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

Train Preprocessed

@kaggle.faizanyousafonly_bank_churn_pre_processed_dataset.train_preprocessed

3.35 MB
165034 rows
19 columns

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


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