Welcome to my page. My job is to identify whether a client will subscribe to a bank term deposit, by analysing personal details and previous contact history. I have been trained and cross-validated with about 1.6 thousand real-world cases and am able to detect up to 78% of potential subscribers.

My outputs are:

[yes]: The client is likely to subscribe
[no]: The client will not subscribe

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TRY ME, TYPE IN YOUR CLIENT DETAILS HERE:

I need to know the feature bellow to make a prediction:

Personal data

• [age]: (numeric)
• [job]: type of job
• [marital]: marital status
• [education]: education (categorical)
• [default]: has credit in default
• [housing]: has housing loan?
• [loan]: has personal loan?

Last contact of the current campaign:

• [contact]: contact communication type
• [month]: last contact month of year
• [day_of_week]: last contact day of the week (categorical: ‘mon’,’tue’,’wed’,’thu’,’fri’)

Other attributes:

• [campaign]: number of contacts performed during this campaign and for this client (numeric, includes last contact)
• [pdays]: number of days that passed by after the client was last contacted from a previous campaign (999 if the client was not previously contacted)
• [previous]: number of contacts performed before this campaign and for this client (numeric)
• [poutcome]: outcome of the previous marketing campaign
• [duration]: last contact duration, in seconds. This input should only be included for benchmark purposes and should be discarded if the intention is to have a realistic predictive model.

Credit: Data from 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