In this notebook we analyze a dataset call All Lending Club loan data, which you can find in Kaggle.
One of the objectives of this notebook is to show step-by-step how to visualize the dataset and assess whether or not a new customer is likely to pay back the loan.
LendingClub is a US peer-to-peer lending company, headquartered in San Francisco, California. It was the first peer-to-peer lender to register its offerings as securities with the Securities and Exchange Commission, 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 that can 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.
To see the Kernel directly from Kaggle click here.
Feature Columns
There are many LendingClub data sets on Kaggle. Here is the information on this particular data set:
| LoanStatNew | Description | |
|---|---|---|
| 0 | loan_amnt | The listed amount of the loan applied for by the borrower. If at some point in time, the credit department reduces the loan amount, then it will be reflected in this value. |
| 1 | term | The number of payments on the loan. Values are in months and can be either 36 or 60. |
| 2 | int_rate | Interest Rate on the loan |
| 3 | installment | The monthly payment owed by the borrower if the loan originates. |
| 4 | grade | LC assigned loan grade |
| 5 | sub_grade | LC assigned loan subgrade |
| 6 | emp_title | The job title supplied by the Borrower when applying for the loan.* |
| 7 | emp_length | Employment length in years. Possible values are between 0 and 10 where 0 means less than one year and 10 means ten or more years. |
| 8 | home_ownership | The home ownership status provided by the borrower during registration or obtained from the credit report. Our values are: RENT, OWN, MORTGAGE, OTHER |
| 9 | annual_inc | The self-reported annual income provided by the borrower during registration. |
| 10 | verification_status | Indicates if income was verified by LC, not verified, or if the income source was verified |
| 11 | issue_d | The month which the loan was funded |
| 12 | loan_status | Current status of the loan |
| 13 | purpose | A category provided by the borrower for the loan request. |
| 14 | title | The loan title provided by the borrower |
| 15 | zip_code | The first 3 numbers of the zip code provided by the borrower in the loan application. |
| 16 | addr_state | The state provided by the borrower in the loan application |
| 17 | dti | A ratio calculated using the borrower’s total monthly debt payments on the total debt obligations, excluding mortgage and the requested LC loan, divided by the borrower’s self-reported monthly income. |
| 18 | earliest_cr_line | The month the borrower's earliest reported credit line was opened |
| 19 | open_acc | The number of open credit lines in the borrower's credit file. |
| 20 | pub_rec | Number of derogatory public records |
| 21 | revol_bal | Total credit revolving balance |
| 22 | revol_util | Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit. |
| 23 | total_acc | The total number of credit lines currently in the borrower's credit file |
| 24 | initial_list_status | The initial listing status of the loan. Possible values are – W, F |
| 25 | application_type | Indicates whether the loan is an individual application or a joint application with two co-borrowers |
| 26 | mort_acc | Number of mortgage accounts. |
| 27 | pub_rec_bankruptcies | Number of public record bankruptcies |
This Notebook has been released under the Apache 2.0 open source license.