Discernment of Potential Buyers Based on Purchasing Behaviour Via Machine Learning Techniques

Artificial Intelligence (AI) is a fascinating technology that will rule the roost on various dimensions of life in time to come. Artificial Intelligence capacitates the machines to simulate human intelligence. Machine Learning is one of the momentous subsets of Artificial Intelligence. The phrase Machine Learning (ML) is self-explanatory meaning the machines that will learn on their own using their prior experience. The machines are not requisite to be programmed explicitly for learning new interactions. Today companies invest a great time and resource in mining the data of customers. As customer's data has concealed patterns and trends which are lucrative for the companies. Companies implement AI techniques onto the customer data to classify the potential clients for their products and services. In the proposed work, authors have implemented supervised machine learning algorithms i.e. Support Vector Machine (SVM), Random Forest, Logistic Regression, k-Nearest Neighbour on online customer shopping dataset for classifying whether the customer ended up purchasing the product or not. The authors have also made a critical comparison among the classification accuracies of these ML Algorithms. The paper brings to light that Random Forest performs better with the classification of categorical response variable.