Machine Learning Workflow (Simplified) - Exercise

Bank Analysis

Task

  • Classification

Objective

  • Predict whether a bank client will subscribe to a term deposit.

Data Description

Download the dataset from the following link: Data

Bank Client Data

  • age: Age of the client (numeric).
  • job: Type of job (categorical: admin., unknown, unemployed, management, housemaid, entrepreneur, student, blue-collar, self-employed, retired, technician, services).
  • marital: Marital status (categorical: married, divorced, single; divorced includes widowed individuals).
  • education: Level of education (categorical: unknown, secondary, primary, tertiary).
  • default: Has credit in default? (binary: yes, no).
  • balance: Average yearly balance in euros (numeric).
  • housing: Has a housing loan? (binary: yes, no).
  • loan: Has a personal loan? (binary: yes, no).

Communication Data from the Last Campaign

  • contact: Type of communication used (categorical: unknown, telephone, cellular).
  • day: Last contact day of the month (numeric).
  • month: Last contact month (categorical: jan, feb, mar, …, nov, dec).
  • duration: Last contact duration in seconds (numeric).

Other Attributes

  • campaign: Number of contacts performed during this campaign for this client (numeric, includes last contact).
  • pdays: Number of days passed since the client was last contacted in a previous campaign (numeric, -1 means never contacted before).
  • previous: Number of contacts performed before this campaign for this client (numeric).
  • poutcome: Outcome of the previous marketing campaign (categorical: unknown, other, failure, success).

Target Variable (Desired Output)

  • y: Has the client subscribed to a term deposit? (binary: yes, no).