Data Visualization

EDA of Loan Data with Python

by Osama Hamdy Osman

Prosper’s Story:

Prosper was founded in 2005 as the first peer-to-peer lending marketplace in the United States. Since then, Prosper has facilitated more than $13 billion in loans to more than 850,000 people.

Through Prosper, people can invest in each other in a way that is financially and socially rewarding. Borrowers apply online for a fixed-rate, fixed-term loan between $2,000 and \$40,000. Individuals and institutions can invest in the loans and earn attractive returns. Prosper handles all loan servicing on behalf of the matched borrowers and investors.

Prosper Marketplace is backed by leading investors including Sequoia Capital, Francisco Partners, Institutional Venture Partners, and Credit Suisse NEXT Fund.

What is the structure of The dataset?

This data set comprises 113,937 loans with 81 attributes on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, borrower employment status, borrower credit history, and the latest payment information.

Before 2009 credit rating named "CreditGrade" “The Credit rating that was assigned at the time the listing went live. Applicable for listings pre-2009 period and will only be populated for those listings.

After July 2009, there’s another measure “ProsperRating (Alpha)” The Prosper Rating assigned at the time the listing was created between AA - HR. Applicable for loans originated after July 2009.

Both variables are imported as object datatype in the dataframe. Yet, It’s more sensible to exhibit them as ordered categorical variables, the matter that will come in handy if any modeling that involves such variables is pondered.

worth noting that the dataset was last updated 03/11/2014

What is/are the main feature(s) of interest in The dataset?

Intrigued by the affluence of the dataset and its wide range of variables, i will explore it to glean some insights and refine my understanding of the lending market in the US. In this journey i will try to cover the following points:

Learn about borrowers:

The Distribution of the loan amounts they usually go for.

Delinquency incidents and their leading factors

Associations between income range, and borrowers’ credit score and defaulting on loans.

Correlations between risk of a loan “estimated loss” and its revenues.

Learn about the investors:

Their count per loan

The yields they garner from lending.

Prosper performance trends “by year and by state”

Note: This data dictionary explains the variables in the data set.