Prediction of Air Pollution Using Machine Learning Models

Air pollution has become one of the most critical environmental challenges worldwide, signifi-cantly impacting human health, climate change, and overall ecological balance. Rapid industriali-zation, urbanization, and increased vehicular emissions have led to a drastic rise in air pollutants such as particulate matter (PM2.5 and PM10), carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃). Accurate prediction of air pollution levels is essential for implementing effective control measures, improving public awareness, and supporting policy-making decisions. This project focuses on the development of a machine learning-based predic-tive system to forecast air pollution levels using historical and real-time environmental data. The proposed system utilizes various machine learning algorithms such as Linear Regression, Deci-sion Tree, Random Forest, Support Vector Machine (SVM), and Artificial Neural Networks (ANN) to analyze patterns and relationships among different pollution indicators and meteorolog-ical parameters including temperature, humidity, wind speed, and atmospheric pressure. The dataset used in this study is collected from reliable sources such as government pollution control boards and environmental monitoring agencies. Data preprocessing techniques such as handling missing values, normalization, and feature selection are applied to improve model performance and accuracy. Exploratory Data Analysis (EDA) is conducted to identify trends, seasonal varia-tions, and correlations between pollutants and weather conditions.