International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | Machine Learning Strategies for Food Demand Forecasting and Delivery Time Prediction |
|---|---|
| ABSTRACT | This paper discusses machine learning techniques used in food demand forecasting and food delivery prediction. Models such as Random Forest, XGBoost, LightGBM, and Gradient Boosting are analyzed. The study also highlights the importance of feature engineering and real-time prediction systems in improving operational efficiency.This paper discusses machine learning techniques used in food demand forecasting and food delivery prediction. Models such as Random Forest, XGBoost, LightGBM, and Gradient Boosting are analyzed. The study also highlights the importance of feature engineering and real-time prediction systems in improving operational efficiency.This paper discusses machine learning techniques used in food demand forecasting and food delivery prediction. Models such as Random Forest, XGBoost, LightGBM, and Gradient Boosting are analyzed. The study also highlights the importance of feature engineering and real-time prediction systems in improving operational efficiency.This paper discusses machine learning techniques used in food demand forecasting and food delivery prediction. Models such as Random Forest, XGBoost, LightGBM, and Gradient Boosting are analyzed. The study also highlights the importance of feature engineering and real-time prediction systems in improving operational efficiency. |
| AUTHOR | PROF. USHA K, AMRUTHA B K, CHINMAYI C S, SAKSHI S PATIL, PRATEEK M HALEMANI, AKASH L, MITHUN V Department of Computer Science and Engineering, JIT College, Davanagere, Karnataka, India |
| VOLUME | 184 |
| DOI | DOI: 10.15680/IJIRCCE.2026.1405086 |
| pdf/86_Machine Learning Strategies for Food Demand Forecasting and Delivery Time Prediction.pdf | |
| KEYWORDS | |
| References | 1. Chaudhari et al. (2025). Forecasting and modelling of food demand supply chain using machine learning. 2. Jayapal, S. (2023). Food demand prediction using statistical and machine learning models. 3. Shinde & Singh (2025). Food delivery time prediction using machine learning. 4. Panda & Mohanty (2023). Time series forecasting and modeling of food demand supply chain. 5. Fattah et al. (2018). Forecasting of demand using ARIMA model. |
