Prediction Analysis using Random Forest Algorithms to Forecast the Air Pollution Level in a Particular Location

To forecast the degree of air pollution in a specific area of a region using techniques Innovative Random Forest against Naive Bayes. Two groups of algorithms are Random Forest and Naive Bayes. The technique was developed and tested on a 32516-record dataset. In a programming experiment, each approach was iterated N=5 times to identify different levels of air pollution. The threshold value is 0.05 percent, and the confidence interval is 95%. The G-power test is around 80% effective. When compared to Naive Bayes, the innovative Random Forest method (98.26%) offers higher accuracy (97.32%).Random forest has the highest accuracy in comparison to the Naive Bayes algorithm. Significance value for accuracy is 0.056(p>0.05), Precision 0.02(p<0.05) and recall 0.01(p<0.05) based on 2-tail analysis. Conclusion: Random Forest has improved performance when compared to Naive Bayes in the forecast of pollution in air.