Driving E-commerce Success with Advanced Machine Learning: Customer Purchase Pattern Insights

Understanding client buying patterns in an e-commerce context is a crucial area of investigation for firms looking to streamline their operations and improve customer experiences. This investigation examines the use of several machine learning and data mining algorithms, such as Random Forest, Decision Tree, K nearest neighbors, Neural Networks and support vector machine, to assess customer behavior and preferences in an e-commerce site. Huge amounts of client transaction data have been produced by the e-commerce industry's meteoric expansion. To improve customer experiences, streamline inventory management, and hone marketing efforts, e-commerce enterprises must have a thorough understanding of consumer buying habits. This study uses a variety of machine learning methods to give a thorough examination of client purchasing behavior in an online business. The study investigates feature engineering, model selection, and evaluation, as well as data preparation, to offer useful insights for e-commerce organizations looking to enhance their operations.