Air Predictive Modeling for Air Quality: A Comparative Study of Machine Learning and Deep Learning Techniques

Air quality emerges as a critical global concern affecting millions of individuals. This paper explores into the development and application of data-driven models for air quality prediction, Motivated by air quality's profound impact on both the environment and human well-being, we leverage deep learning and machine learning methodologies to enhance the precision of air quality forecasts. Dataset used comprising over 33,360 entries. This dataset encompasses diverse characteristics, including PM2.5, PM10, SO2, NO2, CO, O3, etc., with pollutant levels documented on an hourly basis. To gauge air quality levels, we employ various models such as the Random Forest model, Long Short-Term Memory (LSTM), Artificial Neural Network (ANN), Support Vector Machine (SVM), and K-Nearest Neighbors (K-NN). A thorough evaluation and performance comparison of these models is conducted using Kaggle's air quality dataset. The study seeks to not only address the immediate challenge of air quality prediction but also holds implications for broader environmental and public health considerations on a global scale.