Advanced Gas Detection and Classification using MQ Series Sensors Integrated with Machine Learning and Deep Learning Techniques

The dataset focuses on gas detection using MQ series gas sensors in conjunction with machine learning and deep learning models. Data were collected using a setup of MQ gas sensors (MQ-135, MQ-5, MQ-6) connected to an Arduino UNO microcontroller. The sensors were simulated using Proteus software, allowing them to detect gases such as ammonia, carbon dioxide, benzene, natural gas, carbon monoxide, and liquefied petroleum gas (LPG). The data from these sensors were transmitted to a LabView GUI for visualization and storage, and the final dataset was saved in CSV format, containing time-series data with sensor readings and corresponding timestamps.The data collection spanned a 10-day period in March 2023, generating time-series data from six MQ series gas sensors, labeled as Gas1 to Gas6. Each row in the dataset represents a specific timestamp, along with the raw outputs of each sensor and their corresponding gas concentration values in parts per million (PPM). In total, the dataset includes 100,422 samples, each labeled with a "Class" indicator to show the presence or absence of specific gases. The data underwent preprocessing steps, including outlier removal and scaling, to ensure its accuracy and reliability for further analysis.