The Crisp-DM methodology data mining cycle 

The CRISP-DM methodology considers the analytical activities as a cyclical set of phases to be repeated until a satisfactory result is obtained. Not surprisingly then, Crisp-DM methodology phases are usually represented as a circle going from business understanding to the final deployment:

As we can see within the diagram, the cycle is composed of six phases:

• Business understanding
• Data understanding 
• Data preparation
• Modeling
• Evaluation
• Deployment

This is the greater abstraction level of the Crisp-DM methodology, meaning one that can apply, with no exception, to all data mining problems. Three more specific layers are then conceived as a conjunction between the general model and the specific data mining project:

• Generic tasks
• Specialized tasks
• Process instances

All of the components of every level are mapped to one component of the layer above, so that when dealing with a specific data mining problem, both bottom-up and top-down approaches are allowed, as...