This project demonstrates an IoT-based air quality monitoring system that integrates real-time sensor data with Google Cloud Platform (GCP) for processing, storage, and visualization. The data is analyzed using a Random Forest Classifier to predict air quality levels (Good, Moderate, Poor).
Real-Time Data Streaming: Using MQTT protocol for sensor data transmission. Data Storage and Analysis: Leveraging GCP BigQuery for data storage and preprocessing. Machine Learning: Training and deploying a Random Forest Classifier for air quality prediction. Visualization: Graphical representation of air quality distribution.
pubs.py Publishes sensor data to an MQTT broker from a dataset stored in Google Cloud Storage (GCS) where data sensor simulation takes place.
subs_data.py Subscribes to MQTT messages, processes the data, and inserts it into BigQuery for analysis and storage.
steps1_E.py Handles data loading from BigQuery, machine learning model training, predictions, and air quality visualization.`