The project is about exploratory data analysis(EDA) of a loan dataset from Prosper in R language, which is a part of Udacity Data Analyst Nanodegree.

Click here to see the Prosper Loan EDA report.

Prosper is a leading financial peer-to-peer lending platform company which connects borrowers and investors. In this Prosper loan data set, it roughly contains four kinds of loan data information — Loan Status, Borrower Data, Loan Information Data and Credit Risk Matrices during 2005 to 2014. I started by understanding some stories about Prosper and exploring the individual variables related to my initial question of interest — What factors are connected to defaulted/completed loan? Then I found Prosper have improved their credit measurement system by Prosper Rating from only using Credit Score before. And I continued to make observations on plots through using R language and ggplot2, then I make assumption that maybe Prosper put more weight of Prosper Prosper Score than Credit Score in their Prosper Rating. I kept exploring features related to Prosper Score and relationship between Prosper Score and these features and Loan Status. Eventually, I pick features that I observed that having some trends with Loan Status and Prosper Score. I created a Logistic model to predict the likelihood of a loan would be high risk or completed using these features.

This Prosper loan data set can be download from here which was provided by Udacity. The dataset contains 113,937 loans with 81 variables on each loan, for more variable definition, see the link.

MIT License