Credit Car Prediction Dataset

Unlocking Financial Access: Predicting Credit Card Eligibility

@kaggle.tanayatipre_car_price_prediction_dataset

Table List

2 tables(619.63 KB)

Test Data

@kaggle.tanayatipre_car_price_prediction_dataset.test_data

142.79 KB
7292 rows
20 columns

id ID	gender Gender	has_a_car Has A Car	has_a_property Has A Property	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
5091261	F	N	Y	202500	State servant	Secondary / secondary special	Separated	House / apartment	-16834	-1692	1				Medicine staff	1	-6
5096963	M	Y	N	675000	Commercial associate	Higher education	Married	House / apartment	-18126	-948	1		1		Managers	2	-16
5087880	F	N	N	234000	State servant	Higher education	Civil marriage	House / apartment	-21967	-5215	1			1	Core staff	2	-52
5021949	F	Y	Y	445500	Commercial associate	Higher education	Married	House / apartment	-12477	-456	1				Managers	2	-54
5105705	F	Y	N	225000	Working	Secondary / secondary special	Married	Municipal apartment	-12155	-667	1				Laborers	2	-48
5029195	F	N	Y	94500	Working	Secondary / secondary special	Married	House / apartment	-18131	-617	1				Laborers	2	-18
5029213	F	N	Y	180000	Pensioner	Secondary / secondary special	Married	House / apartment	-22115	365243	1		1		nan	2	-23
5051050	M	Y	N	67500	Working	Secondary / secondary special	Married	House / apartment	-22407	-2174	1				Laborers	2	-9
5061569	F	N	N	189000	Working	Secondary / secondary special	Married	Municipal apartment	-18452	-137	1				Laborers	2	-37
5091430	F	N	Y	225000	Working	Higher education	Married	House / apartment	-16202	-2939	1	1	1		Managers	2	-43


CREATE TABLE test_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
);