This document summarizes a dataset containing over 2 million records of vehicle accident records from the United States between 2016 and 2019. It includes 49 data columns and identifies the severity of accidents as the target variable to predict. The document explores data cleaning, feature engineering, and compares the performance of different machine learning models like support vector machines, decision trees, naive bayes, and more in predicting accident severity. It concludes that the dataset provides valuable insights for future traffic accident analysis and research to improve transportation safety and infrastructure planning.

1. POM 681

2. US Accidents
Countrywide
Collected from 2016
to 2019
2.25 million records
49 columns
Response variable is
Severity

3. Severity 1: Low Impact
Severity 2: Minor
Severity 3: Moderate
Severity 4: Serious

4. DATA
EXPLORATION

5. WHERE?

7. WEATHER CONDITIONS

8. Weather Conditions

9. DIRECTIONS

13. DATA
EXPLORATION

14. Year Month

15. Weekend or
Weekday that is
the question?

17. Let’s talk about..

20. KEEP
CALM OUR
MODELS
ARE HERE…

23. Support Vector Machine
Training Testing

24. Decision Tree Model

25. Decision Tree
Training
Testing

26. Naive Bayes Model
Training
Testing
Multinomial Model

27. Best Model of the Semester goes to..
61.69% 61.97% 61.20% 60.98%

28. We
Given the magnitude of the data
Urban Planning
Improving transport
infrastructure
Pattern discovery

29. We believe that US-Accidents provides a context for future
research on traffic accident analysis and prediction.
Conclusion
THE
END