Predictive Modeling of Air Quality Levels Using Decision Tree Classification: Insights from Environmental and Demographic Factors
Abstract
Air pollution poses a significant global challenge, adversely impacting public health and environmental sustainability. Understanding the factors influencing air quality is essential for developing effective mitigation strategies. This study aims to analyse key environmental and demographic factors, such as PM2.5 concentration, population density, and proximity to industrial areas, to predict air quality levels using a Decision Tree model. The dataset, comprising 5000 samples, was pre-processed by encoding the target variable and applying Z-score normalization to numerical features. The model was trained on 80% of the data and evaluated on the remaining 20%, achieving an accuracy of 93%. Evaluation metrics, including a classification report and confusion matrix, demonstrated the model's effectiveness in distinguishing between four air quality categories: Good, Moderate, Poor, and Hazardous. PM2.5 emerged as the most critical predictor, followed by demographic and industrial factors. These findings underscore the potential of machine learning models in providing actionable insights for air quality management. The results contribute to public policy by highlighting the need for targeted interventions in high-risk areas and the importance of incorporating environmental data into urban planning. Future work should focus on expanding the feature set and exploring ensemble techniques to further enhance predictive accuracy and robustness.
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