Optimizing Feature Selection Process Through Variation Inflation Factor for Air Pollution Analysis

Air pollution, caused by various pollutants such as SO2, NO2, CO, and NH3, poses significant health risks. This research focuses on developing a machine learning-based system for air pollution monitoring and analysis. The system utilizes air quality monitoring stations to measure pollutant levels and calculates the Air Quality Index (AQI) to assess air quality. By comparing pollutant levels with permissible limits, the system can identify potential health risks and trigger necessary precautions. Feature selection techniques, such as Variation Inflation Factor and skewness, are employed to identify the most relevant features for accurate prediction. Weak learner algorithms, including Random Forest, Ada Boost, and Gradient Descent, are evaluated for their effectiveness in air pollution detection. The proposed system provides a valuable tool for monitoring air quality, identifying pollution hotspots, and informing public health interventions. By leveraging machine learning techniques, the system can enhance the accuracy and efficiency of air pollution analysis, contributing to improved air quality and public health.