A project inspired by an Udemy course teaching Tensorflow. Here, i received data based from kaggle about whether or not the borrower defaults. I had to: • Explore the data by various visualization methods. • Clean, fill and order the data and finally • Use dummy variable • Design a network predicting the profitability of a loaner
---- THIS WAS GREAT FUN!! ------
This data is a subset of the LendingClub DataSet obtained from Kaggle. However, this dataset is a special version modified forextre feature eninnering to-do work as part of the exerciseto download the data, use my dropbox link : https://www.dropbox.com/s/c2yzukpnr91c21f/lending_club_loan_two.csv?dl=0 or contact me
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 thatcan 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.
The "loan_status" column contains our label.