Application of Machine Learning for Air Quality Analysis

The main objective of this research work is to apply Machine Learning to gas sensors, to analyze air quality and transmit the data to a server through the Internet of Things (IoT). The Arduino MEGA 2560 was used, the Ethernet shield module and four gas sensors, MQ2, MQ5, MQ7 and MQ135, were placed; Machine Learning was designed with Artificial Neural Networks (ANN) and greater effectiveness was obtained using the backpropagation training algorithm. The CRISP-DM methodology was used, which contains seven stages, first the problem was identified, in the second stage the data understanding, the third data preparation, in the fourth the Machine Learning with artificial neural networks was designed, the fifth was the modeling, the sixth is the implementation of the model and the seventh is the validation by performing the most common gas tests, resulting in the recognition of the gases in its environment, obtaining a functional system. In conclusion, it was verified and confirmed that Machine Learning with Artificial Neural Networks and the use of the Backpropagation algorithm can automatically detect gases and show the Air Quality Analysis. This research is exclusive for those interested who are beginning their investigation into the world of Machine Learning.