CRISP-DM Process

The model splits a data mining project into six phases and it allows for needing to go back and forth between different stages.


1. Business Understanding
  • Understanding the business goal
  • Situation assessment
  • Translating the business goal in a data mining objective
  • Development of a project plan
2. Data understanding
  • Considering data requirements
  • Initial data collection, exploration, and quality assessment
3. Data preparation
  • Selection of required data
  • Data acquisition
  • Data integration and formatting
  • Data cleaning
  • Data tranaformation and enrichment
4. Modeling
  • Selection of appropriate modeling technique
  • Splitting of the dataset into training and testing subsets for evaluation purposes
  • Development and examination of alternative modeling algorithms and parameter settings
  • Fine tuning of the model settings according to an initial assessment of the model’s performance
5. Model evaluation
  • Evaluation of the model in the context of the business success criteria
  • Model approval
6. Deployment
  • Create a report of findings
  • Planning and development of the deployment procedure
  • Deployment of the model
  • Distribution of the model results and integration in the organisation’s operational system
  • Development of a maintenance / update plan
  • Review of the project
  • Planning the next steps


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