Improving the accuracy of predicting congested traffic flow road transport using random forest algorithm and compared with the naives bayes algorithm using machine learning

Through a comparison of the Naive Bayes algorithm and the Random Forest approach, the purpose of this research is to improve the accuracy of the former in order to anticipate the amount of traffic congestion that would occur on roadways. In order to conduct an analysis of the flow of traffic along a route, the research endeavourutilised the Random Forest technique with ten samples and the Naive Bayes algorithm with ten samples. The traffic flow road transport dataset may be utilised for the purpose of determining the flow of traffic on roads by utilising only a few factors from the dataset, as well as for the purpose of making forecasts. During times of congestion, the dataset contains a number of elements that may be utilised to forecast the flow of traffic. These features include the date, the time, the junction, and the id. The results of the statistical analysis conducted by SPSS indicate that there is a noteworthy distinction (p=0.0001; p