CRISP-DM stands for Cross-Industry Standard Process for Data Mining, a widely adopted framework that provides a structured approach to planning, organizing, and conducting data mining projects. It was developed in 1996 by Daimler-Benz, SPSS, and NCR.
CRISP-DM provides a flexible, iterative process that allows for refinement at any stage, making it adaptable to various industries and types of data mining projects.
graph TD
A[Business Understanding] --> B[Data Understanding]
B --> C[Data Preparation]
C --> D[Modeling]
D --> E[Evaluation]
E --> F[Deployment]
F --> A
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Business Understanding: Focuses on understanding the project’s objectives from a business perspective. The goal is to translate the business problem into a data mining problem.
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Data Understanding: Involves collecting, exploring, and understanding the data to ensure its quality and to get insights relevant to the problem.
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Data Preparation: This phase includes all activities needed to construct the final dataset, such as data cleaning, transformation, and feature selection.
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Modeling: Various modeling techniques are applied to the data, and parameters are tuned to improve performance. Models like decision trees, neural networks, or clustering may be used.
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Evaluation: The model is thoroughly evaluated to ensure it meets the business objectives and provides actionable insights.
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Deployment: The final model is deployed in a production environment. This could involve automating a process, generating reports, or integrating the model into a decision-support system.