Work

Predictive analysis of wholesale customer purchases using machine learning models

Year: 2025

Type: article

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.

Source: Supply Chain Research

DOI: 10.59429/scr.v2i2.9929

Authors: Ranu ., Nitin Kumar Mishra, Prerna Jain, RenukS. Namwad

Institutions: NIILM University, Lovely Professional University, Guru Gobind Singh Indraprastha University, Gitarattan International Business School

Language: English

Topic: Forecasting Techniques and Applications

Subfield: Management Science and Operations Research

Field: Decision Sciences

Domain: Social Sciences

SDG: Zero hunger

Open Access status: bronze

OpenAlex ID: W4413529472

FWCI:

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