Predictive analysis of wholesale customer purchases using machine learning models
Ranu .
Department of Mathematics, NIILM University, Haryana, Zip-136027, INDIA
Nitin Kumar Mishra
Department of Mathematics, Lovely Professional University, Phagwara, Punjab, Zip-144411, INDIA
Prerna Jain
Gitarattan International Business School, Guru Govind Singh Indraprastha University, Delhi, Zip-110078, INDIA
RenukS. Namwad
Department of Mathematics, Lovely Professional University, Phagwara, Punjab, Zip-144411, INDIA
DOI: https://doi.org/10.59429/scr.v2i2.9929
Keywords: Wholesale; customers; purchasing patterns; machine learning models; forecasting accuracy; Generalised Linear Models (GLM)
Abstract
To forecast future purchases across several product categories—including fresh milk, groceries, frozen foods, detergents, paper, and delicatessen—this study looks at the purchasing patterns of wholesale clients using machine learning models. Support Vector Machines, Generalised Linear Models, and Linear Regression were among the predictive models used. RMSE, MAPE, and R² metrics were used to assess each model's performance and ascertain its correctness. The results show that when it came to forecasting consumer purchases, GLM performed better than other models, including LR, SVM, RT, and ER.
References
1. Kotler, P., et al. (2019), "Customer Segmentation with Clustering Algorithms," Journal of Retail Analytics.
2. Nguyen, T., et al. (2021), "Customer Segmentation in Wholesale Using Decision Trees," Wholesale Business Journal.
3. Chen, Y., et al. (2018), "Neural Networks for Predicting Customer Behaviour," Journal of Machine Learning in Retail.
4. Kaggle (2020), "Customer Segmentation Competition Results," Kaggle Competitions.
5. Agrawal, S., et al. (2020), "Comparing Regression Models and Ensemble Learning in Retail Forecasting," Journal of Data Science.
6. Mohamed, A., & Ibrahim, M. (2019), "Regression Trees for Retail Customer Behaviour Analysis," Retail Business Journal.
7. Liu, D., & Tan, W. (2017), "Generalised Linear Models for Wholesale Customer Predictions," Journal of Data Analytics.
8. Zhang, X., et al. (2019), "GLMs and Variability in Customer Purchasing Patterns," Wholesale Analytics Quarterly.
9. Nguyen, Q., & Tran, H. (2020), "Deep Learning Models vs. Traditional Methods in Wholesale Prediction," Journal of Artificial Intelligence Applications.
10. Smith, J., et al. (2019), "Predicting Customer Churn with Decision Trees," Retail Marketing Insights.
11. Kohavi, R., et al. (2020), "Boosted Decision Trees in Customer Retention," Journal of Retail Predictive Analytics.
12. Rai, A., et al. (2021), "Time Series Forecasting in Wholesale: ARIMA vs. LSTM," Journal of Statistical Forecasting.
13. Martinez, F., et al. (2020), "Improving SVM Models with Feature Engineering for Retail Predictions," Journal of Machine Learning Research.
14. Wilson, P., et al. (2018), "Machine Learning Models for Grocery Purchase Prediction," Retail Data Science Journal.
15. Gartner (2021), "Predictive Analytics in Retail: 2021 Trends," Gartner Reports.
16. Chen, L., Zhang, Y., & Torres, R. (2023).Gradient Boosting and Deep Learning for Customer Purchase Forecasting.Journal of Retail Data Science, 12(3), 45–59. https://doi.org/10.1016/j.jrds.2023.04.005
17. Wang, Y., & Patel, S. (2022).Improving Wholesale Customer Segmentation using XGBoost.Applied AI in Business, 8(2), 101–113. https://doi.org/10.1080/aib.2022.0015
18. Lee, J., Gupta, R., & Kim, S. (2024).LightGBM vs. Neural Networks for Retail Demand Prediction.International Journal of Data Science and Analytics, 14(1), 22–34. https://doi.org/10.1007/s41060-024-00234-7