The CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology is a widely used process model for data mining projects. It consists of six phases:

1. Business Acumen: This phase involves understanding the project objectives and requirements from a business perspective, and determining how data mining can help achieve these objectives.

2. Understanding Data: In this phase, the data is collected, examined and analyzed to gain a better understanding of its structure, quality and completeness.

3. Data Preparation: This phase involves preparing the data for analysis by selecting, cleaning, transforming and integrating it into a form suitable for modeling.

4. Modeling: In this phase, various modeling techniques are applied to the prepared data to create predictive or descriptive models that can be used to achieve business objectives.

5. Evaluation: This phase involves evaluating the effectiveness and accuracy of the models created in the previous phase and selecting the best model for implementation.

6. Implementation: In this final phase, the selected model is deployed into the operational environment and its performance is monitored and maintained.

CRISP-DM is an iterative process, meaning each phase can be revisited and refined as new information is gathered or new business requirements arise. The methodology provides a structured approach to data mining projects and helps ensure that they are executed efficiently, effectively and with a clear focus on achieving business objectives.

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