Multi-Class Classification Problem

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

@kaggle.sudhanshu2198_processed_data_credit_score

Table List

1 table(3.69 MB)

Score

@kaggle.sudhanshu2198_processed_data_credit_score.score

3.69 MB
99960 rows
21 columns

delay_from_due_date Delay From Due Date	num_of_delayed_payment Num Of Delayed Payment	num_credit_inquiries Num Credit Inquiries	credit_utilization_ratio Credit Utilization Ratio	credit_history_age Credit History Age	payment_of_min_amount Payment Of Min Amount	amount_invested_monthly Amount Invested Monthly	monthly_balance Monthly Balance	credit_score Credit Score	credit_mix Credit Mix	payment_behaviour Payment Behaviour	age Age	annual_income Annual Income	num_bank_accounts Num Bank Accounts	num_credit_card Num Credit Card	interest_rate Interest Rate	num_of_loan Num Of Loan	monthly_inhand_salary Monthly Inhand Salary	changed_credit_limit Changed Credit Limit	outstanding_debt Outstanding Debt	total_emi_per_month Total EMI Per Month
3	7	4	26.822619623699016	265	No	80.41529543900253	312.49408867943663	Good	Good	High_spent_Medium_value_payments	23	19114.12	3	4	3	4	1824.8433333333328	11.27	809.98	49.57494921489417
3	7	4	31.94496005538421	265	No	118.28022162236736	284.62916249607184	Good	Good	High_spent_Medium_value_payments	23	19114.12	3	4	3	4	1824.8433333333328	11.27	809.98	49.57494921489417
3	7	4	28.60935202206993	267	No	81.699521264648	331.2098628537912	Good	Good	High_spent_Medium_value_payments	23	19114.12	3	4	3	4	1824.8433333333328	11.27	809.98	49.57494921489417
5	4	4	31.37786186958236	268	No	199.4580743910713	223.45130972736783	Good	Good	High_spent_Medium_value_payments	23	19114.12	3	4	3	4	1824.8433333333328	11.27	809.98	49.57494921489417
6	4	4	24.797346908844982	269	No	41.420153086217326	341.48923103222177	Good	Good	High_spent_Medium_value_payments	23	19114.12	3	4	3	4	1824.8433333333328	11.27	809.98	49.57494921489417
8	4	4	27.26225871052017	270	No	62.430172331195294	340.4792117872438	Good	Good	High_spent_Medium_value_payments	23	19114.12	3	4	3	4	1824.8433333333328	11.27	809.98	49.57494921489417
3	8	4	22.53759303178384	271	No	178.3440674122349	244.5653167062043	Good	Good	High_spent_Medium_value_payments	23	19114.12	3	4	3	4	1824.8433333333328	11.27	809.98	49.57494921489417
3	6	4	23.93379480196552	271	No	24.785216509052056	358.12416760938714	Standard	Good	High_spent_Medium_value_payments	23	19114.12	3	4	3	4	1824.8433333333328	11.27	809.98	49.57494921489417
3	4	2	24.46403063758457	319	No	104.291825168246	470.69062692529184	Standard	Good	High_spent_Large_value_payments	28	34847.84	2	4	6	1	3037.986666666666	5.42	605.03	18.816214573128885
7	1	2	38.550848433956325	320	No	40.39123782853101	484.5912142650067	Good	Good	High_spent_Large_value_payments	28	34847.84	2	4	6	1	3037.986666666666	5.42	605.03	18.816214573128885


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
);