Air Quality Prediction and Analysis using Machine Learning

ABSTRACT: This paper presents an advanced Air Quality Prediction and Analysis using Machine Learning technologies to detect and respond to hazardous environmental conditions. The system incorporates an MQ135 sensor for CO2 readings, an MQ2 sensor for CO readings, and a DHT11 sensor for temperature and humidity measurements. These sensors continuously monitor the air quality and transmit data to a machine learning model via serial communication. The model, trained on relevant datasets, predicts the presence of dangerous gas levels and abnormal temperature conditions. When the system detects elevated CO2 or CO levels, it triggers multiple alerts: a buzzer sounds, an LCD displays "Gases Detected," and an SMS notification is sent via a GSM module. Similarly, if the temperature exceeds a predefined threshold, the system activates the buzzer, displays "Abnormal Temperature Detected" on the LCD, and sends an SMS alert. Additionally, all sensor data is uploaded to the ThingSpeak IoT platform for real-time monitoring and historical analysis.