Baselight

Bank Marketing

Bank marketing Dataset

@kaggle.dhirajnirne_bank_marketing

Bank Marketing
@kaggle.dhirajnirne_bank_marketing.bank_marketing

  • 433.85 KB
  • 45211 rows
  • 23 columns
age

Age

age_group

Age Group

eligible

Eligible

job

Job

salary

Salary

marital

Marital

education

Education

marital_education

Marital-education

targeted

Targeted

default

Default

balance

Balance

housing

Housing

loan

Loan

contact

Contact

day

Day

month

Month

duration

Duration

campaign

Campaign

pdays

Pdays

previous

Previous

poutcome

Poutcome

y

Y

response

Response

585Ymanagement100000marriedtertiarymarried-tertiaryyesno2143yesnounknown5may2611-1unknownno
444Ytechnician60000singlesecondarysingle-secondaryyesno29yesnounknown5may1511-1unknownno
333Yentrepreneur120000marriedsecondarymarried-secondaryyesno2yesyesunknown5may761-1unknownno
474Yblue-collar20000marriedunknownmarried-unknownnono1506yesnounknown5may921-1unknownno
333Yunknownsingleunknownsingle-unknownnono1nonounknown5may1981-1unknownno
353Ymanagement100000marriedtertiarymarried-tertiaryyesno231yesnounknown5may1391-1unknownno
282Ymanagement100000singletertiarysingle-tertiarynono447yesyesunknown5may2171-1unknownno
424Yentrepreneur120000divorcedtertiarydivorced-tertiarynoyes2yesnounknown5may3801-1unknownno
585Yretired55000marriedprimarymarried-primaryyesno121yesnounknown5may501-1unknownno
434Ytechnician60000singlesecondarysingle-secondaryyesno593yesnounknown5may551-1unknownno

CREATE TABLE bank_marketing (
  "age" BIGINT,
  "age_group" BIGINT,
  "eligible" VARCHAR,
  "job" VARCHAR,
  "salary" BIGINT,
  "marital" VARCHAR,
  "education" VARCHAR,
  "marital_education" VARCHAR,
  "targeted" VARCHAR,
  "default" VARCHAR,
  "balance" BIGINT,
  "housing" VARCHAR,
  "loan" VARCHAR,
  "contact" VARCHAR,
  "day" BIGINT,
  "month" VARCHAR,
  "duration" BIGINT,
  "campaign" BIGINT,
  "pdays" BIGINT,
  "previous" BIGINT,
  "poutcome" VARCHAR,
  "y" VARCHAR,
  "response" BIGINT
);

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