Room Occupancy Detection
Project Overview
The Occupancy Prediction project aims to predict whether a room is occupied or not based on the data collected from various sensors. The dataset used in this project is obtained from the UCI Machine Learning Repository and consists of 7 attributes, namely date, temperature, humidity, light, CO2, humidity ratio, and occupancy.
About the Dataset:
The dataset contains experimental data used for binary classification of room occupancy in an office room. The key features used for prediction are Temperature, Humidity, Light, and CO2. The ground-truth occupancy labels were obtained from time-stamped pictures taken every minute.
Data Dictionary
| Column Position | Atrribute Name | Definition | Data Type | Example | % Null Ratios |
|---|---|---|---|---|---|
| 1 | Date | Date & time in year-month-day hour:minute:second format | Qualitative | 2/4/2015 17:57, 2/4/2015 17:55, 2/4/2015 18:06 | 0 |
| 2 | Temperature | Temperature in degree Celcius | Quantitative | 23.150, 23.075, 22.890 | 0 |
| 3 | Humidity | Relative humidity in percentage | Quantitative | 27.272000, 27.200000, 27.390000 | 0 |
| 4 | Light | Illuminance measurement in unit Lux | Quantitative | 426.0, 419.0, 0.0 | 0 |
| 5 | CO2 | CO2 in parts per million (ppm) | Quantitative | 489.666667, 495.500000, 534.500000 | 0 |
| 6 | HumidityRatio | Humadity ratio: Derived quantity from temperature and relative humidity, in kgwater-vapor/kg-air | Quantitative | 0.004986, 0.005088, 0.005203 | 0 |
| 7 | Occupancy | Occupied or not: 1 for occupied and 0 for not occupied | Quantitative | 1, 0 | 0 |
Impact
The Occupancy Prediction project has the potential to revolutionize various domains, including energy efficiency, resource allocation, safety, and IoT applications. By accurately predicting room occupancy based on sensor data, it can enable smart building automation, optimize resource usage, enhance security measures, and promote sustainable practices. Additionally, it offers valuable insights into occupancy patterns, empowering researchers and policymakers to make informed decisions for a more efficient and comfortable living and working environment.
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