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Engineering and Technology OPEN ACCESS

MACHINE LEARNING APPROACHES FOR DEMAND FORECASTING: THE IMPACT OF CUSTOMER SATISFACTION ON PREDICTION ACCURACY

Ashim Chandra Das
Master of Science in Information Technology, Washington University of Science and Technology, USA
Md Shahin Alam Mozumder
Master of Science in Information Technology, Washington University of Science and Technology, USA
Md Amit Hasan
Master of Science in Information Technology, Washington University of Science and Technology, USA
Maniruzzaman Bhuiyan
Satish & Yasmin Gupta College of Business, University of Dallas, Texas
Md Rasibul Islam
Department of Management Science and Quantitative Methods, Gannon University, USA
Md Nur Hossain
Master’s in information technology management, Webster University, USA
Salma Akter
Department of Public Administration, Gannon University, Erie, PA, USA
Md Imdadul Alam
Master of Science in Financial Analysis, Fox School of Business, Temple University, USA
DOI Crossref DOI 10.37547/tajet/Volume06Issue10-06
tajet 2024
VOL. 6 / NO. 10 OCTOBER
FULL TEXT PP. 42-53
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VOLUME 6
ISSUE 10
YEAR 2024
PAGES 42-53
RESEARCH OVERVIEW

Abstract

This study investigates the effectiveness of various machine learning models in predicting product demand based on customer satisfaction data. Four models—Linear Regression, Random Forest, Gradient Boosting, and Support Vector Machine (SVM)—were evaluated using performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R² score. The results indicate that Gradient Boosting achieved the highest accuracy, with an MAE of 2.56, MSE of 12.75, RMSE of 3.57, and R² score of 0.82, effectively capturing the complex, non-linear relationships inherent in customer satisfaction factors. Random Forest also demonstrated strong performance, while Linear Regression and SVM showed limitations in handling intricate datasets. These findings underscore the importance of utilizing advanced machine learning techniques for accurate demand forecasting, highlighting the critical role of customer satisfaction data in enhancing predictive capabilities. The insights gained from this research can guide organizations in optimizing inventory management and improving customer satisfaction in a rapidly evolving market.

zenodo DOI:- https://doi.org/10.5281/zenodo.13908001

RESEARCH TOPICS

Keywords

Product Demand Forecasting Customer Satisfaction Machine Learning

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