A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms for Predictive Analytics: Convergence of AI and IoT at the Cutting Edge

Predictive analytics is an emerging technology used to make predictions about unknown factors using relevant data. It encompasses techniques such as machine learning, artificial intelligence, data science, big data, etc. Artificial intelligence is the milestone of future technology in business and industries. It uses data and machine learning algorithms and techniques to predict future outcomes by identifying their likelihood according to the data previously used. The aim is to go beyond knowing things that have happened to determining what will happen in the future in the the business and industrial sectors. Machine learning is a branch of computer science, which is also a part or subset of artificial intelligence that analyzes data, identifies patterns, and makes decisions with minimal human intervention. The machines learn the data from the past and predict the future automatically without explicit programming. There are huge numbers of algorithms in machine learning that are used for predictive analytics. Supervised and unsupervised machine learning algorithms are the types used for analyzing the data to predict the future probabilities according to the data. This chapter presents a comparative study on these major types of machine learning algorithms that predict future trends and patterns concerning the areas of business and industry.