Learning From Imbalanced Insurance Data

Cross-sell Prediction

@kaggle.arashnic_imbalanced_data_practice

Table List

3 tables(6.2 MB)

Aug Test

Aug Train

Sample Submission

Aug Train

@kaggle.arashnic_imbalanced_data_practice.aug_train

4.56 MB
382154 rows
12 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	response Response
167647	Male	22	1	7	1	< 1 Year	No	2630	152	16
17163	Male	42	1	28		1-2 Year	Yes	43327	26	135
32023	Female	66	1	33		1-2 Year	Yes	35841	124	253
87447	Female	22	1	33		< 1 Year	No	27645	152	69
501933	Male	28	1	46	1	< 1 Year	No	29023	152	211
295775	Female	25	1	25	1	< 1 Year	No	27954	152	23
71711	Male	51	1	8		1-2 Year	Yes	2630	26	209
331781	Male	38	1	28	1	1-2 Year	No	2630	26	51
290704	Female	45	1	28		1-2 Year	Yes	55873	124	262
344792	Male	42	1	28		1-2 Year	Yes	27801	122	217	1


CREATE TABLE aug_train (
  "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,
  "response" BIGINT
);