Credit Car Prediction Dataset

Unlocking Financial Access: Predicting Credit Card Eligibility

@kaggle.tanayatipre_car_price_prediction_dataset

Table List

2 tables(619.63 KB)

Train Data

@kaggle.tanayatipre_car_price_prediction_dataset.train_data

476.84 KB
29165 rows
20 columns

id ID	gender Gender	has_a_car Has A Car	has_a_property Has A Property	children_count Children Count	income Income	employment_status Employment Status	education_level Education Level	marital_status Marital Status	dwelling Dwelling	age Age	employment_length Employment Length	has_a_mobile_phone Has A Mobile Phone	has_a_work_phone Has A Work Phone	has_a_phone Has A Phone	has_an_email Has An Email	job_title Job Title	family_member_count Family Member Count	account_age Account Age
5037048	M	Y	Y		135000	Working	Secondary / secondary special	Married	With parents	-16271	-3111	1				Core staff	2	-17
5044630	F	Y	N	1	135000	Commercial associate	Higher education	Single / not married	House / apartment	-10130	-1651	1				Accountants	2	-1
5079079	F	N	Y	2	180000	Commercial associate	Secondary / secondary special	Married	House / apartment	-12821	-5657	1				Laborers	4	-38
5112872	F	Y	Y		360000	Commercial associate	Higher education	Single / not married	House / apartment	-20929	-2046	1			1	Managers	1	-11
5105858	F	N	N		270000	Working	Secondary / secondary special	Separated	House / apartment	-16207	-515	1		1		nan	1	-41
5100411	F	Y	Y		135000	Working	Secondary / secondary special	Married	House / apartment	-13251	-3839	1	1			Accountants	2	-1
5022817	M	Y	Y		202500	Working	Secondary / secondary special	Married	House / apartment	-17262	-1617	1				Core staff	2	-16
5009811	F	N	N	1	202500	Working	Secondary / secondary special	Married	House / apartment	-11813	-3266	1	1	1		Sales staff	3	-21
5113922	F	N	N		90000	Pensioner	Secondary / secondary special	Single / not married	Municipal apartment	-23478	365243	1				nan	1	-50
5021541	F	Y	N	1	306000	Working	Higher education	Married	House / apartment	-9310	-1678	1				nan	3	-13


CREATE TABLE train_data (
  "id" BIGINT,
  "gender" VARCHAR,
  "has_a_car" VARCHAR,
  "has_a_property" VARCHAR,
  "children_count" BIGINT,
  "income" DOUBLE,
  "employment_status" VARCHAR,
  "education_level" VARCHAR,
  "marital_status" VARCHAR,
  "dwelling" VARCHAR,
  "age" BIGINT,
  "employment_length" BIGINT,
  "has_a_mobile_phone" BIGINT,
  "has_a_work_phone" BIGINT,
  "has_a_phone" BIGINT,
  "has_an_email" BIGINT,
  "job_title" VARCHAR,
  "family_member_count" DOUBLE,
  "account_age" DOUBLE,
  "is_high_risk" BIGINT
);