In this Jupyter Notebook we will be using a subset of the LendingClub DataSet obtained from Kaggle: https://www.kaggle.com/wordsforthewise/lending-club

LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California.[3] It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission (SEC), and to offer loan trading on a secondary market. LendingClub is the world's largest peer-to-peer lending platform.

Given historical data on loans given out with information on whether or not the borrower defaulted (charge-off), can we build a model that can predict wether or nor a borrower will pay back their loan. This way in the future when we get a new potential customer we can assess whether or not they are likely to pay back the loan.

We begin by reading the file, performing some explanatory data analysis, along with visualizations, then we do some data preprocessing to prepare our data for the ML model and finally we create and evaluate different types of ML algorithms.