  In this paper, we performs a credit risk analysis, on the data of past loan
applicants of a company named Lending Club. The calculation required the use of
exploratory data analysis and machine learning classification algorithms,
namely, Logistic Regression and Random Forest Algorithm. We further used the
calculated probability of default to design a credit derivative based on the
idea of a Credit Default Swap, to hedge against an event of default. The
results on the test set are presented using various performance measures.
