Paper Title
Analyzing The Effectiveness of Classification Algorithms on Labelled Multi-Class Datasets

Abstract
The primary aim of this research paper is to analyze and compare the effectiveness of various classification algorithms on labelled multi-class datasets. Specifically, the objective is to evaluate the performance of seven distinct machine learning algorithms – Random Forest, Gradient Boosting, Support Vector Machine (SVM), K-Nearest Neighbours (KNN), Naïve Bayes, Multinomial Logistic Regression, and Decision Tree using metrics such as accuracy, precision, recall, F1-score and the total execution time. By conducting a thorough analysis, this study seeks to determine which algorithms yield the best results for multi-class classification problems. Keywords - Multi class dataset, Classification, Supervised learning, Random Forest, Support Vector Machine, Gradient Boosting, Naïve Bayes, Multinomial Logistic Regression, Decision Tree, K-Nearest Neighbours