CRISP-DM is a process for data mining projects with six main phases: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Each phase involves several steps and produces reports and logs to document the process. The goal is to iteratively apply data mining techniques to achieve the business objectives.

1. CRISP-DM
-- ASHISH CHANDRA JHA

2. © Nicole Leaper

3. Business Understanding
1. Determine Business Objectives
• Background
• Business Objectives
• Business Success Criteria (Log and Report Process)
2. Assess Situation
• Inventory of Resources
• Requirements
• Assumptions
• and Constraints
• Risks and Contingencies Terminology
• Costs and Benefits (Log and Report Process)

4. Continued…
3. Determine Data Mining Goals
• Data Mining Goals
• Data Mining Success Criteria (Log and Report Process)
4. Produce Project Plan
• Project Plan
• Initial Assessment of Tools and
• Techniques
• (Log and Report Process)

5. Data Understanding
1. Collect Initial DataBackground
• Business Objectives
• Business Success Criteria (Log and Report Process)
2. Describe Data
• Data Description Report(Log and Report Process)
3. Explore Data
• Data Exploration Report (Log and Report Process)
4. Verify Data Quality
• Data Quality Report (Log and Report Process)

6. Data Preparation
1. Data Set
• Data Set Description (Log and Report Process)
2. Select Data
• Rationale for Inclusion/ Exclusion (Log and Report Process)
3. Clean Data
• Data Cleaning Report (Log and Report Process)

7. Continued…
4. Construct Data
• Derived Attributes
• Generated Records (Log and Report Process)
5. Integrate Data
• Merged Data (Log and Report Process)

8. Modeling
1. Select Modeling Technique
• Modeling Technique
• Modeling Assumptions
• (Log and Report Process)
2. Generate Test Design
• Test Design (Log and Report Process)

9. Continued…
3. Build Model Parameter Settings
• Models
• Model Description
• (Log and Report Process)
4. Assess Model
• Model Assessment
• Revised Parameter
• (Log and Report Process)

10. Evaluation
1. Evaluate Results
• Align Assessment of Data Mining Results with Business Success Criteria
(Log and Report Process)
2. Approved Models
• Review Process
• Review of Process (Log and Report Process)
3. Determine Next Steps
• List of Possible Actions Decision (Log and Report Process)

11. Deployment
1. Plan Deployment
Deployment Plan (Log and Report Process)
2. Plan Monitoring and Maintenance
• Monitoring and Maintenance Plan (Log and Report Process)
3. Produce Final Report
• Final Report
• Final Presentation (Log and Report Process)
4. Review Project
Experience
Documentation (Log and Report Process)

12. Thank You