—In the populated and developing countries, governments consider the regulation of air as a major task. The meteorological and traffic factors, burning of fossil fuels, industrial parameters such as power plant emissions play significant roles in air pollution. Among all the particulate matter that determine the quality of the air, Particulate matter (PM 2.5) needs more attention. When it’s level is high in the air, it causes serious issues on people’s health. Hence, controlling it by constantly keeping a check on its level in the air is important. In this paper, Logistic regression is employed to detect whether a data sample is either polluted or not polluted. Autoregression is employed to predict future values of PM2.5 based on the previous PM2.5 readings. Knowledge of level of PM2.5 in nearing years, month or week, enables us to reduce its level to lesser than the harmful range. This system attempts to predict PM2.5 level and detect air quality based on a data set consisting of daily atmospheric conditions in a specific city.
1. Support Vector Machine
2. Random Forest
3. Neural Network
4. Logistic Regression
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