Air Quality Classification Using PCA and Random Forest

Developed a machine learning model to classify air quality based on environmental data. The project involved data preprocessing and dimensionality reduction using Principal Component Analysis (PCA), followed by training a Random Forest classifier to predict air quality categories and evaluate the model's performance.