Abstract
The proposed system shows different models used for prediction of Air Quality Index (AQI) using various machine learning procedures. The system examines machine learning algorithm for air quality index by computing algorithm accuracy which will bring about the best precision. Moreover, the system shows various machine learning accuracy figures from the dataset values with assessment check report which recognize the proximity index. The results show the importance of ML suggested evaluation techniques which can be contrasted and best appropriateness with accuracy, realness and F1 Score. The air pollution datasets contain data for every state (zone) of India. Several machine learning algorithms like logistic regression, decision tree, support vector machine (SVM), random forest tree, Naïve Bayes theorem and K-nearest neighbor (KNN) are all parallels compared and evaluated.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Innovative Research in Advanced Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
