Machine Learning Modeling for Forecasting Repeat Purchases in Online Shopping
DOI:
https://doi.org/10.57152/malcom.v4i3.1388Keywords:
Data Analysis, Data Modeling, Machine Learning, Online Shopping, Repeat Purchase ForecastAbstract
Online shopping merchants will conduct a series of marketing activities to increase customers, but in many cases, most of the new customers will not make repeat purchases, which is not conducive to the long-term interests of the merchants. Therefore, it is important for merchants to target users who are more likely to repurchase, as this can reduce marketing costs and increase ROI. Based on the dataset provided by the online shopping website, this paper conducts mining and exploratory analysis of the data, utilizes feature engineering methodology, and modeling analysis using LightGBM, Logistic, Xgboost for machine learning modeling. Meanwhile, parameter optimization and model evaluation verification are performed, Finally, the comparative analysis resulted in LightGBM as the best prediction model, will provide efficient marketing decisions for the operation of online shopping stores.
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