IEEE Style Citation: Monika Agarwal, Samarth Kulkarni, Vaishnavi Nagre, Aanchal Joshi, Damini Nagpure, “Food Demand Forecasting Using Machine Learning And Statistical Analysis,” International Journal of Computer Sciences and Engineering, Vol.10, Issue.5, pp.25-29, 2022.
MLA Style Citation: Monika Agarwal, Samarth Kulkarni, Vaishnavi Nagre, Aanchal Joshi, Damini Nagpure "Food Demand Forecasting Using Machine Learning And Statistical Analysis." International Journal of Computer Sciences and Engineering 10.5 (2022): 25-29.
APA Style Citation: Monika Agarwal, Samarth Kulkarni, Vaishnavi Nagre, Aanchal Joshi, Damini Nagpure, (2022). Food Demand Forecasting Using Machine Learning And Statistical Analysis. International Journal of Computer Sciences and Engineering, 10(5), 25-29.
BibTex Style Citation:
@article{Agarwal_2022,
author = {Monika Agarwal, Samarth Kulkarni, Vaishnavi Nagre, Aanchal Joshi, Damini Nagpure},
title = {Food Demand Forecasting Using Machine Learning And Statistical Analysis},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {5 2022},
volume = {10},
Issue = {5},
month = {5},
year = {2022},
issn = {2347-2693},
pages = {25-29},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=5463},
doi = {https://doi.org/10.26438/ijcse/v10i5.2529}
publisher = {IJCSE, Indore, INDIA},
}
RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v10i5.2529}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=5463
TI - Food Demand Forecasting Using Machine Learning And Statistical Analysis
T2 - International Journal of Computer Sciences and Engineering
AU - Monika Agarwal, Samarth Kulkarni, Vaishnavi Nagre, Aanchal Joshi, Damini Nagpure
PY - 2022
DA - 2022/05/31
PB - IJCSE, Indore, INDIA
SP - 25-29
IS - 5
VL - 10
SN - 2347-2693
ER -
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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.
Key-Words / Index Term
Machine Learning, Prediction, Random Forest, XgBoost, Support Vector Machines, Clustering
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
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