Machine Learning-based Multiclass Classification Model for Effective Air Quality Prediction
Shilpi Rani, Priya Kumari, Sunil Kumar Singh
Abstract
According to the recent studies, statistics of the world health organization say that out of ten people, nine breathe unhealthy air that is not fit for human health. This is responsible for the death of over 7 million annually. Now, if we look in terms of air pollution in case of India, national quality standards of air is much low with compared to the guidelines given by WHO. In India, the concentration of ozone boosts noteworthy that is 17% during the last ten years. In this work, we have applied Support Vector Machine (SVM), K-Nearest Neighbor(KNN), Logistic Regression, Decision Tree, and Random Forest models for multiclass classification of machine learning to the data collected from Indore- MPPCB and Anand Vihar, Delhi –DPCC (2020-2022) that is available on Central Pollution Control Board, Ministry of Environment, Forest and Climate Change for AQI prediction. We compared the performance of these algorithms of machine learning using various performance metrics that is Accuracy, Precision, Recall, F1-Score, AUC ROC, Kappa Score, and MCC. However, we found that the Random Forest model is best suited for this work.