Crisp Dm

6. Collect and explore data, identifying issues; for instance, checking COVID-19 patient data for missing or faulty entries.
8. Translate business objectives into a data mining problem, like assessing COVID-19 death risk based on predispositions like diabetes.
10. Implement models into systems for real-time data categorization; deploy in healthcare systems after thorough vital sign checks.

1. Apply techniques (e.g., Clustering) for prediction; data format adjustments might occur based on model requirements, such as using supervised models for disease contraction prediction.
2. Clean and format data for analysis, ensuring quality; analysts prepare data for modeling in suitable formats.
3. Merging multiple datasets for comprehensive analysis, such as integrating sales and customer data to refine marketing strategies.
4. Test models, ensuring generalization and addressing business concerns; select champion models meeting set accuracy criteria, like 90% prediction accuracy.
5. Unorganized data lacking a predefined structure, such as social media posts, requiring advanced techniques like natural language processing for meaningful analysis.
7. Extracting meaningful insights from structured data, like analyzing sales figures to identify trends and customer preferences.
9. Organized data with clear format, like databases storing customer names, addresses, and purchase history for easy analysis.