Improvisation of Food Delivery Business by Leveraging Ensemble Learning with Various Algorithms

Food delivery has continuously been one of the most profitable and rapidly developing industries. As technology advances, the food delivery industry must develop new and creative services to give clients with better and timely services. The goal of this study is to investigate the application of ensemble machine learning (ML) methodologies in digital marketing for the food delivery industry. Artificial intelligence (AI) algorithms are used to evaluate customer data in order to uncover preferences and anticipate behavior, enabling AI-driven suggestions. The results show that the decision tree and nearest neighbor algorithms obtain flawless predictions, while the Naive Bayes algorithm gets a high overall accuracy of 97.175%. To improve customer satisfaction even further, study suggest a majority vote strategy that combines various models by mixing randomized data with real-world customer experiences, significantly lowering consumer aggravation. Surprisingly, the combination of three ML algorithms boosts digital marketing approaches for food delivery companies, resulting in lower resource expenditure while producing exceptional outcomes. This work adds to the body of knowledge by proving the efficacy of AI-driven ensemble learning in optimizing marketing tactics for the food delivery business. Thus strategy enables organizations to deliver better and more timely services by identifying client preferences and behavior, therefore cementing their position in this competitive market.