Virtual networks are a crucial technology of the Internet of Things (IoT). They enable devices and sensors to interact with each other and with the cloud.The ability of IoT virtual networks to link numerous devices with minimum infrastructure is a big benefit. Our project demonstrates how to access sensor data from IoT devices using the IoT PnP app and MXCHIP through Microsoft Azure and Visual Studio Code (VS Code).Overall, we aimed to explore the potential of using accelerometer data from the MXCHIP Devkit to predict hand movement for the purpose of detecting writing. Through the collection and labelling of accelerometer data, we were able to train a decision tree classifier using sklearn library. The classifier was able to accurately distinguish between hand movement associated with writing and non-writing activities, achieving an accuracy score of 97% on the test dataset.