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)

Train Dataset

@kaggle.saurabhbagchi_dish_network_hackathon.train_dataset

5.46 MB
121856 rows
40 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
12142509	6750			1			61190.55	3416.85	Alone	Commercial	Secondary	M	Male	CL	Home	0.028663	13957	1062	6123	383		1	1		Sales	2	2	6	17	Yes	Yes	Self-employed	0.56806615	0.47878667	nan	0.0186	63
12138936	20250	1		1			15282	1826.55	Alone	Service	Graduation	M	Male	CL	Home	0.008575	14162	4129	7833	21		1		1	nan	2	2	3	10	Yes	Yes	Government	0.563360099	0.215068341	nan
12181264	18000			1		1	59527.35	2788.2	Alone	Service	Graduation dropout	W	Male	CL	Family	0.0228	16790	5102	nan	331		1			Realty agents	2	2	4		Yes	Yes	Self-employed		0.552794972	0.329655054	0.0742	277
12188929	15750			1	1		53870.4	2295.45	Alone	Retired	Secondary	M	Male	CL	Home	0.010556	23195	365243	nan	775		1			nan	2	3	2	15	Yes	Yes	XNA		0.135182337	0.631354537		1700	3
12133385	33750	1		1		2	133988.4	3547.35	Alone	Commercial	Secondary	M	Female	CL	Home	0.020713	11366	2977	5516	4043	6	1			Laborers	4	1	3		Yes	Yes	Business Entity Type 3	0.508198522	0.301181977	0.355638717	0.2021	674	1
12191614	11250		1	1	1	1	13752	653.85	Alone	Service	Secondary	W	Female	CL	Home	0.019101	13881	1184	3910	3910		1			Laborers	2	2	2	10	Yes	Yes	Other		0.697927562	0.420610964	0.0639	739
12128086	15750	1	1		1		128835	3779.55	Alone	Retired	Secondary	S	Male	CL	Home	0.016612	21323	365243	113	4855	10	1			nan	1	2	3	14	Yes	Yes	XNA	0.729913215	0.602545334	0.511891802	0.2041		3
12215264	13500			1	1		60415.2	3097.8	Alone	Retired	Secondary	M	Male	CL	Home	0.009175	22493	365243	12617	5280		1		1	nan	2	2	4	15	Yes	Yes	XNA	0.711468199	0.657507898	0.549596502		1687	4
12159147	13500	1	1		1	1	45000	1200.15	Relative	Commercial	Graduation	M	Female	CL	Home	0.006008	nan	7889	5455	2665	14	1		1	Sales	3	2	4	13	Yes	Yes	Self-employed	0.475726518	0.637593554	0.553164699	0.167	1611
12130547	12150				1		16320.15	1294.65	Alone	Retired	Secondary	W	Male	CL	Home	0.016612	20507	365243	2834	4053		1			nan	1	2		9	Yes	Yes	XNA	0.682285092	0.063343128	0.080649544		533	5


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
);