I’m now going to take you thru a mission involving Predictive Upkeep Advice Programs built-in with IoT (Web of Issues) to cut back unplanned downtimes.
The thought is to make the most of IoT sensor knowledge from industrial tools — after all, we’ll be working with fictitious knowledge, however it’s going to simulate what can be actual knowledge inside an organization.
We’ll use this knowledge to create a totally machine-learning-based suggestion system. Alongside the way in which, I’ll place a powerful emphasis on dealing with imbalanced knowledge.
I’ll introduce a minimum of 5 totally different strategies to you. We’ll create 5 mannequin variations. Ultimately, we are going to choose one of the best mannequin, justify our selection, check the mannequin, after which deploy it via an online software utilizing Streamlit.
So, we’ve got fairly a bit of labor forward. The hyperlink to the mission on my GitHub might be on the finish of this tutorial, together with the bibliography and reference hyperlinks so that you can seek the advice of if you want.
Let me know if this adjustment meets your expectations or if there’s the rest you’d like to switch!
