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Predicting Loan Defaults Using Logistic Regression

Selena is an 18 year-old high school student from Saratoga, California.
High School
Lynbrook High School
Student review
Polygence was a great experience for me. Jiying was really prepared in all of the sessions and spread the workload out evenly throughout the 10 weeks so that writing the final report wasn't too overwhelming. During each meeting, I learned about some new aspect of modeling or analyzing data that would advance the project. For homework, Jiying kept the options pretty open. I had the freedom to explore whichever part of the data I was most drawn to, and then we would discuss it in the next session. Overall, I had a lot of fun and learned interesting concepts beyond the scope of a typical high school course!
Project description

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.

Predicting Loan Defaults Using Logistic Regression
Project outcome

Selena published her research on Medium's Towards Data Science Publication and at The Journal of Student Research.

MS Master of Science
Arts, Quantitative, Comp Sci, Biology
Anything related to data science, machine learning and AI, social sciences, and the arts!

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