Credit Score Classification: Case Study

Download the dataset below to solve this Data Science case study on Credit Score Classification. Dataset Source: Kaggle)

Credit Score Classification: Case Study

The credit score of a person determines the creditworthiness of the person. It helps financial companies determine if you can repay the loan or credit you are applying for.

Here is a dataset based on the credit score classification submitted by Rohan Paris on Kaggle. Below are all the features in the dataset:

  1. ID: Unique ID of the record
  2. Customer_ID: Unique ID of the customer
  3. Month: Month of the year
  4. Name: The name of the person
  5. Age: The age of the person
  6. SSN: Social Security Number of the person
  7. Occupation: The occupation of the person
  8. Annual_Income: The Annual Income of the person
  9. Monthly_Inhand_Salary: Monthly in-hand salary of the person
  10. Num_Bank_Accounts: The number of bank accounts of the person
  11. Num_Credit_Card: Number of credit cards the person is having
  12. Interest_Rate: The interest rate on the credit card of the person
  13. Num_of_Loan: The number of loans taken by the person from the bank
  14. Type_of_Loan: The types of loans taken by the person from the bank
  15. Delay_from_due_date: The average number of days delayed by the person from the date of payment
  16. Num_of_Delayed_Payment: Number of payments delayed by the person
  17. Changed_Credit_Card: The percentage change in the credit card limit of the person
  18. Num_Credit_Inquiries: The number of credit card inquiries by the person
  19. Credit_Mix: Classification of Credit Mix of the customer
  20. Outstanding_Debt: The outstanding balance of the person
  21. Credit_Utilization_Ratio: The credit utilization ratio of the credit card of the customer
  22. Credit_History_Age: The age of the credit history of the person
  23. Payment_of_Min_Amount: Yes if the person paid the minimum amount to be paid only, otherwise no.
  24. Total_EMI_per_month: The total EMI per month of the person
  25. Amount_invested_monthly: The monthly amount invested by the person
  26. Payment_Behaviour: The payment behaviour of the person
  27. Monthly_Balance: The monthly balance left in the account of the person
  28. Credit_Score: The credit score of the person

The Credit_Score column is the target variable in this problem. You are required to find relationships based on how banks classify credit scores and train a model to classify the credit score of a person.

References to Solve this Data Science Case Study