Optimization of Food Inputs in Restaurants in Metropolitan Lima Through Prediction and Monitoring Based on Machine Learning

This work presents the development of a web-based monitoring and prediction system designed to optimize food supply in restaurants in Metropolitan Lima, addressing challenges such as efficient inventory management and food waste reduction. The solution employs six Machine Learning models (Random Forest, Gradient Boosting, Ridge Regression, Lasso Regression, Linear SVR, and Neural Network), evaluated using accuracy metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Among the models, Gradient Boosting demonstrated the best performance, with an MSE of 0.0032, RMSE of 0.057, and MAE of 0.027, outperforming the others in terms of accuracy, including Neural Network and Random Forest, which also offered competitive results. While the approach was developed in the specific context of Metropolitan Lima, the applied methods and obtained results can be adapted to other urban markets with similar dynamics, demonstrating broader applicability. This system not only promotes more efficient and sustainable inventory planning, but also contributes to the economic growth of restaurants by optimizing resources and improving their profitability in a highly competitive environment.