Air Quality Prediction using Random Forest algorithm

As a result of lifestyle choices, industry, and growing urbanisation, pollution in the atmosphere has spread throughout the world and is now a serious threat to human existence. One of the air contaminants, particulate matter, is known to contribute to a variety of illnesses, including cardiovascular and respiratory diseases. To safeguard people from the negative impacts of air pollution in advance, accurate assessment of air pollution concentrations is required. A variety of variables influence air pollution, including weather conditions and the concentration of other contaminants in urban areas. The purpose is to diagnose machine learning-based solutions for predicting air pollution findings as accurately as possible. The dataset is analysed using the supervised machine learning approach (SMLT), which collects a wide range of data, including variable identification and results from univariate, bivariate, and multivariate analyses. We compare and evaluate the performance measures of several machine learning methods on the provided pollution dataset using evaluation approaches.