By correctly estimating the number of occupants in a room, energy use can be controlled, resulting in cost minimization. In this project, we applied machine learning in sensor readings to estimate the number of occupants. We extracted several features that are related to occupancy estimation. Then we applied five machine learning algorithms. The performance of the classifiers was evaluated using accuracy, precision, recall, and F1-score. The full project details can be found here.

1. K-Nearest Neighbors (KNN)
2. Support Vector Machine (SVM)
3. Logistic Regression
4. Decision Tree
5. Naïve Bayes

We have used the room occupancy estimation dataset from the UCI machine learning repository.

1. Monjure Mowla Abir
   
2. Kaji Fuad Bin Akhter