Capstone Project : Marketing-Airplane Passenger Satisfaction Prediction Using MachineLearningTechniques

Abstract

Customer satisfaction questionnaires are a rich and strong source of information for companies to seek loyalty, customer and client retention, opti-mize resources, and repurchase products. Several advanced machine learning and statistical models have been employed to estimate the customer satisfaction score; however, there is not a single model that can yield the best result in all situations. Ensembles of regression techniques have demonstrated their effective-ness for various applications, where the success of these models lies in the con-struction of a set of single models. Iperformed an experimental study using a real dataset of 90917samples from US airline carrier ‘Falcon airlines’, in order verify the ben-efits of ensemble models for predicting customer satisfaction. Accordingly, in this project I evaluated the following models; Logistic Regression, Decision Tree, Bagging classifier and Random forest. The obtained results indicate that the Random forest performs better in terms of Recall and Precision.