# Context This is the dataset that is used for the paper " A data-driven approach to predict the success of bank telemarketing ". IMHO it's a good dataset for training oneself in M.L. by building one classifier. After you can compare your results with the paper. # Content Input variables: **bank client data:** 1. age (numeric) job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown') 2. marital : marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed) 3. education (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown') 4. default: has credit in default? (categorical: 'no','yes','unknown') 5. housing: has housing loan? (categorical: 'no','yes','unknown') loan: 6. has personal loan? (categorical: 'no','yes','unknown') **related with the last contact of the current campaign:** 7. contact: contact communication type (categorical: 'cellular','telephone') 8. month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec') 9. day_of_week: last contact day of the week (categorical: 'mon','tue','wed','thu','fri') 10. duration: last contact duration, in seconds (numeric). Important note: this attribute highly affects the output target (e.g., if duration=0 then y='no'). Yet, the duration is not known before a call is performed. Also, after the end of the call y is obviously known. Thus, this input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model. **other attributes:** 11. campaign: number of contacts performed during this campaign and for this client (numeric, includes last contact) 12. pdays: number of days that passed by after the client was last contacted from a previous campaign (numeric; 999 means client was not previously contacted) 13. previous: number of contacts performed before this campaign and for this client (numeric) 14. poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success') **social and economic context attributes** 15. emp.var.rate: employment variation rate - quarterly indicator (numeric) 16. cons.price.idx: consumer price index - monthly indicator (numeric) 17. cons.conf.idx: consumer confidence index - monthly indicator (numeric) 18. euribor3m: euribor 3 month rate - daily indicator (numeric) 19. nr.employed: number of employees - quarterly indicator (numeric) **Output variable (desired target):** 20. y - has the client subscribed a term deposit? (binary: 'yes','no') # Citation Request (Acknowledgements) This dataset is public available for research. The details are described in [Moro et al., 2014]. Please include this citation if you plan to use this database: [Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014