Integrated Machine Learning Approaches for E-commerce Customer Behavior Prediction
These authors contributed equally.
- DOI
- 10.2991/aebmr.k.220307.166How to use a DOI?
- Keywords
- E-commerce; Classification; Machine Learning; Customer behavior
- Abstract
How to predict the customers’ behavior is always a crucial problem for enterprises in E-commerce. In this paper, a data set containing the behavior data for 2019 October and November from a large multi-category online store has been used as well as diverse Machine Learning algorithms are used in Python to precisely predict the behaviors of customers. By extracting 5 datasets containing 10,000 observations out of one billion observations and applying the concepts of Label Encoder, this paper was able to build the models and hence analyze this paper’s data. As a result, this paper found that Pipeline and Random Forest works the best that both of them perform a prediction accuracy of 96% which is significantly greater than other algorithms. In addition, the feature of user id and user session present the greatest importance among all the features. On the customers’ side, they would focus more on the price-performance ratio, which is price, because it would help customers with making purchasing decisions. This paper were able to recommend individually customized products for each single person based on their personal preference and emphasize the features of data, user id and user session, that sellers should be focus on.
- Copyright
- © 2022 The Authors. Published by Atlantis Press International B.V.
- Open Access
- This is an open access article under the CC BY-NC license.
Cite this article
TY - CONF AU - Yuran Dong AU - Junyi Tang AU - Zhixi Zhang PY - 2022 DA - 2022/03/26 TI - Integrated Machine Learning Approaches for E-commerce Customer Behavior Prediction BT - Proceedings of the 2022 7th International Conference on Financial Innovation and Economic Development (ICFIED 2022) PB - Atlantis Press SP - 1008 EP - 1015 SN - 2352-5428 UR - https://doi.org/10.2991/aebmr.k.220307.166 DO - 10.2991/aebmr.k.220307.166 ID - Dong2022 ER -