Customer Behaviour Analysis and Predictive Modelling in Supermarket Retail: A Comprehensive Data Mining Approach

Abstract

In the dynamic landscape of supermarket retail, understanding customer behavior is paramount for optimizing business strategies and enhancing profitability. This paper presents a comprehensive data mining approach to analyze customer behavior and build predictive models within the supermarket retail domain. Leveraging advanced data analytics techniques, our methodology encompasses data preprocessing, exploratory data analysis, feature engineering, model selection, and evaluation. This paper presents a comprehensive approach to customer behavior analysis and predictive modelling within the context of supermarket retail. We delve into the intricacies of data mining methodologies, exploring how retailers can leverage diverse datasets to uncover valuable insights and build predictive models that drive business growth and customer satisfaction. From data preprocessing to model evaluation, each step in the process is meticulously examined, highlighting best practices and key considerations for effective implementation.