Projected default classification using logistic regression and determine an appropriate classification threshold.
• Selected fitted parameters with predictive power via a process of data cleaning, data visualization, and data analysis.
• Tested the model accuracy and determined the optimized model through building confusion matrices, calculating information criteria, and set the best threshold via the ROC curve as a tradeoff between sensitivity and specificity.