Predicting If Customer Will Subscribe to a Term Deposit

On November 9th to 10th 2019, my team won the First Place for Best Model Award at Data Hackathon (a less than 24 data science competition) held by OCRUG and Merage Analytics Club at The Paul Merage School of Business. We were a team of 3 people from UCI Master’s in Business Analytics Program.

Background:

The data is about direct marketing campaigns (phone calls) of a Portuguese banking institution from May 2008 to November 2010. The clients were contacted more than once in order to access if the product (bank term deposit) would be (‘yes’) or not (‘no’) subscribed.

Goal:

Predict whether the client will subscribe (yes/no) to a term deposit. Key Process Owners can use the output of this prediction to improve the stragegy for the next market campaign.

Data Dictionary:

Independent Variable

Bank client data

• Age (numeric)
• Job : type of job
• Marital : marital status
• Education
• Default: has credit in default? (“yes”,“no”)
• Balance: average yearly balance, in euros
• Housing: has housing loan? (“yes”,“no”)
• Loan: has personal loan? “yes”,“no”)

Related with the last contact of the current campaign

• Contact: contact communication type
• Day: last contact day of the month
• Month: last contact month of year
• Duration: last contact duration, in seconds (numeric)

Other Variables

• Campaign: number of contacts performed during this campaign and for this client
• Pdays: number of days that passed by after the client was last contacted from a previous campaign (-1 means - client was not previously contacted)
• Previous: number of contacts performed before this campaign and for this client
• Poutcome: outcome of the previous marketing campaign

Dependent Variable (desired target)

• Deposit - has the client subscribed a term deposit? (“yes”,“no”)

Presentation

• The details around the results and methodology can be found in the document:
  • Hackathon_Presentation