Harnessing Machine Learning for Accurate Air Quality Classification
Air pollution is a major global concern, affecting human health and the environment.
Machine learning (ML) offers a powerful alternative by analyzing historical air quality data to predict pollution levels in real time. By leveraging classification models,
By leveraging classification models, we can categorize air quality into different levels—such as Good, Moderate, and Unhealthy.
Traditional air quality monitoring relies on sensor networks, which can be expensive and require frequent maintenance.
Our machine learning model follows a structured approach, starting with data preprocessing, feature selection, and
model training using classification algorithms such as Random Forest, Support Vector Machines (SVM), and Neural Networks.
The dataset is split into training and testing sets to ensure the model generalizes well. Performance is evaluated using metrics like accuracy, precision, recall, and F1-score.