Abstract

Because of fierce market competition, businesses must participate in one-to-one marketing with clients. Using data mining and machine learning to predict client behavior has become a competitive advantage for firms. Due to their precision, machine learning algorithms have gained popularity. A customer's future behavior is hard to forecast due to unforeseeable circumstances. For the same purpose, many algorithms are devised.This study examines how machine learning may help marketers forecast client behavior in marketing efforts, allowing firms to gain additional knowledge about their clients and better serve them. It explains how firms improve marketing to attract new clients, create long-term connections, and improve customer retention to generate revenues. Predicting customer behavior helps businesses in the marketing sector design service offerings and focused promotions. In this method, knowledge is obtained utilizing six classification algorithms: K-nearest neighbor (KNN), Support Vector Machines (SVM), Naive Bayes (NB), Linear Discriminant Analysis (LDA), Ada Boost Classifier, and CatBoost Classifier. According to this research, the CatBoost Classifier makes better accurate forecasts. The CatBoost Classifier is well-tested. The findings show that CatBoost Classifier beats uniform categorization techniques in recall, precision, Cohen's Kappa, accuracy, and F1-score.