CRISP-DM

NoLabelActionIncludes
1Business UnderstandingBusiness ObjectivesBackground

Business objectives

Business Success Criteria

Assess SituationList of requirements

Assumption and constraints

Risks and contingencies

Terminology

Cost and benefit

Data Mining OutcomesData mining goals

Data mining success criteria

Project PlanA project plan

Assessment of likely tools and techniques

2Data UnderstandingCollect Initial DataInitial data collection report
Describe DataData description report
Explore DataData exploration report
Verify Data QualityData quality report
3Data PreparationSelect DataRational for inclusion/ exclusion
Clean DataData cleaning report
Construct DataDerived attributes

Generates report

Integrate DataMerge Data
Format DataReformat data

Revised parameter settings

4ModelingSelect Modeling TechniquesModeling technique

Modeling assumptions

Generate Test DesignTest Design
Build ModelParameter Settings

Models

Model Description

Assess ModelModel assessment

Revised parameter settings

5EvaluationEvaluate ResultsAssessment of data

Approve model

Review ProcessReview process
Determine Next StepList of future actions decisions
6DeploymentPlan DeploymentDeployment plan
Plan Monitoring & MaintenanceMonitoring and maintenance plan
Produce Final ReportFinal reportFinal presentation
Review ProjectDocument experience

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