Problem prediction model
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The document describes a problem prediction model that uses artificial intelligence algorithms to evaluate changes made by an IT company and anticipate potential problems. The model analyzed 194 known problems, 2,400 past changes, and 201 predicted future changes. As a result, the model identified one change from October 29, 2019 that was likely to cause a problem. A team is investigating this potential issue. The document concludes that the naive Bayes classifier model is an important tool for change analysis and problem prediction.
Problem prediction model
- 1. Guttenberg Ferreira Passoshttps://medium.com/@kozyr_91350/o-guia-definitivo- para-come%C3%A7ar-com-ia-e7e7dc68f376 Image: Cassie Kozyrkov Problem Prediction Model
- 2. Agenda 1. Challenges 2. Preprocessing Model 3. Analysis Model 4. Clustering model 5. Training Model 6. Prediction Model 7. Predictions 8. Conclusion
- 3. Challenges 1. Identify the most relevant change and problem (ITIL) words in an Information Technology company; 2. Evaluate changes using various artificial intelligence algorithms to anticipate potential problems.
- 4. Orange Canvas • Sandeco https://www.youtube.com/watch?v=5bjY_WyZKm4 • Ajda Pretnar https://www.youtube.com/watch?v=HXjnDIgGDuI&t=10s
- 6. Read File
- 8. Bag of Words
- 9. Word Cloud
- 10. Corpus Viewer
- 11. Data Table
- 12. Analysis Model
- 13. Clustering model
- 14. Multidimensional Scale - MDS
- 15. Training Model
- 16. Test & Score
- 17. Confusion Matrix
- 18. Area Under the Curve - ROC
- 19. Prediction Model
- 20. Predictions
- 21. Classifiers AdaBoost https://en.wikipedia.org/wiki/AdaBoost KNN https://en.wikipedia.org/wiki/K-nearest_neighbors_algorithm Logistic Regression https://en.wikipedia.org/wiki/Logistic_regression Naive Bayes https://en.wikipedia.org/wiki/Naive_Bayes_classifier Random Forest https://en.wikipedia.org/wiki/Random_forest
- 23. The Problem Prediction Model has the function of evaluate the changes (Change Management discipline - ITIL), using various artificial intelligence algorithms, to anticipate possible problems (Problem Management discipline – ITIL). All 194 knowledge base problems, 2,400 changes from the last 3 months, and 201 forecast changes were used in the model. The result can already be considered positive, because the model identified, for example, one change of 10/29/2019, likely to cause a problem. A multidisciplinary team was looking into that problem, still out of the knowledge base, just to address this issue. We conclude that the Model is an important tool for change analysis aiming to identify the main occurrences and anticipate possible problems, using the Naive Bayes classifier from Orange software. Conclusion
- 24. Next steps 1. Identify the keywords (tokens & tags) of changes and problems to properly feed Bag of Words 2. Replace the contents of the Impact field of the problem file with another type of identification (Token & Tag) 3. Replace the contents of the Customers field from the problem file with the Customer related to the Control Item - CI 4. Identify problem-causing keywords (tokens & tags) 5. Develop Incident Model 6. Develop a Decision Intelligence Model based on the Cynefin framework to determine the context of change according to the 5 realms: simple, complicated, complex, chaotic and disorder