CRISP-DM is a data-mining methodology that structures analytical work into phases such as business understanding, data understanding, preparation, modeling, evaluation, and deployment.
CRISP-DM is a data-mining methodology for structuring analytical work from business understanding through deployment. It emerged for early machine-learning practice, where patterns were extracted from data to produce insights. section 1.3
This explainer was generated by Manning's knowledge graph from the text of Delivering Machine Learning Projects: From design to deployment. The section links point to the parts of the book it draws on.
