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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. [ABSTRACT FROM AUTHOR]

The primary purpose of this investigation is to compare and contrast the performance of two machine learning algorithms, namely the Lasso regression Algorithm and the Support Vector Machine Algorithm, in the context of accident prediction. Over the course of 20 iterations, utilising N=10 samples, the Support Vector Machine approach, and Lasso regression, the accuracy of accident predicting was tested. A substantial gap (84.28 percent) separates the performance of the Support Vector Machine method from that of the Lasso regression methodology (94.56 percent). A significance level of 0.001 has been assigned to the new SVM classifier (p0.05 independent sample test). Comparisons of data showed that the Support Vector Machine Novel classifier was more accurate than the Lasso regression in predicting the occurrence of traffic accidents. [ABSTRACT FROM AUTHOR]