Dataset: NBFI Vehicle Loan Repayment Dataset

About this Dataset

NBFI Vehicle Loan Repayment Dataset

A non-banking financial institution (NBFI) or non-bank financial company (NBFC) is a type of financial institution that is not authorized to operate as a bank or is not under the supervision of a banking regulatory agency at the national or international level. NBFCs provide financial services similar to those of banks, such as investment, risk pooling, contractual savings, and market brokering.

An NBFC is currently facing profitability issues due to an increase in defaults in the vehicle loan category. The company aims to assess the loan repayment ability of clients and determine the relative importance of each factor contributing to a borrower's ability to repay the loan.

The objective is to predict whether a client is likely to default on their vehicle loan payment or not. The task involves building a model using the Train_Dataset and testing the model on the Test_Dataset. The predictions for each ID in the Test_Dataset are to be submitted to the Hackathon platform.

Tables

Data Dictionary

@kaggle.meastanmay_nbfi_vehicle_loan_repayment_dataset.data_dictionary

4.3 KB
40 rows
2 columns


CREATE TABLE data_dictionary (
  "variable" VARCHAR,
  "description" VARCHAR
);

Test Dataset

@kaggle.meastanmay_nbfi_vehicle_loan_repayment_dataset.test_dataset

3.65 MB
80900 rows
39 columns


CREATE TABLE test_dataset (
  "id" BIGINT,
  "client_income" VARCHAR,
  "car_owned" DOUBLE,
  "bike_owned" DOUBLE,
  "active_loan" DOUBLE,
  "house_own" DOUBLE,
  "child_count" DOUBLE,
  "credit_amount" VARCHAR,
  "loan_annuity" VARCHAR,
  "accompany_client" VARCHAR,
  "client_income_type" VARCHAR,
  "client_education" VARCHAR,
  "client_marital_status" VARCHAR,
  "client_gender" VARCHAR,
  "loan_contract_type" VARCHAR,
  "client_housing_type" VARCHAR,
  "population_region_relative" VARCHAR,
  "age_days" VARCHAR,
  "employed_days" VARCHAR,
  "registration_days" VARCHAR,
  "id_days" VARCHAR,
  "own_house_age" DOUBLE,
  "mobile_tag" BIGINT,
  "homephone_tag" BIGINT,
  "workphone_working" BIGINT,
  "client_occupation" VARCHAR,
  "client_family_members" DOUBLE,
  "cleint_city_rating" DOUBLE,
  "application_process_day" DOUBLE,
  "application_process_hour" DOUBLE,
  "client_permanent_match_tag" VARCHAR,
  "client_contact_work_tag" VARCHAR,
  "type_organization" VARCHAR,
  "score_source_1" DOUBLE,
  "score_source_2" VARCHAR,
  "score_source_3" VARCHAR,
  "social_circle_default" DOUBLE,
  "phone_change" DOUBLE,
  "credit_bureau" DOUBLE
);

Train Dataset

@kaggle.meastanmay_nbfi_vehicle_loan_repayment_dataset.train_dataset

5.46 MB
121856 rows
40 columns


CREATE TABLE train_dataset (
  "id" BIGINT,
  "client_income" VARCHAR,
  "car_owned" DOUBLE,
  "bike_owned" DOUBLE,
  "active_loan" DOUBLE,
  "house_own" DOUBLE,
  "child_count" DOUBLE,
  "credit_amount" VARCHAR,
  "loan_annuity" VARCHAR,
  "accompany_client" VARCHAR,
  "client_income_type" VARCHAR,
  "client_education" VARCHAR,
  "client_marital_status" VARCHAR,
  "client_gender" VARCHAR,
  "loan_contract_type" VARCHAR,
  "client_housing_type" VARCHAR,
  "population_region_relative" VARCHAR,
  "age_days" VARCHAR,
  "employed_days" VARCHAR,
  "registration_days" VARCHAR,
  "id_days" VARCHAR,
  "own_house_age" DOUBLE,
  "mobile_tag" BIGINT,
  "homephone_tag" BIGINT,
  "workphone_working" BIGINT,
  "client_occupation" VARCHAR,
  "client_family_members" DOUBLE,
  "cleint_city_rating" DOUBLE,
  "application_process_day" DOUBLE,
  "application_process_hour" DOUBLE,
  "client_permanent_match_tag" VARCHAR,
  "client_contact_work_tag" VARCHAR,
  "type_organization" VARCHAR,
  "score_source_1" DOUBLE,
  "score_source_2" DOUBLE,
  "score_source_3" VARCHAR,
  "social_circle_default" DOUBLE,
  "phone_change" DOUBLE,
  "credit_bureau" DOUBLE,
  "default" BIGINT
);