In the realm of document classification, the choice of algorithm plays a pivotal role in achieving accurate and efficient results. This research paper delves into a comparative analysis of three distinct algorithms: Naive Bayes, K-Nearest Neighbors (KNN), and Support Vector Machines. It models the probability of a document belonging to a particular class, making it a fundamental choice for text classification. KNN, an instance-based learning approach, operates on the premise of proximity to classify documents by their similarity to labeled instances. Support Vector Machine (SVM) is a supervised machine learning algorithm used for both classification and regression. This research paper comprehensively evaluates the performance of these algorithms using a diverse and representative dataset comprising various document categories. Standard evaluation metrics, including accuracy, precision, recall, F1-score, and computational time, were employed to assess the efficacy of each algorithm. The study also explores the impact of dataset size and dimensionality on the algorithms' performance and scalability.

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Naive Bayes KNN Support Vector Machine