CRISP-DM

No	Label	Action	Includes
1	Business Understanding	Business Objectives	Background Business objectives Business Success Criteria
		Assess Situation	List of requirements Assumption and constraints Risks and contingencies Terminology Cost and benefit
		Data Mining Outcomes	Data mining goals Data mining success criteria
		Project Plan	A project plan Assessment of likely tools and techniques
2	Data Understanding	Collect Initial Data	Initial data collection report
		Describe Data	Data description report
		Explore Data	Data exploration report
		Verify Data Quality	Data quality report
3	Data Preparation	Select Data	Rational for inclusion/ exclusion
		Clean Data	Data cleaning report
		Construct Data	Derived attributes Generates report
		Integrate Data	Merge Data
		Format Data	Reformat data Revised parameter settings
4	Modeling	Select Modeling Techniques	Modeling technique Modeling assumptions
		Generate Test Design	Test Design
		Build Model	Parameter Settings Models Model Description
		Assess Model	Model assessment Revised parameter settings
5	Evaluation	Evaluate Results	Assessment of data Approve model
		Review Process	Review process
		Determine Next Step	List of future actions decisions
6	Deployment	Plan Deployment	Deployment plan
		Plan Monitoring & Maintenance	Monitoring and maintenance plan
		Produce Final Report	Final reportFinal presentation
		Review Project	Document experience

Dan Campbell