Vehicle Insurance Purchase Response

Predicting Customer Interest in Vehicle Insurance Using Demographic and Policy D

@kaggle.baleshs_vehicle_insurance_purchase_response

Table List

2 tables(5.89 MB)

Test Pruning

@kaggle.baleshs_vehicle_insurance_purchase_response.test_pruning

1.66 MB
127037 rows
11 columns

id Id	gender Gender	age Age	driving_license Driving License	region_code Region Code	previously_insured Previously Insured	vehicle_age Vehicle Age	vehicle_damage Vehicle Damage	annual_premium Annual Premium	policy_sales_channel Policy Sales Channel	vintage Vintage
381110	Male	25	1	11	1	< 1 Year	No	35786	152	53
381111	Male	40	1	28		1-2 Year	Yes	33762	7	111
381112	Male	47	1	28		1-2 Year	Yes	40050	124	199
381113	Male	24	1	27	1	< 1 Year	Yes	37356	152	187
381114	Male	27	1	28	1	< 1 Year	No	59097	152	297
381115	Male	22	1	30	1	< 1 Year	No	40207	152	266
381116	Female	51	1	37	1	1-2 Year	No	40118	26	107
381117	Male	25	1	41	1	< 1 Year	No	23375	152	232
381118	Male	42	1	41		1-2 Year	Yes	2630	26	277
381119	Female	37	1	12	1	1-2 Year	No	27124	124	133


CREATE TABLE test_pruning (
  "id" BIGINT,
  "gender" VARCHAR,
  "age" BIGINT,
  "driving_license" BIGINT,
  "region_code" DOUBLE,
  "previously_insured" BIGINT,
  "vehicle_age" VARCHAR,
  "vehicle_damage" VARCHAR,
  "annual_premium" DOUBLE,
  "policy_sales_channel" DOUBLE,
  "vintage" BIGINT
);