Predicting customer purchase behaviour

The primary objective of this project is to develop a robust and accurate predictive model that classifies customers into three distinct purchasing behavior categories rare, occasional, and frequent based on their demographic and behavioral data. The project uses supervised machine learning algorithms, specifically Random Forest and XGBoost, to perform this classification.The goal is to leverage the information contained in features such as age, gender, income, education, loyalty status, promotion usage, and satisfaction score to determine the likelihood of a customer's purchasing behavior. By doing so, businesses can understand the dynamics of their customer base, improve customer segmentation, and design targeted marketing and loyalty strategies.