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

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