Baselight

Multi-Class Classification Problem

Given a person’s credit-related information, build a machine learning model that

@kaggle.sudhanshu2198_processed_data_credit_score

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About this Dataset

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)

Tables

Score

@kaggle.sudhanshu2198_processed_data_credit_score.score
  • 3.69 MB
  • 99960 rows
  • 21 columns
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CREATE TABLE score (
  "delay_from_due_date" DOUBLE,
  "num_of_delayed_payment" DOUBLE,
  "num_credit_inquiries" DOUBLE,
  "credit_utilization_ratio" DOUBLE,
  "credit_history_age" DOUBLE,
  "payment_of_min_amount" VARCHAR,
  "amount_invested_monthly" DOUBLE,
  "monthly_balance" DOUBLE,
  "credit_score" VARCHAR,
  "credit_mix" VARCHAR,
  "payment_behaviour" VARCHAR,
  "age" DOUBLE,
  "annual_income" DOUBLE,
  "num_bank_accounts" DOUBLE,
  "num_credit_card" DOUBLE,
  "interest_rate" DOUBLE,
  "num_of_loan" DOUBLE,
  "monthly_inhand_salary" DOUBLE,
  "changed_credit_limit" DOUBLE,
  "outstanding_debt" DOUBLE,
  "total_emi_per_month" DOUBLE
);

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