The problem for this course project is related to direct marketing campaigns (phone calls) of a Portuguese banking institution. The classification goal is to predict whether the client subscribes a term deposit or not. The target class is the last attribute (subscribed) and has two values (yes and no).
The training set is in trainset.csv and the test set is in testset.csv. The dataset contains subscribed (yes) and unsubscribed (no) customers.
1. age (numeric)
2. job: type of job (categorical: 'admin.','blue-collar', 'entrepreneur', 'housemaid', 'management', 'retired','self-employed', 'services', 'student', 'technician', 'unemployed', 'unknown')
3. marital: marital status (categorical: 'divorced','married','single','unknown'; note: 'divorced' means divorced or widowed)
4. education: (categorical: 'basic.4y', 'basic.6y', 'basic.9y', 'high.school', 'illiterate', 'professional.course', 'university.degree', 'unknown')
5. housing: has housing loan? (categorical: 'no','yes','unknown')
6. loan: has personal loan? (categorical: 'no','yes','unknown')
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).
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. poutcome: outcome of the previous marketing campaign (categorical: 'failure','nonexistent','success')
14. nr.employed: number of employees - quarterly indicator (numeric)
15. Target Attribute: Subscribed - has the client subscribed a term deposit? (binary: 'yes','no')