CRISP-DM methodology

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The cross-industry standard process for data mining or CRISP-DM is an open standard process framework model for data mining project planning, created in 1996.

The process of CRISP-DM is into 6 phases or components:

  1. Business understanding – What does the business need?
  2. Data understanding – What data do we have / need? Is it clean?
  3. Data preparation – How do we organize the data for modeling?
  4. Modeling – What modeling techniques should we apply?
  5. Evaluation – Which model best meets the business objectives?
  6. Deployment – How do stakeholders access the results?

1. Business understanding

Basic checklist:

  • Understand business requirements
  • Form a business question
  • Turn the business question into ML question/s
  • Define criteria for successful outcome of the project
  • Highlight project’s critical features
  • List assumptions
  • List resources (specially data resources)
  • List risks and potential mitigation actions
  • Build a return of investment (ROI) calculation

2. Data understanding

Basic checklist:

  • Data collection:
    • Define steps to extract data
    • Analyze data for detecting additional requirements
    • Consider other data sources
  • Data properties
    • Describe data: amount, metadata, properties
    • Find features and relationships in the data
  • Quality
    • Verifying attributes
    • Identifying missing data
    • Reveal inconsistencies
    • List problems

3.- Data preparation

Basic checklist:

  • Final dataset selection
    • Analyze constraints: size, included/excluded columns, record selection, data types
  • Final dataset preparation
    • Steps: clean, transform, merge, format
    • Make decisions about what to do with missing data. Take notes about these decisions.
    • Make decisions about what to do with missing properties. Take notes about these decisions.

4.- Modelling

Basic checklist:

  • Select a model, create it
  • Create a testing plan for that model
    • Split training and testing datasets
    • model evaluation criterium
  • Tune and test the different available parameters
    • Tweak the model for better performance
    • Describe the trained models and report findings

5.- Evaluation

Basic checklist:

  • What is the accuracy of the model?
  • Model generalization on unseen/unknown data
  • Perform quality assurance checks
    • Was any important criteria overlooked?
    • What is the model performance using determined data?
    • Is data available for future training?
  • Does the model meets the success criteria? yes / no

6.- Deployment

Basic checklist:

  • Planning deployment
    • Understand infrastructure deployment requirements and processes
    • Understand applications deployment requirements and processes
    • Understand required environments to deploy (testing, QA, production)
    • Determine how code is going to be managed between environments (probably done already)
  • Maintenance and monitoring
    • Enable the required monitoring tools, views and reports.
    • Review data quality thresholds.
  • Final report
    • Document all processes used in the project
    • Review all goals defined for the project and if they met/not-met them (and reasons)
    • Detail findings of the project
    • Explain the model used and the reason this model is selected as the right one.
    • Explain the models tested but finally discarded, and its reasons
    • Identify the customer groups using this model
  • Project review
    • Summarize the results and present to stakeholders

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