Air Quality Monitoring and Prediction System

This dataset was generated as part of an IoT-based Air Quality Monitoring and Prediction System designed to collect, analyze, and evaluate environmental parameters in real time. The system integrates multiple sensors with a microcontroller platform to measure air quality indicators such as particulate matter, gas concentration, temperature, and humidity. The collected data is transmitted to a cloud platform for storage and visualization, enabling continuous environmental monitoring.

The dataset contains time-stamped sensor readings recorded at regular intervals and reflects real-world atmospheric conditions from the deployment location. It is structured in CSV format for ease of use and compatibility with data analysis tools and machine learning frameworks. The data can be utilized for air quality assessment, trend analysis, anomaly detection, and predictive modeling.

This dataset supports the development and evaluation of machine learning models aimed at forecasting air quality levels. It is particularly useful for researchers working in environmental monitoring, smart city applications, IoT systems, and environmental data analytics. The dataset may also assist in academic projects, research experiments, and comparative performance studies of classification and regression algorithms.

By making this dataset publicly available, we aim to promote transparency, reproducibility, and further research in the domain of intelligent environmental monitoring systems.