Anticipating Consumer Demand using ML

• Published in: International journal for research in applied science and engineering technology Vol. 11; no. 4; pp. 1053 - 1058
• Main Authors: Devi, P. Rama, Meesala, Srujitha, Reddy, Ramya, Senapathi, Kushal, Kolala, Udaya
• Format: Journal Article
• Language: English
• Published: 30.04.2023
• ISSN: 2321-9653; 2321-9653
• DOI: 10.22214/ijraset.2023.50283

Demand forecasting is essential for every growing online business. Without efficient demand forecasting systems in place, it might be next to impossible to always have the right amount of stock on hand. Because a food delivery service deals with a high volume of perishable raw materials, it is critical for the company to accurately forecast daily and weekly demand. If a warehouse has too much inventory, there is a greater likelihood of wastage, and if it has too little, there may be shortages, which would encourage customers to turn to your competitors. Therefore, predicting demand is one of the important tasks to be done. The project represents a food delivery company that operates in multiple cities. This particular company has various fulfillment centres in these cities for dispatching meal orders to their customers. Its objective is to anticipate consumer demand and the goal is to build a predictive regression model to assist the client in projecting demand for the following weeks so that these centres can organize their raw material stock properly, with the usage of various Machine Learning and Deep Learning Models and Techniques. For that purpose, there are various tools, techniques and methods are proposed. Linear regression model, Random Forest, XG Boosting, Decision tree is some of the models performed for getting the highest accuracy.