Prediction of financial customer buying behavior based on machine learning

Abstract

This study investigates the prediction of online purchasing behavior on the palm life APP through comprehensive analysis of customer operation logs, attribute sets, and purchase labels. Utilizing advanced feature engineering techniques, including time-based metrics, frequency analyses, and category-specific operations, we constructed a robust feature system comprising 61 associated features. The predictive models, leveraging Logistic Regression and LightGBM algorithms, were evaluated using cross-validation and AUC scores, demonstrating strong generalization capabilities and effectiveness in predicting customer behavior. Findings highlight the significance of personalized recommendations and targeted marketing strategies in enhancing customer engagement and optimizing operational efficiencies for e-commerce platforms. This research contributes to both theoretical advancements in consumer behavior prediction and practical implications for enhancing customer experience and service personalization in fintech applications.