Implementation of Text Classification Using K-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machine (SVM) Algorithms

In this assignment, we implemented classification using K-Nearest Neighbors (KNN), Naive Bayes (NB), and Support Vector Machine (SVM) algorithms. The KNN implementation involves calculating Euclidean distances between data points and selecting the majority class among k-nearest neighbors, while NB applies probability-based classification using Bayes' theorem with feature independence assumptions. The SVM approach utilizes kernel functions (like linear or RBF) to create optimal hyperplanes for separation. We provide the dataset along with a comprehensive experimental report that includes each algorithm's advantages and limitations, step-by-step implementation procedures, and detailed analysis of experimental results. The report further investigates algorithmic performance and scalability, suggesting potential future enhancements. Overall, this assignment offers complete implementation and analysis of classification algorithms, providing readers with deep insights into their practical applications and constraints.