Food Quantity Prediction Using Machine Learning Algorithms

Food Quantity Prediction Using Machine Learning Algorithms is an emerging field that aims to optimize food preparation and reduce waste by accurately forecasting the amount of food required in various settings such as restaurants, cafeterias, and event catering. This study explores the development and implementation of predictive models using machine learning techniques to analyze historical consumption data, environmental factors, and user preferences to estimate food demand. The primary goal is to improve efficiency in food production by minimizing overproduction and underproduction, which can lead to financial losses and increased environmental impact. The research involves collecting and preprocessing data that includes variables such as date, time, weather conditions, special occasions, menu items, and past consumption records. Several machine learning algorithms including linear regression, decision trees, random forests, support vector machines, and neural networks are evaluated for their effectiveness in predicting food quantity The research also highlights the potential of combining machine learning with Internet of Things (IoT) devices to gather real-time data, further refining predictions. In conclusion, machine learning-based food quantity prediction presents a promising approach to enhance operational efficiency, reduce food waste, and promote sustainability in the food industry. This paper contributes to the growing body of knowledge by providing a comprehensive analysis of various algorithms and their applicability to food demand forecasting, paving the way for future advancements and integration into smart food service systems.

Food Quantity Prediction, Machine Learning, Demand Forecasting, Food Waste Reduction, Predictive Modeling, Feature Engineering

1. S. W. Kim, S. Y. Park, and K. H. Kim, “A Machine Learning Approach for Food Demand Forecasting
in Restaurants,” International Journal of Hospitality Management, vol. 82, pp. 45–53, 2019.
2. J. Liu, Y. Zhang, and X. Li, “Food Waste Reduction Using Predictive Analytics Based on Machine Learning Models,” Journal of Cleaner Production, vol. 245, no. 118859, 2020.
3. A. Gupta, R. Singh, and P. Kumar, “Demand Forecasting in Food Supply Chains Using Random
Forest and Neural Networks,” Computers and Electronics in Agriculture, vol. 177, p. 105692, 2020.
4. M. R. Islam, T. A. Hossain, and M. S. Uddin, “Predictive Modeling of Food Demand Using Support Vector Machines and Ensemble Methods,” Applied Soft Computing, vol. 101, p. 107013, 2021.
5. K. Lee, D. Park, and H. Kim, “Feature Engineering Techniques for Food Consumption Prediction
Using IoT Data,” Sensors, vol. 20, no. 16, p. 4598, 2020.
6. P. S. Fernandes and M. A. P. Santos, “Machine Learning Algorithms for Demand Forecasting in
Food Services: A Comparative Study,” Journal of Food Engineering, vol. 285, p. 110120, 2020.
7. R. K. Singh and A. Kumar, “An IoT-Enabled Machine Learning Framework for Dynamic Food
Demand Prediction,” IEEE Internet of Things Journal, vol. 7, no. 11, pp. 10912–10920, 2020.
8. L. Chen and W. Zhao, “Neural Network-Based Forecasting of Food Consumption Patterns in
Institutional Catering,” Neural Computing and Applications, vol. 32, pp. 12095–12105, 2020.
9. H. Zhang, X. Wang, and Y. Liu, “Data-Driven Food Demand Forecasting to Reduce Waste in Retail
and Food Service,” Sustainable Cities and Society, vol. 62, p. 102384, 2020.
10. T. Nguyen and J. Lee, “Applying Machine Learning for Food Demand Forecasting: Challenges and Opportunities,” Journal of Artificial Intelligence Research, vol. 68, pp. 459–480, 2020.