Data Archive

Data Mining - CRISP

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

  1. 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.

  2. Data Understanding: Involves collecting, exploring, and understanding the data to ensure its quality and to get insights relevant to the problem.

  3. Data Preparation: This phase includes all activities needed to construct the final dataset, such as data cleaning, transformation, and feature selection.

  4. 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.

  5. Evaluation: The model is thoroughly evaluated to ensure it meets the business objectives and provides actionable insights.

  6. 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.

Graph View