Author:Â Sumit Upreti
Abstract:Â People have been paying greater attention to air quality in recent years since it has such a direct impact on people's health and day-to-day lives. In air pollution, weather and transportation conditions, the use of petroleum derivatives, and mechanical boundaries all play important roles. Successful air quality expectation has become one of the most hotly debated topics. Machine Learning is one way that might open up new possibilities for air pollution prediction. As a result, the objective behind this study and implementation project is to anticipate and classify air quality. Different categorization and prediction methods have been applied here. The objective is to construct a system that can categorise the processed data into defined classes later by applying data pre-processing such as data purification and feature selection. For the prediction of the next day's pollution, this system employed the Support Vector Machine and Nave Bayes. Based on fundamental characteristics such as Particulate matter (PM2), temperature, and pressure, the system assists in predicting future pollution details, assessing pollution details, and forecasting future pollution. As a result, willing to apply machine learning skills to the environmental area in order to create a pollution-free society.
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