Bank Churn (train)

Training Insights: Exploring Patterns and Predictors in the Bank Churn Training.

@kaggle.harshitstark_bank_churn_train

Table List

1 table(3.15 MB)

Train

@kaggle.harshitstark_bank_churn_train.train

3.15 MB
165034 rows
14 columns

id Id	customerid CustomerId	surname Surname	creditscore CreditScore	geography Geography	gender Gender	age Age	tenure Tenure	balance Balance	numofproducts NumOfProducts	hascrcard HasCrCard	isactivemember IsActiveMember	estimatedsalary EstimatedSalary	exited Exited
	15674932	Okwudilichukwu	668	France	Male	33	3		2	1		181449.97
1	15749177	Okwudiliolisa	627	France	Male	33	1		2	1	1	49503.5
2	15694510	Hsueh	678	France	Male	40	10		2	1		184866.69
3	15741417	Kao	581	France	Male	34	2	148882.54	1	1	1	84560.88
4	15766172	Chiemenam	716	Spain	Male	33	5		2	1	1	15068.83
5	15771669	Genovese	588	Germany	Male	36	4	131778.58	1	1		136024.31	1
6	15692819	Ch'ang	593	France	Female	30	8	144772.69	1	1		29792.11
7	15669611	Chukwuebuka	678	Spain	Male	37	1	138476.41	1	1		106851.6
8	15691707	Manna	676	France	Male	43	4		2	1		142917.13
9	15591721	Cattaneo	583	Germany	Male	40	4	81274.33	1	1	1	170843.07


CREATE TABLE train (
  "id" BIGINT,
  "customerid" BIGINT,
  "surname" VARCHAR,
  "creditscore" BIGINT,
  "geography" VARCHAR,
  "gender" VARCHAR,
  "age" DOUBLE,
  "tenure" BIGINT,
  "balance" DOUBLE,
  "numofproducts" BIGINT,
  "hascrcard" DOUBLE,
  "isactivemember" DOUBLE,
  "estimatedsalary" DOUBLE,
  "exited" BIGINT
);