In this project, we explore the application of predictive modeling to address a common challenge faced by many service providers, which is Customer Satisfaction. The focus is on analyzing data related to airline passenger satisfaction to develop a model that can effectively predict customer satisfaction levels. This report outlines the methodology and findings of our analysis, aiming to provide insights that can be applied broadly across various industries.
Used Pearson correlation to identify key factors impacting satisfaction. Built and optimized Gaussian Naive Bayes and Logistic Regression models for accurate customer satisfaction classification and evaluated models for accuracy.