Activity Classification & Prediction
This project runs a activity classification model (Random Forest/Decision Tree) on the cloud based on sensors deployed in a house (ARUBA DATASET) and it predicts based on that. The actual setup of the sensors are not done, so it’s a simulated sensor setup that randomly takes sensor values and according to timestamps and sends to the model to classify and hence predict the activity done at that time using those virtual sensor data. This project serves as a proof of concept that one can predict and monitor activity over the internet through sensors while being away for tasks such as elderly care monitoring