Academic Journal of Business & Management, 2024, 6(12); doi: 10.25236/AJBM.2024.061217.
Wei Wu
AWS EKS Team, Amazon, Seattle, Washington, 98121, United States
The purpose of this study is to optimize the wind risk customer prediction of e-commerce platform by constructing a combination model based on regression model and neural network. According to the real transaction data of the e-commerce platform, the research shows that the accuracy of the combination model has increased by 15%, and the recall rate has increased by 20%. Feature selection and cross-validation are used in data analysis to ensure the reliability and applicability of the model. The results show that the model can effectively identify high-risk customers and help the platform to develop more targeted risk management strategies. The research provides a new idea for future risk prediction and has important practical significance. Our work not only provides a scientific basis for the practice of risk control in the e-commerce industry, but also provides inspiration for researchers in related fields.
Risk Customer Prediction, Regression Model, Neural Network, Combination Model, E-Commerce
Wei Wu. Combination Model Optimization and Empirical Analysis of Risk Customer Prediction in E-commerce Platform Based on Regression Model and Neural Network. Academic Journal of Business & Management (2024) Vol. 6, Issue 12: 127-131. https://doi.org/10.25236/AJBM.2024.061217.
[1] Tandra S, Manashty A. Probabilistic feature selection for interpretable random forest model[C]. Advances in Information and Communication: Proceedings of the 2021 Future of Information and Communication Conference (FICC), Volume 2. Springer International Publishing, 2021: 707-718.
[2] Yoshino N. Taghizadeh-Hesary F. Hometown Investment Trust Funds: An Analysis of Credit Risk [J]. Social Science Electronic Publishing, 2014(505).DOI:10.2139/ssrn.2533789.
[3] Oja T. Prediction Power of Logistic Regression (LR) and Multi-Layer Perceptron (MLP) Models in Exploring Driving Forces of Urban Expansion to Be Sustainable in Estonia[J]. Sustainability, 2021, 14.DOI:10.3390/su14010160.
[4] Nielsen, Michael A. Neural networks and deep learning [J]. San Francisco, CA, USA: Determination press, 2015, 25.
[5] Zeiler M D. Fergus R. Visualizing and Understanding Convolutional Neural Networks[J]. Springer International Publishing, 2013. DOI: 10.1007/978-3-319-10590-1_53.
[6] Jalali V. Leake D. Forouzandehmehr N. Learning and applying adaptation rules for categorical features: An ensemble approach [J]. AI Communications, 2017, 30(3-4):193-205.