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Credit_Score_Prediction

Credit scoring is the process of assessing the creditworthiness of an individual or business. This process is used by lenders to determine whether or not to approve a loan application and what interest rate to charge. Creditworthiness is typically assessed based on a number of factors, including the applicant's credit history, income, employment history, and debt-to-income ratio.

Objective

The objective of a credit score prediction project is to develop a machine learning model that can accurately predict credit scores. This model can then be used by lenders to make more informed decisions about loan applications. The objective of a credit score prediction project is to develop a machine learning/Deep Learning model that can accurately predict credit scores. This model can then be used by lenders to make more informed decisions about loan applications.

Data Description

  • ID: Represents a unique identification of an entry
  • Customer_ID: Represents a unique identification of a person
  • Month: Represents the month of the year
  • Name: Represents the name of a person
  • Age: Represents the age of the person
  • SSN: Represents the social security number of a person
  • Occupation: Represents the occupation of the person
  • Annual_Income: Represents the annual income of the person
  • Monthly_Inhand_Salary: Represents the monthly base salary of a person
  • Num_Bank_Accounts: Represents the number of bank accounts a person holds
  • Num_Credit_Card: Represents the number of other credit cards held by a person
  • Interest_Rate: Represents the interest rate on credit card
  • Num_of_Loan: Represents the number of loans taken from the bank
  • Type_of_Loan: Represents the types of loan taken by a person
  • Delay_from_due_date: Represents the average number of days delayed from the payment date
  • Num_of_Delayed_Payment: Represents the average number of payments delayed by a person
  • Changed_Credit_Limit: Represents the percentage change in credit card limit
  • Num_Credit_Inquiries: Represents the number of credit card inquiries
  • Credit_Mix: Represents the classification of the mix of credits
  • Outstanding_Debt: Represents the remaining debt to be paid