Introduction to CRISP-DM

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 CRISP-DM, which stands for Cross-Industry Standard Process for Data Mining, is a proven methodology guiding data mining efforts. It provides a structured approach for data-driven decision-making and consists of six stages:

  1. Business Understanding: This stage outlines the intentions of the data analysis project, setting project goals and objectives. Clear communication is crucial to overcome stakeholders' differing objectives and biases.

  2. Data Understanding: In this stage, data scientists decide on data sources, acquire data, and understand its characteristics. It combines data requirements, data collection, and data understanding from other methodologies.

  3. Data Preparation: Data collected is transformed into a usable subset, addressing missing or ambiguous data values. Data preparation ensures the dataset is suitable for analysis.

  4. Modeling: Data models are created to reveal patterns and structures within the data, addressing the stated business problem and goals. Model selection is done based on subsets of the data, with adjustments made as needed.

  5. Evaluation: The selected model is tested using pre-selected test data to assess its effectiveness. Testing results determine the model's efficacy and its role in the next stage.

  6. Deployment: The model is used on new data outside the dataset's scope. New interactions may reveal the need for different data or models. Deployment results might initiate revisions to the business needs, actions, model, or data.

CRISP-DM is iterative and cyclical, with stages being revisited as needed. After completing all stages, stakeholders discuss the results, similar to the Feedback stage in other methodologies. The process continues until stakeholders agree that the data model and analysis provide the answers needed to resolve business problems and attain goals.

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