Bank Churn Dataset

Deciphering Customer Loyalty: Analyzing Bank Churn through Binary Classification

@kaggle.harshitstark_dataset

Table List

4 tables(7.69 MB)

Bank Churn Output Submission

Sample Submission

Test

Train

Test

@kaggle.harshitstark_dataset.test

2.2 MB
110023 rows
13 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
165034	15773898	Lucchese	586	France	Female	23	2		2		1	160976.75
165035	15782418	Nott	683	France	Female	46	2		1	1		72549.27
165036	15807120	K?	656	France	Female	34	7		2	1		138882.09
165037	15808905	O'Donnell	681	France	Male	36	8		1	1		113931.57
165038	15607314	Higgins	752	Germany	Male	38	10	121263.62	1	1		139431
165039	15672704	Pearson	593	France	Female	22	9		2			51907.72
165040	15647838	Onyemere	682	Spain	Male	45	4		2	1	1	157878.67
165041	15775307	Hargreaves	539	Spain	Female	47	8		2	1	1	126784.29
165042	15653937	Hsueh	845	France	Female	47	3	111096.91	1	1		94978.1
165043	15752344	Teng	645	Spain	Male	30	5		2		1	149195.44


CREATE TABLE test (
  "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
);