Machine Learning Model for Air Quality Prediction

Developed a predictive machine learning model designed to forecast Air Quality Index (AQI) levels based on environmental and meteorological data. The project involved extensive data preprocessing, feature engineering, and the implementation of regression algorithms to ensure high prediction accuracy. This model can be utilized to monitor pollution trends and provide early warnings for hazardous air conditions