Efficient bankruptcy (credit cards) prediction using logistic regression over linear regression with improved accuracy.

To enhance the precision of bankruptcy prediction by implementing Logistic Regression, as opposed to Linear Regression, with a particular focus on efficiency. The study involves a classification approach where Logistic Regression is applied to a sample of 20 cases, while a separate sample of 20 cases is used for Linear Regression. The study configured a Logistic Regression model with specific parameters (alpha, g-power, beta) and aimed to assess its performance with a 95% confidence interval (likely to ensure the results are statistically significant). They compared this to a Linear Regression model. The Logistic Regression achieved a higher accuracy (95.1%) compared to Linear Regression (88.8%) for this task. The mean accuracy detection stands at 0.660 (±2SD), with a significance value of 0.00 (p<0.05). Thus, it is evident that a statistically significant distinction exists between the two groups. The assertion is correct, and it has been conducted through an Independent Sample T-test. The investigation into bankruptcy, the Logistic Regression had greater accuracy (95.3%) while in comparison with the Linear Regression (LR). This research proposes a novel Logistic Regression approach that addresses the limitations of previous studies and achieves higher accuracy. This method empowers users to make more accurate predictions. [ABSTRACT FROM AUTHOR]