A CRITICAL STUDY ON APPLICATIONS OF MACHINE LEARNING ALGORITHMS IN BUSINESS ANALYTICS

The advancement of Information and Communication Computing Technology (ICCT) and Artificial Intelligence (AI) made a revolution in Computer Science and its applications areas like Business Intelligence (BI). Artificial Intelligence, Business Intelligence, and Machine Learning work together in order to predict and analyze some complex business analytics and thereby assisting the top managers to take some better decisions at higher levels. With the aid of Artificial Intelligence, Machine Learning can able to provide the ability to machines or systems automatically learn, and take decisions like human beings without programming explicitly based on experience. Machine Learning concepts have applications in diverse fields like day-to-day or daily business transactions to complex business analytics like Marketing Analytics, Pricing Analytics, Social Media Analytics, Customer Analytics, Supply Chain Analytics, and many more. Business Analytics is analyzing the business in 3600 angle which covers historical perspective or Descriptive, future perspective or Predictive and combinations of both or Perspective in order to make better and highly valuable decisions. Machine learning algorithms include supervised learning, unsupervised learning, and Meta- learning. The Machine learning algorithms include different techniques like Nearest Neighbour, Naive Bayes, Decision Tree, Classification Rule, Regression Analysis, Regression Trees, Neural Networks, Support Vector Machines, and many others. Unsupervised learning includes Clustering, Anomaly Detection, Association Rule, and a few others. Meta-learning algorithm covers Bagging, Boosting, Random Forest and a few others. This paper covers applications of machine learning algorithms in different areas of business analytics which includes Marketing Analytics, Pricing Analytics, Social Media Analytics, Customer Analytics, Supply Chain Analytics, Healthcare Analytics, and Portfolio analytics. This paper also tries to distinguish between supervised learning and unsupervised learning. This paper tries to find upcoming areas where machine learning algorithms can best fit in order to make some complex decision or to solve complex business problems