Title: Intelligent computational techniques of machine learning models for demand analysis and prediction

Authors: G. Naveen Sundar; K. Anushka Xavier; D. Narmadha; K. Martin Sagayam; A. Amir Anton Jone; Marc Pomplun; Hien Dang

Addresses: Department of CSE, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of CSE, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of CSE, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore, India ' Department of Computer Science, University of Massachusetts Boston, MA, USA ' Department of Computer Science, University of Massachusetts Boston, MA, USA; Faculty of Computer Science and Engineering, Thuyloi University, Hanoi, Vietnam

Abstract: In the proposed model, a novel approach is introduced to discover an optimal machine learning model for food demand prediction. To create an exemplary model, we used twelve different machine learning models to analyse and interpret the historical data. Feature engineering techniques have been deployed to yield better performance. All methods were evaluated using RMSE evaluation metrics to determine the optimal model. Our methodology is one of its kind to reduce the error rate to a marginal level. The novelty of our research is that the root mean square error (RMSE) value for the demand prediction was reduced to 2.61e-16 using linear regression, thus achieving a better performance. The random forest, decision tree, and extreme gradient boosting regression also performed well, producing an RMSE value of 1.42e-9, 1.93e-15, and 4.87e-18 respectively. The predictive power of the system was 100% for R-squared metrics.

Keywords: demand prediction; machine learning; linear regression; feature extraction.

DOI: 10.1504/IJIIDS.2023.128288

International Journal of Intelligent Information and Database Systems, 2023 Vol.16 No.1, pp.39 - 61

Received: 11 Apr 2022
Accepted: 25 Sep 2022

Published online: 16 Jan 2023 *

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