Bank Churn Pre-processed Dataset

A complete pipeline from raw data to model-ready features 🚀

@kaggle.faizanyousafonly_bank_churn_pre_processed_dataset

Table List

3 tables(6.34 MB)

Train Preprocessed

Test Preprocessed

Sample Submission

Test Preprocessed

@kaggle.faizanyousafonly_bank_churn_pre_processed_dataset.test_preprocessed

2.34 MB
110023 rows
18 columns

id Id	customerid CustomerId	surname Surname	creditscore CreditScore	age Age	tenure Tenure	balance Balance	numofproducts NumOfProducts	hascrcard HasCrCard	isactivemember IsActiveMember	estimatedsalary EstimatedSalary	agecategory AgeCategory	creditscorecategory CreditScoreCategory	balancecategory BalanceCategory	salarycategory SalaryCategory	geography_germany Geography Germany	geography_spain Geography Spain	gender_male Gender Male
165034	15773898	Lucchese	586	23	2		2		1	160976.75	2	1	3
165035	15782418	Nott	683	46	2		1	1		72549.27	3	2	3	2
165036	15807120	K?	656	34	7		2	1		138882.09	2	1	3	4
165037	15808905	O'Donnell	681	36	8		1	1		113931.57	3	2	3	3			1
165038	15607314	Higgins	752	38	10	121263.62	1	1		139431	3	3	1	4	1		1
165039	15672704	Pearson	593	22	9		2			51907.72	2	1	3	1
165040	15647838	Onyemere	682	45	4		2	1	1	157878.67	3	2	3			1	1
165041	15775307	Hargreaves	539	47	8		2	1	1	126784.29	3	4	3	4		1
165042	15653937	Hsueh	845	47	3	111096.91	1	1		94978.1	3		1	3
165043	15752344	Teng	645	30	5		2		1	149195.44	2	1	3	4		1	1


CREATE TABLE test_preprocessed (
  "id" BIGINT,
  "customerid" BIGINT,
  "surname" VARCHAR,
  "creditscore" BIGINT,
  "age" DOUBLE,
  "tenure" BIGINT,
  "balance" DOUBLE,
  "numofproducts" BIGINT,
  "hascrcard" DOUBLE,
  "isactivemember" DOUBLE,
  "estimatedsalary" DOUBLE,
  "agecategory" BIGINT,
  "creditscorecategory" DOUBLE,
  "balancecategory" DOUBLE,
  "salarycategory" DOUBLE,
  "geography_germany" DOUBLE,
  "geography_spain" DOUBLE,
  "gender_male" DOUBLE
);