Early prediction of road accidents using RMSE by comparing random forest algorithm with naive bayes algorithm

• Published in: AIP conference proceedings Vol. 2853; no. 1
• Main Authors: Prakash, T. Deva, Nagaraju, V.
• Format: Journal Article Conference Proceeding
• Language: English
• Published: Melville American Institute of Physics 07.05.2024
• Subjects: Algorithms; Machine learning; Roads & highways; Traffic accidents
• ISSN: 0094-243X; 1551-7616
• DOI: 10.1063/5.0197587

The fundamental objective of this research is to investigate several approaches that may be used to enhance the precision of recent machine learning algorithms that are used to anticipate road accidents, in particular the Random Forest Algorithm and the Naive Bayes Algorithm. Both the Materials and the Procedures are: Both the Random Forest and the Naive Bayes algorithms were run through 20 iterations in order to see how well they could predict future traffic incidents. The results show that in terms of accuracy, the Random Forest approach performs better than the Naive Bayes algorithm (89.95 percent). The significant independent samples T-test for the Random Forest approach is 0.001, as stated in the previous sentence (p0.05). When the data were compared, it was evident that the Random Forest approach surpassed the Naive Bayes algorithm in terms of the prediction of traffic accidents. This was the conclusion reached after analysing the data.