Analyzing E-Commerce Product Trends Using Data Mining Tools

Abstract

E-commerce platforms produce extensive data, rendering trend research essential for comprehending consumer behaviour and refining product plans. This research presents a framework for examining e-commerce product trends through sophisticated data mining techniques, including scaling normalization, filter-based feature selection, and neural networks. Scaling normalization guarantees uniform data representation by converting numerical attributes such as price, sales, and ratings into equivalent scales, hence enhancing the efficacy of subsequent models. Filter-based feature selection evaluates features based on their statistical significance, including mutual information and correlation, facilitating the identification of critical attributes that affect product trends. Neural networks are utilized to identify intricate patterns and non-linear interactions among many dimensions of e-commerce data. The proposed system is validated using real-world datasets, showing substantial enhancements in trend prediction accuracy and feature interpretability relative to conventional techniques. This research emphasizes the capability of data mining techniques to provide firms with actionable insights, hence improving decision-making in dynamic e-commerce settings.