THE PREDICTION OF QUALITY OF THE AIR USING SUPERVISED LEARNING
In general, Pollution of the climate refers to the discharge of pollutants into the atmosphere that damage human health and the environment as a whole. It has the ability to be one of the most dangerous things humans have ever experienced. It damages livestock, crops, and forests, among other things. To avoid this issue in mostly urban areas, the popular approach such as machine learning techniques may be used to predict air quality from contaminants. As a consequence, the quality of the air assessment and forecasting has become an important field of study. The goal is to develop machine learning-based air quality forecasting techniques that are as accurate as possible. The supervised machine learning technique (SMLT) will be used to gather several pieces of information from the dataset, including variable recognition, uni-variate analysis, bi-variate and multi-variate analysis, missing value treatment and analysis, data cleaning/preparation, and data representation. Our findings provide a valuable guide to sensitivity analysis of model parameters in terms of success in air quality pollution prediction through accuracy measurement. By comparing supervise classification machine learning algorithms and generating prediction results in the form of best accuracy, create a machine learning-based method for accurately predicting the Air Quality Index value. Furthermore, to compare and discuss various machine learning algorithms in order to determine the most accurate algorithm with the performance of a GUI-based user interface for air quality prediction.