The dataset used for this project is derived from a Portuguese banking institution. It’s a dataset from one of their campaigns from May 2008 to November 2010, in this campaign direct phone call marketing strategy was applied with the aim to get the existing customers to do term deposits.
• age: age of client (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')
• education: (categorical: 'basic.4y','basic.6y','basic.9y','high.school','illiterate','professional.course','university.degree','unknown')
• default: any credit in default? (categorical: 'no','yes','unknown')
• balance: the amount of money in an account.
• housing: housing loan (categorical: 'no','yes','unknown')
• loan: personal loan (categorical: 'no','yes','unknown')
• contact: contact communication type (categorical: 'cellular','telephone')
• day: day of the month
• month: last contact month of year (categorical: 'jan', 'feb', 'mar' etc)
• duration: last contact duration, in seconds (numeric)
• campaign: number of contacts made during that campaign and for that particular client(numeric)
• pdays: how many days have passed since the client was last contacted (numeric; 999 means client was not previously contacted)
• previous: number of prior to that campaign and for that client (numeric)
• y: has the client subscribed a term deposit? (binary: 'yes','no')
The jupyter file in the repository explains the functioning of the code.