Florentina Yuni Arini, Lyon Ambrosio Djuanda, Ananda Hisma Putra Kristianto, Muthia Nis Tiadah, Aufa Putra Wicaksono, Fatih Akbar Alim Putra

Performance Evaluation of Gradient Boosting Techniques for Predicting Customer Purchase Decisions

Introduction

Performance evaluation of gradient boosting techniques for predicting customer purchase decisions. Evaluate XGBoost, LightGBM, and CatBoost for customer purchase prediction using demographic and review data. CatBoost achieves 78.26% accuracy, offering insights for e-commerce.

Abstract

Customer purchase prediction remains a critical challenge in e-commerce and retail analytics, with significant implications for marketing strategies and business revenue. This research provides a detailed comparative evaluation of advanced gradient boosting techniques XGBoost, LightGBM, and CatBoost to predict customer purchasing behavior using review trends and demographic factors. The study employed a dataset of 100 customer records with attributes such as age, gender, review quality, and education level. Through systematic feature engineering, including age group categorization and categorical feature combinations, as well as addressing class imbalance using the Synthetic Minority Oversampling Technique (SMOTE), all three models were trained and evaluated using default hyperparameters with optimal settings. The experimental results show that CatBoost achieved the best performance, with 78.26% accuracy, 0.8011 precision, 0.7826 recall, and a 0.7775 F1-score, outperforming LightGBM (73.91% accuracy) and XGBoost (60.87% accuracy). The evaluation includes confusion matrix analysis, precision–recall metrics, and visual comparisons across all performance dimensions. These findings provide valuable insights for practitioners selecting appropriate machine learning algorithms for customer purchase prediction tasks, particularly in scenarios involving limited datasets and categorical features. This research contributes to the growing body of literature on the use of gradient boosting techniques for predicting consumer behavior and offers important practical implications for e-commerce applications. These findings offer important contributions to machine learning applications in customer behavior prediction.

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