Loan default prediction - cleaning the data and intro EDA
The following dataset contains information on loans. Can you do the following to prepare the data set for analysis?
Create a new column called "loan_status_type" which will categorize "loan_status" into the following:
Current - loans currently outstanding
Closed - loans that are no longer open
Create a new column called "loan_status_standing" which will categorize "loan_status" into the following:
Good - customers who have (so far) successfully met the condition of their loan (e.g. no missed payments, no late fees accumulated)
Bad - customers who have missed payments / defaulted
With these 2 new columns, can you plot the month and year the loan was issued and the sum of the loan amounts by loan_status_type and loan_status_contract?
The data provided is a subset of a larger dataset. You can find more information about the larger dataset here.
|The first 3 numbers of the zip code provided by the borrower in the loan application.
|The state provided by the borrower in the loan application
|The annual income provided by the borrower during registration.
|post charge off collection fee
|Number of collections in 12 months excluding medical collections
|The number of 30+ days past-due incidences of delinquency in the borrower’s credit file for the past 2 years
|Loan description provided by the borrower
|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.
|The month the borrower’s earliest reported credit line was opened
|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.
|The job title supplied by the Borrower when applying for the loan.
|The total amount committed to that loan at that point in time.
|The total amount committed by investors for that loan at that point in time.
|LC assigned loan grade
|The home ownership status provided by the borrower during registration. Our values are: RENT, OWN, MORTGAGE, OTHER.
|A unique LC assigned ID for the loan listing.
|The initial listing status of the loan. Possible values are – W, F
|The number of inquiries by creditors during the past 6 months.
|The monthly payment owed by the borrower if the loan originates.
|Interest Rate on the loan
|The month which the loan was funded
|The most recent month LC pulled credit for this loan
|Last total payment amount received
|Last month payment was received
|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.
|Current status of the loan
|A unique LC assigned Id for the borrower member.
|The number of months since the borrower’s last delinquency.
|Months since most recent 90-day or worse rating
|The number of months since the last public record.
|Next scheduled payment date
|The number of open credit lines in the borrower’s credit file.
|Remaining outstanding principal for total amount funded
|Remaining outstanding principal for portion of total amount funded by investors
|Publicly available policy_code=1, new products not publicly available policy_code=2
|Number of derogatory public records
|A category provided by the borrower for the loan request.
|Indicates if a payment plan has been put in place for the loan
|post charge off gross recovery
|Total credit revolving balance
|Revolving line utilization rate, or the amount of credit the borrower is using relative to all available revolving credit.
|LC assigned loan subgrade
|The number of payments on the loan. Values are in months and can be either 36 or 60.
|The loan title provided by the borrower
|The total number of credit lines currently in the borrower’s credit file
|Payments received to date for total amount funded
|Payments received to date for portion of total amount funded by investors
|Interest received to date
|Late fees received to date
|Principal received to date
|URL for the LC page with listing data.
Subscribe to premium account to see the solution.Get premium now