Predictive modeling of PM2.5: Unraveling environmental influences and enhancing accuracy through ensemble methods Available to Purchase

In the face of escalating environmental concerns, analyzing, particulate particles with a minimum diameter of 2.5 micrometers, is crucial for understanding air quality dynamics. PM2.5 analysis provides vital insights into air pollutant composition, sources, and potential implications for environmental factors along with one’s health. Leveraging Python, proposed project conducts an comprehensive analysis and predictive modeling of air quality in Dingling city, Beijing, from March 2013 to February 2017. The dataset undergoes meticulous preprocessing and Exploratory Data Analysis to uncover patterns and relationships. Objectives include investigating PM2.5 concentration patterns and understanding environmental factor impacts. For predictive modeling with grid search hyperparameter optimization, ensemble Gradient Boosting Regressor, Random Forest Regressor, and Decision Tree Regressor are utilized. Gradient Boosting Regressor outperforms grid search optimization with an accuracy of 95.67%, according to metrics for evaluating models like Accuracy and Mean Squared Error (MSE). Python guarantees a solid implementation, adding significant knowledge to studies and forecasts of air quality.