Food Demand Forecasting Using Machine Learning And Statistical Analysis

Authors

  • Agarwal M Department of Computer Engineering, Sanjivani College of Engineering, Savitribai Phule Pune University, Kopargaon, India
  • Kulkarni S Department of Computer Engineering, Sanjivani College of Engineering, Savitribai Phule Pune University, Kopargaon, India
  • Nagre V Department of Computer Engineering, Sanjivani College of Engineering, Savitribai Phule Pune University, Kopargaon, India
  • Joshi A Department of Computer Engineering, Sanjivani College of Engineering, Savitribai Phule Pune University, Kopargaon, India
  • Nagpure D Department of Computer Engineering, Sanjivani College of Engineering, Savitribai Phule Pune University, Kopargaon, India

DOI:

https://doi.org/10.26438/ijcse/v10i5.2529

Keywords:

Machine Learning, Prediction, Random Fores, , XgBoost, Support Vector Machine, Clustering

Abstract

Food loss is considered a problem because food loss refers to the loss of resources such as water, soil nutrition, and investment. Food shortages lead to food shortages. This means that poor people around the world are being deprived of food as the cost of available food is increasing. Providing fresh food is one of the major constraints which is already considered by various meal provider agents or companies. Many of them want to get an estimated number of stocks for given respective times, which could help them understand patterns and stocks required. Meal delivery companies want to know the estimated number of stocks that would be delivered or manufactured over the given period based upon previous data. Forecasting process is useful in various domains like weather forecasting, restaurants, retailing etc. It determines the expected demand for the future and establishes the level of readiness required on the supply side to meet the demand. This paper represents machine learning algorithms as an application to solve such problem with forecasting number of orders for given week and meal using algorithms Random Forest, XgBoost, Support Vector Machine, etc. with optimized results.

References

[1] D. Adebanjo and R. Mann, “Identifying problems in forecast-ing consumer demand within the fast paced commodity sector” Benchmarking: An International Journal, 7(3):223– 230, 2000.

[2] Md.Erfanul Hoque, Aerambamoorthy Thavaneswaran, Srimantoorao S. Appadoo, “A Novel Dynamic Demand Forecasting Model for Rresilient Supply Chains using Machine Learning.”,IEEE ISSN:0730-3157,2021

[3] K Siva Rama Krishna, Pooja Pasula, T.Kavyakeerthi, I.Karthik, “Identifying Demand Forecasting using Machine Learning for Business Intelligence.”, IEEE, ISBN:978-1-6654-1029-8, 2022

[4] Kenji Shinoda, Masato Yamada, Motoki Takanashi, Tetsuya Tsuboi, “Prediction of Restaurant Sales during high demand states using population statistical data.”, IEEE , ISBN:978-1-6654-2397-7, 2021

Downloads

Published

2022-05-31
CITATION
DOI: 10.26438/ijcse/v10i5.2529
Published: 2022-05-31

How to Cite

[1]
M. Agarwal, S. Kulkarni, V. Nagre, A. Joshi, and D. Nagpure, “Food Demand Forecasting Using Machine Learning And Statistical Analysis”, Int. J. Comp. Sci. Eng., vol. 10, no. 5, pp. 25–29, May 2022.

Issue

Vol. 10 No. 5 (2022): IJCSE May Edition

Section

Research Article

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.