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lending_club_kaggle's Introduction

Lending_club_kaggle

In this project, I predicted which peer-to-peer loans are charged off or fully paid with 72% accuracy.

Project scope:

Lending Club says, "Our mission is to transform the banking system to make credit more affordable and investing more rewarding." You can view their loan statistics and visualizations.

According to Wikipedia, Lending Club is the world's largest peer-to-peer lending platform.

Lending Club enables borrowers to create unsecured personal loans between $1,000 and $40,000. The standard loan period is three years. Investors can search and browse the loan listings on Lending Club website and select loans that they want to invest in based on the information supplied about the borrower, amount of loan, loan grade, and loan purpose. Investors make money from interest. Lending Club makes money by charging borrowers an origination fee and investors a service fee.

The data comes from all Lending Club peer-to-peer loans with a loan status of "Charged Off" or "Fully Paid", issued from 2007 through 2018.

The set of variables included here are the intersection of what's available both when investors download historical data and when investors browse loans for manual investing.

The data was randomly shuffled and split, with stratified sampling, so labels have the same class balance in both the train and test sets. For this challenge, you should not download data directly from Lending Club.

Labels 1: Charged Off 0: Fully Paid Data dictionary https://resources.lendingclub.com/LCDataDictionary.xlsx

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