steam-bell-92/Intel_Sensors
An Project consisting both—Supervised Learning (Random Forest Classifier) & Unsupervised Learning (K-Means Clustering) which predicts whether the room is occupied or not by help of sensors
This project helps operations managers or facilities staff predict whether a room is occupied using temperature, humidity, light, and voltage sensor data. It takes raw sensor readings as input and outputs a prediction of occupancy, specifically prioritizing the detection of actual presence. This is useful for anyone managing physical spaces where knowing occupancy is critical.
Use this if you need a system that prioritizes detecting every instance of occupancy, even if it means getting a few false alarms, especially in safety-critical applications.
Not ideal if your primary goal is perfect accuracy and you cannot tolerate any false positives, as this system is optimized to avoid missing actual occupancy events.
Stars
8
Forks
—
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jan 15, 2026
Commits (30d)
0
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