SUMMARY Credit card companies are leveraging data analysis to gain a deeper understanding of the factors driving the variation in customer monthly credit card balances. The primary aim is to enhance customer service and optimize business operations through data-informed decision-making.
GOALS This study involves the utilization of regression modeling through Analysis of Variance (ANOVA) to achieve the following objectives:
- Identification of key determinants influencing credit card activity within the organization.
- Selection of optimal factors for accurately estimating monthly customer credit card balances.
- Uncovering the significant factors contributing to the observed balance levels.
- Identification and analysis of the top five factors exerting an influence on credit card balances.
PROJECT FLOW
- Initial identification of potential variables was performed by subjecting prediction models to regression analysis.
- Assessment of the prediction model involved evaluating metrics such as the standard error of estimate and coefficient of determination (R-squared).
- The statistical significance of the model was established using hypothesis testing, including examination of Type 1 errors, assessment of collinearity, and analysis of p-values. Tools and Concepts: The analytical toolkit for this project includes ANOVA, predictive modeling, regression analysis, hypothesis testing, considerations of Type 1 errors, collinearity assessment, p-value analysis, determination of R-squared, and evaluation of the F-value.