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Temperature and CO2 Room Status Data
Data Science and Analytics
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About
This resource provides experimental data specifically gathered for binary classification, focusing on predicting whether a room is occupied or not. It captures crucial environmental factors—such as temperature, light, and carbon dioxide levels—which serve as inputs for the predictive model. The dataset is highly relevant for machine learning projects aimed at understanding feature insights and developing algorithms for real-time occupancy detection in automated or smart building systems.
Columns
The data includes five measured features and one target variable:
- Temperature: The ambient temperature measured in degrees Celsius.
- Humidity: Relative Humidity expressed as a percentage.
- Light: The light intensity within the room, measured in Lux.
- Carbon dioxide (CO2): The concentration of carbon dioxide measured in parts per million (ppm).
- HumidityRatio: A derived physical quantity calculated from both temperature and relative humidity, presented in kg water-vapor per kg-air.
- Occupancy: The target variable, indicating occupancy status (1 for occupied, 0 for not occupied).
Distribution
This dataset typically arrives in a standard data file format, such as CSV. The file size is approximately 135.86 kB. Structurally, it contains 6 columns and is composed of 2,665 total records. The quality of the data is high, with no missing or mismatched values identified across the records. The expected schedule for refreshing this type of data is monthly.
Usage
This data product is suited for several key analytical applications:
💡 Applying various Exploratory Data Analysis (EDA) methods to extract underlying patterns and insight information from the environmental features.
💡 Training, testing, and validating algorithms designed for binary classification to accurately predict room occupancy status.
💡 Developing predictive models for use in Internet of Things (IoT) solutions or building automation systems focused on energy optimisation.
Educational and research purposes studying how environmental metrics correlate with human presence.
Coverage
The scope covers physical measurements of indoor environmental conditions. While specific temporal or geographic boundaries are not detailed, the data captures key environmental variables necessary for determining room status. All 2,665 records maintain 100% validity across all defined features.
License
CC0: Public Domain
Who Can Use It
The primary audience consists of professionals and researchers involved in data analysis and development:
- Data Scientists and Machine Learning Practitioners: Individuals needing validated, structured data for developing and benchmarking predictive room occupancy models.
- Researchers: Academics focused on sensor data analysis, feature engineering, and binary outcome prediction in environmental sciences.
- Developers: Those building applications related to smart homes, smart offices, or other systems tagged under the ‘Internet’ category.
Dataset Name Suggestions
- Room Occupancy Environmental Predictors
- Sensor Data for Indoor Status Classification
- Building Occupancy Prediction Metrics
- Temperature and CO2 Room Status Data
Attributes
Original Data Source: Temperature and CO2 Room Status Data
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