Machine Learning (ML) is a research area that has developed over the past few decades as a result of the work of a small group of computer enthusiasts who were interested in the idea of computers learning to play video games and from a branch of mathematics called statistics that hardly ever took computational methods into consideration. The development of a large number of algorithms that are frequently used for text interpretation, pattern recognition, and a variety of other business purposes has sparked clear research interest in data mining to find hidden regularities or irregularities in data. data. data. data. social data is growing by the second This article describes the idea and history of machine learning and contrasts the three most popular machine learning algorithms using some fundamental ideas. The Sentiment140 dataset has been used to demonstrate and evaluate the efficiency of each method in terms of training time, prediction time, and prediction accuracy. Machine learning algorithms have become indispensable tools in analyzing complex datasets and extracting valuable insights. Among the myriad of algorithms available, one particular technique has gained widespread popularity due to its versatility and effectiveness. This comprehensive review aims to delve into the efficacy of this popular machine learning algorithm by offering a comprehensive analysis of its underlying principles, diverse applications, notable strengths, and inherent limitations.

Keywords : Algorithm, Supervised Learning, Unsupervised Learning, Regression, Deep Learning and Support Vector Machines.