Air Quality Prediction using LSTM Networks

This project uses Long Short-Term Memory (LSTM) networks, a type of recurrent neural network, to predict air quality over time. The model is trained on sequential data such as past air quality records, weather data, and pollution levels. The system can predict short-term future air quality trends and help people avoid exposure to unhealthy air conditions.