Application of K-Means, Random Forest, and Linear Regression to Improve the Accuracy of Kaggle E-Commerce Shopping Behavior Analysis - Azzahra Risa Putri; Anggraini Dwi Olivia; Virha Charoline Josica (Journal of Sustainable Software Engineering and Information Systems, 2026)
Background: Analyzing customer shopping behavior is essential for improving e-commerce marketing strategies. The use of machine learning enables the identification of purchasing patterns and enhances predictive accuracy in understanding customer preferences. Aims: This study aims to improve model accuracy and classification performance in analyzing customer shopping behavior using the shopping_behavior_updated.csv dataset from Kaggle. The scope includes the application of both supervised and unsupervised learning techniques for segmentation, classification, and prediction tasks. Methods: Three machine learning algorithms were applied: K-Means for customer segmentation, Random Forest Classifier for product category prediction, and Linear Regression for estimating purchase amounts. The research involved systematic preprocessing steps, including data cleaning, encoding, scaling, and feature engineering to enhance data quality and model interpretability. Result: The results show that the optimized Random Forest model achieved 100% accuracy. K-Means clustering produced five distinct customer segments with an inertia value of 41,928.17 and a silhouette score of 0.065. However, the Linear Regression model demonstrated poor performance with an R² value of -0.02. Conclusion: The findings indicate that integrating supervised and unsupervised learning methods is effective in identifying customer purchasing patterns and can contribute to improving e-commerce marketing strategies, although not all predictive models yield optimal performance.