A Gentle Introduction to Data Science for Credit Risk Modeling — Part 1The aftermath of the 2008 subprime mortgage crisis has been terrible for many, but it created growth opportunities for new players in the retail credit field.Following the credit scarcity that took place briefly during those times, some new internet companies have thrived, with a new business model that would become known as Peer-to-Peer Lending — or simply P2P Lending.From its inception in the end of the last decade until now, amounts lent through P2P Lending marketplaces have grown impressively. According to PwC, U.S. peer-to-peer lending platforms' origination volumes have grown an average of 84% per quarter since 2007.Using Data Science, Exploratory Data Analysis, Machine Learning and public data from Lending Club, a popular P2P Lending marketplace, we will investigate this scenario further. Throughout a series of posts, we will cover the following dimensions:Loan Variables such as loan amount, term, interest rate, delinquencyBorrower Profile Variables such as employment status, relationship statusMiscellaneous Variables and other factors such as macroeconomic, geographicQuestions to be AnsweredOur objective will be to analyze this data in order to answer to the questions below:How does the loan data distribution look like? Using Data Science, we will paint a picture detailing the most important aspects related to the loans and perform EDA (Exploratory Data Analysis).


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