Predicting Loan Defaults Using Logistic Regression
Selena came to Polygence interested in actuary science (statistics, data modeling, and uncertainty). She had become passionate about this subject after enrolling in classes through FBLA such as Securities & Investments and Insurance & Risk Management. Having taken AP Calc BC and AP statistics, she came to Polygence with an eye towards making her own statistical model, allowing her to develop her data analysis and coding skills. For her project, Selena used known and unknown features pertaining to a loan candidate and the loan to predict the risk of defaulting on a loan through statistical modeling methods in R.