This project contains guidance on the important steps of the CRISP-DM cycle.
The CRoss-Industry Standard Process for Data Mining (CRISP-DM) is a standard process model that describes common approaches to conducting a data mining project.
CRISP-DM organizes the data mining process into six phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.
Figure: Phases of the CRISP-DM
This phase aims to understand the project objectives and requirements from a business perspective, converting the knowledge into a data mining problem definition and then developing a preliminary plan to achieve the objectives.
This phase aims to increase familiarity with the data, to identify data quality problems, to discover initial insights into the data and detect interesting subsets to form hypotesis about hidden information.
This phase aims to build the final dataset to be used in the modeling tools.
This phase aims to select and apply modeling techniques to calibrate their parameters to optimal values.
This phase aims to evaluate the model and review the model construction steps to ensure it adequately achieves business objectives and also to verify if considered all business issues.
This phase aims to organize and present the knowledge acquired with the project so that the client can use the created models in the organization’s decision-making processes.
All the steps are explained in detail on the link below.
- Project: The Crisp-DM Methodology