Authors: E.Poorna, B. Vaishnavi
Abstract: Predicting whether or not a consumer will make a purchase is a crucial part of contemporary business intelligence since it allows companies to tailor their marketing efforts to each individual customer. When faced with massive amounts of changing consumer data, traditional rule-based techniques often fail in their capacity to adapt and make accurate predictions. Using demographic, transactional, and behavioural characteristics, this research proposes a Logistic Regression-based machine learning framework for predicting consumer purchase intent. Age, browsing history, prior purchases, product interactions, and engagement metrics are some of the customer-related variables that the suggested system uses to assess the likelihood of a purchase. The prediction pipeline incorporates thorough preprocessing approaches to enhance data quality and model efficacy. These techniques include managing missing values, One-Hot Encoding, feature normalisation, and feature selection. Optimising model performance while preserving computational efficiency and interpretability is achieved by hyperparameter adjustment. Standard performance criteria such as accuracy, precision, recall, and F1-score are used to assess the usefulness of the suggested technique. The results show that it can give solid purchase predictions. Businesses may find new clients, tailor ads to each individual, and increase interaction with existing consumers all with the help of the built framework's real-time prediction capabilities. The results of the experiment show that Logistic Regression is a great tool for predicting if a consumer will make a purchase, which may help with data-driven decision-making and boost growth in the long run.
