Analysis and Comparison of Air Quality Index Prediction using Regression Based Machine Learning Models

In this research work, multiple machine learning regression techniques were used to predict the pollution and offer a comparative study to establish the optimum model for reliably predicting air quality in terms of data quantity and processing time. The Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) were used as evaluation measures to compare these regression models. Furthermore, the processing time of each algorithm was determined via standalone learning and hyper-parameter tweaking to produce the best-fit model in terms of computational time and error rate. In this paper, we have calculated the custom score which is sum of MAE, RMSE, MAPE and time processing values. The best model obtained is the custom stacked regression model has custom score of 111.41 which is very less as compared to other regression models.