Paper Title :Meal Delivery Forecasting Using Machine Learning Models: A Comparative Analysis

Author :Subhash C Sharma, Divyansh Sharma, Millie Pant

Article Citation :Subhash C Sharma ,Divyansh Sharma ,Millie Pant , (2023 ) " Meal Delivery Forecasting Using Machine Learning Models: A Comparative Analysis " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 80-84, Volume-11,Issue-6

Abstract : Day by day supply chain is becoming more and more competitive. It is now necessary for organizations to accurately predict their customers' behavior to deal with unexpected surges and stillness in the demands of products. With new approaches to data collection coming up, the volume of data generated is large in number and a variety of nature. So, it becomes difficult for traditional methods to make forecasts; for the same reason, the idea of using more recent Machine Learning techniques is being explored and analyzed in this paper. The four models, namely, Linear Regression, Neural network, Random Forest, and Decision Tree, are applied to forecast demand for a meal delivery company. Three performance metrics, namely, mean absolute error, mean squared error, and variance score are used to evaluate the performance of each model. The results showed that the random forest algorithm performed better among all the models with MAE value of 69.2168, MSE value of 21672.019, and explained variance score value of 0.859. Keywords - data analytics, machine learning, supply chain, demand forecasting

Type : Research paper

Published : Volume-11,Issue-6

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