AI-driven ensemble three machine learning to enhance digital marketing strategies in the food delivery business

Purpose: This study focuses on the use of ensemble machine learning (ML) in digital marketing for the food delivery business. Methodology: Artificial intelligence (AI) techniques are used to analyze customer data, identify customer preferences, and predict customer behavior to provide AI-based recommendations. The ensemble method combines the outputs of decision trees, na & iuml;ve Bayes, and nearest neighbor algorithms to generate a single prediction. Findings: The accuracy matrix plots for both the decision tree and nearest neighbor algorithms yielded perfect predictions, with an accuracy of 100.000% and 0.000 error, respectively. Meanwhile, the na & iuml;ve Bayes algorithm had an overall accuracy matrix of 97.175%, with a 0.028 error, indicating successful identification of the correct labels across all classes with a high level of accuracy. Originality: The majority voting method with a probability success rate greater than 90% can potentially integrate models into this process while utilizing less than half the randomized data, blended with customer experience data, thus reducing customer irritation. The driven ensemble of three ML algorithms is shown to successfully improve digital marketing strategies in the food delivery business by decreasing time and costs.