CRISP-DM model

CRISP-DM

The model splits a data mining project into six phases and it allows for needing to go back and forth between different stages. I’d personally stick a few more backwards arrows but it’s generally fine. The CRISP-DM model applies equally well to a data science project.

CRISP-DM Process diagram by Kenneth Jensen (Own work) [CC BY-SA 3.0 (http://creativecommons.org/licenses/by-sa/3.0)], via Wikimedia Commons

TYPICAL ACTIVITIES IN EACH PHASE

• Business Understanding
  • Understanding the business goal
  • Situation assessment
  • Translating the business goal in a data mining objective
  • Development of a project plan
• Data understanding
  • Considering data requirements
  • Initial data collection, exploration, and quality assessment
• Data preparation
  • Selection of required data
  • Data acquisition
  • Data integration and formatting […]
  • Data cleaning
  • Data transformation and enrichment […]
• 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
• Model evaluation
  • Evaluation of the model in the context of the business success criteria
  • Model approval
• 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

CRISP-DM’S VALUE

The CRISP-DM process outlines the steps involved in performing data science activities from business need to deployment, and most importantly it indicates how iterative this process is and that you never get things perfectly right.