Dataset: Dataset For Credit Score Classification

About this Dataset

Dataset For Credit Score Classification

Problem Statement
You are working as a data scientist in a global finance company. Over a year, 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. Given a person's credit related information build a machine learning model that can classify the credit score.

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

The dataset contains 1 lakh records with 28 features. There is an inherent chronological order within the dataset as for each customer 8 consecutive month period data is present across 8 records.

Tables

Credit Score

@kaggle.ayushsharma0812_dataset_for_credit_score_classification.credit_score

7.07 MB
100000 rows
28 columns


CREATE TABLE credit_score (
  "id" VARCHAR,
  "customer_id" VARCHAR,
  "month" VARCHAR,
  "name" VARCHAR,
  "age" VARCHAR,
  "ssn" VARCHAR,
  "occupation" VARCHAR,
  "annual_income" VARCHAR,
  "monthly_inhand_salary" DOUBLE,
  "num_bank_accounts" BIGINT,
  "num_credit_card" BIGINT,
  "interest_rate" BIGINT,
  "num_of_loan" VARCHAR,
  "type_of_loan" VARCHAR,
  "delay_from_due_date" BIGINT,
  "num_of_delayed_payment" VARCHAR,
  "changed_credit_limit" VARCHAR,
  "num_credit_inquiries" DOUBLE,
  "credit_mix" VARCHAR,
  "outstanding_debt" VARCHAR,
  "credit_utilization_ratio" DOUBLE,
  "credit_history_age" VARCHAR,
  "payment_of_min_amount" VARCHAR,
  "total_emi_per_month" DOUBLE,
  "amount_invested_monthly" VARCHAR,
  "payment_behaviour" VARCHAR,
  "monthly_balance" VARCHAR,
  "credit_score" VARCHAR
);

Credit Score Cleaned Data

@kaggle.ayushsharma0812_dataset_for_credit_score_classification.credit_score_cleaned_data

5.3 MB
100000 rows
32 columns


CREATE TABLE credit_score_cleaned_data (
  "customer_id" VARCHAR,
  "age" BIGINT,
  "occupation" VARCHAR,
  "annual_income" DOUBLE,
  "monthly_inhand_salary" DOUBLE,
  "num_bank_accounts" BIGINT,
  "num_credit_card" BIGINT,
  "interest_rate" DOUBLE,
  "num_of_loan" BIGINT,
  "delay_from_due_date" BIGINT,
  "num_of_delayed_payment" BIGINT,
  "changed_credit_limit" DOUBLE,
  "num_credit_inquiries" BIGINT,
  "credit_mix" VARCHAR,
  "outstanding_debt" DOUBLE,
  "credit_utilization_ratio" DOUBLE,
  "credit_history_age" BIGINT,
  "payment_of_min_amount" VARCHAR,
  "total_emi_per_month" DOUBLE,
  "amount_invested_monthly" DOUBLE,
  "payment_behaviour" VARCHAR,
  "monthly_balance" DOUBLE,
  "last_loan_9" VARCHAR,
  "last_loan_8" VARCHAR,
  "last_loan_7" VARCHAR,
  "last_loan_6" VARCHAR,
  "last_loan_5" VARCHAR,
  "last_loan_4" VARCHAR,
  "last_loan_3" VARCHAR,
  "last_loan_2" VARCHAR,
  "last_loan_1" VARCHAR,
  "credit_score" VARCHAR
);