AI-Based Air Quality Prediction System

Air Quality Prediction is an AI and Machine Learning-based project designed to analyze air pollution data and predict the Air Quality Index (AQI). The system uses historical environmental data and machine learning algorithms to identify pollution patterns and estimate air quality levels based on relevant environmental parameters.

The project involves data collection, data preprocessing, exploratory data analysis (EDA), feature engineering, model training, and performance evaluation. Various machine learning algorithms, such as Linear Regression, Decision Tree, Random Forest, and Gradient Boosting, can be implemented and compared to identify the most suitable model for prediction.

The proposed system aims to provide AQI predictions, classify air quality into different categories, and present pollution trends through interactive graphs and visualizations. A user-friendly web application can also be developed to display prediction results and provide general health recommendations based on predicted pollution levels.

The primary goal of this project is to demonstrate how Artificial Intelligence and Machine Learning can support environmental monitoring, improve awareness of air pollution, and assist individuals and authorities in making informed decisions. The project can be further enhanced through real-time data integration, IoT sensors, weather information, and advanced deep learning techniques.