Air Quality Prediction and Analysis using Machine Learning

This project focuses on the prediction and analysis of air quality using machine learning techniques integrated with various environmental sensors. Sensors such as MQ2 and MQ135 (for gas detection), PMS5003 (for particulate matter), and DHT11 (for temperature and humidity) are employed to collect real-time air quality data. The collected data is transmitted to a machine learning model, which analyzes patterns and predicts air quality levels. If any abnormal or hazardous condition is detected, the system triggers an alert by activating a buzzer and sends a notification. Simultaneously, the processed data is sent to an Arduino for real-time response handling and displayed on the ThingSpeak platform for remote monitoring and analysis. This intelligent system ensures continuous air quality monitoring and proactive health and safety alerts.