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Food Demand Prediction using Statistical and Machine Learning Models

Jayapal, Sasikumar (2023) Food Demand Prediction using Statistical and Machine Learning Models. Masters thesis, Dublin, National College of Ireland.

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The demand for food is rising faster than the national economy due to population growth, climate change, and digitalization. The food-based industries like restaurants, canteens, catering services, fast food centers, etc., are the ones that handle perishable materials more often. One of the biggest challenges for the food-based industries to managing food orders for their customers. Sometimes, Inaccurate estimation of the food orders can lead to excessive or insufficient food can result in waste of both food and raw materials, as well as ineffective employee management and decreased business profit. This proposed research investigates the appropriate data mining, statistical, and machine learning models to predict the accurate food orders for a restaurant for the upcoming weeks. The machine learning models lasso, ridge, Bayesian ridge regression, SVR, decision tree, random forest, and gradient boosting regression models like Gradient Boosting, XGBoosting, LightGBM, Cat-Boost, and Facebook prophet can be applied to a large dataset with roughly 155 weeks’ worth of food orders gathered from a restaurant in Italy. As a result, foodbased industries like restaurants, canteens, and fast food centers can minimize their operation costs by reducing food and raw material waste and improving customer satisfaction by serving fresh and delicious food.

Item Type: Thesis (Masters)
Muntean, Cristina Hava
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Food Industry
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 18 May 2023 16:47
Last Modified: 18 May 2023 16:47

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