Analyzing Customer Satisfaction through Demographic and Product-Based Modeling

Abstract

The work presents a comprehensive analysis of customer satisfaction based on a synthetic dataset that incorporates various demographic features. Utilizing statistical methods and machine learning techniques, the study aims to explore the relationships between customer demographics and satisfaction scores. The methodology includes descriptive statistics, hypothesis testing via t-tests, correlation analysis, and regression modeling. Additionally, classification algorithms such as Logistic Regression and Decision Tree are employed to predict customer satisfaction levels. The findings highlight significant differences in satisfaction scores across product types and demonstrate the effectiveness of demographic features in predicting customer satisfaction.