Exercise - Logistic Regression

The client bank XYZ is running a direct marketing (phone calls) campaign. The classification goal is to predict if the client will subscribe a term deposit or not.

The data is obtained from UCI Machine Learning repository

Attribute Information:

bank client data:

• age: (numeric)
• job : type of job (categorical: 'admin.','blue-collar','entrepreneur','housemaid','management','retired','self-employed','services','student','technician','unemployed','unknown')
• marital : marital status (categorical: 'divorced', 'married', 'single', 'unknown'; note: 'divorced' means divorced or widowed)
• education (categorical: 'basic.4y', 'basic.6y', 'basic.9y','high.school', 'illiterate' ,'professional.course', 'university.degree', 'unknown')
• default: has credit in default? (categorical: 'no','yes','unknown')
• **housing: has housing loan? (categorical: 'no','yes','unknown')
• loan: has personal loan? (categorical: 'no','yes','unknown')

related with the last contact of the current campaign:

• contact: contact communication type (categorical: 'cellular', 'telephone')
• month: last contact month of year (categorical: 'jan', 'feb', 'mar', ..., 'nov', 'dec')
• day: last contact day of the month (numerical: 1, 2, 3, 4, ...)
• 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:

• 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 (numeric; 999 means 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 (categorical: 'failure','nonexistent','success')

Output variable (desired target):

• deposit - has the client subscribed a term deposit? (binary: 'yes','no')