Credit Car Prediction Dataset
Unlocking Financial Access: Predicting Credit Card Eligibility
@kaggle.tanayatipre_car_price_prediction_dataset
Train Data
@kaggle.tanayatipre_car_price_prediction_dataset.train_data
- 476.84 KB
- 29165 rows
- 20 columns
idID | genderGender | has_a_carHas A Car | has_a_propertyHas A Property | children_countChildren Count | incomeIncome | employment_statusEmployment Status | education_levelEducation Level | marital_statusMarital Status | dwellingDwelling | ageAge | employment_lengthEmployment Length | has_a_mobile_phoneHas A Mobile Phone | has_a_work_phoneHas A Work Phone | has_a_phoneHas A Phone | has_an_emailHas An Email | job_titleJob Title | family_member_countFamily Member Count | account_ageAccount Age | is_high_riskIs High Risk |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 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
);