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.

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

5. Preprocessing Model

6. Read File

7. Preprocess Text

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

22. Classifier
https://www.youtube.com/watch?v=Q8l0Vip5YUwFonte:

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

25. Thanks!
Guttenberg Ferreira Passos
gut.passos@gmail.com