• This project depicts various strategies utilized for forecast of Air Quality Index (AQI) utilizing supervised machine learning algorithms. I was able to generate predication with an accuracy of 95%.
• Compared six supervised machine learning algorithms such as logistic regression, support vector machine, random forest tree, Naıve Bayes theorem, etc.
• Used Tkinter framework to create a GUI that supports actions such as selecting cities, setting AQI level parameters, etc.
• Successfully defended my thesis in front of the judge and hence paper was published in International Journal of Psychosocial Rehabilitation (IJPR).