Abstract: The emission of contaminants is referred to as air pollution. It harms forests, crops, animals, and other things. To avoid this issue,air quality must be predicted from pollutants and preventive measures should be taken. By foreseeing outcomes with the highest degree of accuracy, the goal is toInvestigate machine learning-based air quality forecasting solutions. The model offers a Model parameter sensitivity analysis manual with performance in terms of accuracy. The accuracy of the Support Vector Machine model is the lowest, while that of The maximum is the Gaussian Naive Bayes model. Using established performance characteristics, these models' performances are compared and evaluated. The XGBoost model surpassed the competition and achieved the strongest linearity between the predicted data and the actual data.
Keywords:Random Forest Classifier
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