Bank Customer Churn Prediction

@kaggle.marslinoedward_bank_customer_churn_prediction

Table List

1 table(329.07 KB)

Churn Modelling

@kaggle.marslinoedward_bank_customer_churn_prediction.churn_modelling

329.07 KB
10000 rows
14 columns

rownumber RowNumber	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
1	15634602	Hargrave	619	France	Female	42	2		1	1	1	101348.88	1
2	15647311	Hill	608	Spain	Female	41	1	83807.86	1		1	112542.58
3	15619304	Onio	502	France	Female	42	8	159660.8	3	1		113931.57	1
4	15701354	Boni	699	France	Female	39	1		2			93826.63
5	15737888	Mitchell	850	Spain	Female	43	2	125510.82	1	1	1	79084.1
6	15574012	Chu	645	Spain	Male	44	8	113755.78	2	1		149756.71	1
7	15592531	Bartlett	822	France	Male	50	7		2	1	1	10062.8
8	15656148	Obinna	376	Germany	Female	29	4	115046.74	4	1		119346.88	1
9	15792365	He	501	France	Male	44	4	142051.07	2		1	74940.5
10	15592389	H?	684	France	Male	27	2	134603.88	1	1	1	71725.73


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