Multi-Class Classification Problem

Problem Statement
You are working as a data scientist in a global finance company. Over the years, the company has collected basic bank details and gathered a lot of credit-related information. The management wants to build an intelligent system to segregate the people into credit score brackets to reduce the manual efforts.

Task
Given a person’s credit-related information, build a machine learning model that can classify the credit score.

• Age: Represents the age 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
• 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 (in USD)
• Credit_Utilization_Ratio: Represents the utilization ratio of credit card
• Credit_History_Age: Represents the age of credit history of the person
• Payment_of_Min_Amount: Represents whether only the minimum amount was paid by the person
• Total_EMI_per_month: Represents the monthly EMI payments (in USD)
• Amount_invested_monthly: Represents the monthly amount invested by the customer (in USD)
• Monthly_Balance: Represents the monthly balance amount of the customer (in USD)
• Credit_Score: Represents the bracket of credit score (Poor, Standard, Good)