Automobile Loan Default Dataset

Predict whether a client will default on the vehicle loan payment or not

@kaggle.saurabhbagchi_dish_network_hackathon

Table List

4 tables(9.59 MB)

Test Dataset

@kaggle.saurabhbagchi_dish_network_hackathon.test_dataset

3.65 MB
80900 rows
39 columns

id ID	client_income Client Income	car_owned Car Owned	bike_owned Bike Owned	active_loan Active Loan	house_own House Own	child_count Child Count	credit_amount Credit Amount	loan_annuity Loan Annuity	accompany_client Accompany Client	client_income_type Client Income Type	client_education Client Education	client_marital_status Client Marital Status	client_gender Client Gender	loan_contract_type Loan Contract Type	client_housing_type Client Housing Type	population_region_relative Population Region Relative	age_days Age Days	employed_days Employed Days	registration_days Registration Days	id_days ID Days	own_house_age Own House Age	mobile_tag Mobile Tag	homephone_tag Homephone Tag	workphone_working Workphone Working	client_occupation Client Occupation	client_family_members Client Family Members	cleint_city_rating Cleint City Rating	application_process_day Application Process Day	application_process_hour Application Process Hour	client_permanent_match_tag Client Permanent Match Tag	client_contact_work_tag Client Contact Work Tag	type_organization Type Organization	score_source_1 Score Source 1	score_source_2 Score Source 2	score_source_3 Score Source 3	social_circle_default Social Circle Default	phone_change Phone Change	credit_bureau Credit Bureau
12202227	11250		1	1	1		112500	4474.8	Relative	Service	nan	M	Male	CL	Home	0.019101	20063	2523	2318	3318		1			nan		2	4	16	Yes	Yes	Self-employed		0.757150809	0.629674251	0.0515	716	2
12279381	13500	1	1	1	1	3	49752	3252.15	Alone	Service	Secondary	M	Female	CL	Home	0.0105	13200	551	6972	3865	6	1	1		Laborers	5	3	3	18	No	Yes	Self-employed		0.342269008	nan		181
12222714	38250		1		1		67500	3375	Alone	Commercial	Graduation	M	Female	RL	Home	0.032561	15943	1319	981	2296		1		1	Managers	2	1	2	19	Yes	Yes	Business Entity Type 3		0.683664701	nan	0.1503	281
12265215	20250			1			50849.55	3814.65	Alone	Govt Job	Secondary	S	Male	CL	nan	0.01452	15634	1510	4975	6121		1	1	1	nan	1	2	6	20	No	No	Medicine	0.351580677	0.229950297	0.67224289		1442	4
12203970	13500	1	1		1		43603.2	2515.95	Alone	Service	Secondary	M	Female	CL	Home	0.008625	15811	1075	5478	3618	7	1			Managers	2	2	1	13	Yes	Yes	Self-employed	0.73111046	0.56697104	0.73098737		927	1
12219891	13500			1	1		110499.75	3664.8	Alone	Retired	Secondary	S	Male	CL	Home	0.019101	22164	365243	14202	4626		1			nan	1	2	4	12	Yes	Yes	XNA		nan	0.746300213	0.1052	1235	1
12279775	11250	1	1		1		53366.85	2273.85	Alone	Govt Job	Secondary	M	Female	CL	Home	0.031329	17278	2598	3260	784	4	1			Core	2	2	2	8	Yes	No	Security Ministries	0.559248418	0.456117503	0.37571101		2154	4
12250293	13500				1		94500	2776.05	Alone	Service	Secondary	W	Male	CL	Home	nan	17487	197	9393	1020		1			Laborers	1	2	1	18	Yes	Yes	Business Entity Type 3		0.761308021	0.517296581	0.0119	495	1
12231470	18000	1			1		53747.1	5801.4	Alone	Commercial	Secondary	M	Female	CL	Home	0.072508	9788	2309	4484	2470	3	1			Laborers	2	1	3	13	Yes	Yes	Industry: type 11	0.431516493	0.687017551	0.712155155	0.0845	1776	5
12270152	22500						126773.55	3719.7	Alone	Service	Secondary	M	Female	CL	nan	0.007305	16793	616	1012	347		1		1	Laborers	2	3	5	13	Yes	Yes	Other	0.585468866	0.382493015	0.605836265	0.0928	1752


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
);