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To predict the air pollution level in a particular region area using a K-Nearest Neighbor algorithm compared with the Support Vector Machine algorithm, The Novel K-Nearest Neighbor Algorithm and the Support Vector Machine Algorithm are two groupings. The algorithms were implemented and evaluated on a dataset of 32516 records. Various air pollution was identified through a programming experiment with N = 5 iterations for each method. G power is set at 80%. The confidence interval is 95%, and the threshold value is 0.05%. The G-power test is around 80% accurate. The K-Nearest Neighbor algorithm (97.44%) has better accuracy when compared with the Support Vector Machine (70.32%). The K-Nearest Neighbor algorithm has the highest accuracy compared to the Support Vector Machine algorithm. In the prediction of air pollution, K-Nearest Neighbor has better performance when compared to the Support Vector Machine Algorithm.

To predict the air pollution level in a particular region area using an Innovative Logistic Regression algorithm compared with the Naive Bayes algorithm. The Logistic Regression Algorithm and the Naive Bayes Algorithm are two sets of algorithms. The algorithms were implemented and evaluated on a dataset of 32516 records. Various air pollution was identified through a programming experiment with N=5 iterations for each method. The threshold value is 0.05%, and the Confidence Interval is 95%. The G-power test used is about 80%. The innovative Logistic Regression algorithm (98.26%) has better accuracy when compared with Naive Bayes(97.32%). Logistic Regression 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. Logistic Regression outperforms the Naive Bayes Algorithm in the prediction of air pollution.