classification models for similar cases.
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