ABSTRACT
In today's world, a variety of enterprises need to prioritise customer pleasure. The expansion of the firm, customer perception, and future income all rank right below the essence of customer happiness. Therefore, understanding and meeting customer expectations is not the only top concern for many businesses; it's also critical to address complaints and grievances in order to retain a base of devoted and consistent consumers. The goal of the article is to improve customer contentment by using machine learning and artificial intelligence to understand a prediction approach on a dataset of airfare satisfaction. The study makes the assumption that genetic algorithms, including the dragonfly and particle swarm optimisation methods, may be used to optimise feature extraction. It contrasts statistical test feature extractions with pertinent feature developments. The purpose of the study is to create a comparable prediction model for the analysis of customer satisfaction in the aviation sector.
