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Synthetic Smoking and Health Relation Dataset
Clinical Trials & Research
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£79.99
About
This synthetic smoking health dataset has been generated as an educational resource for data science, machine learning, and data analysis applications in healthcare. The data focuses on key health metrics, such as heart rate, smoking status, and cholesterol levels, which are important for monitoring individual health. This dataset is designed to help users practice data manipulation, analysis, and predictive modelling.
Dataset Features:
- Age: Age of the individual (in years).
- Gender: Gender of the individual (e.g., "female," "male").
- Smoker_Status: Smoking status, categorized as "yes" or "no," indicating whether the individual smokes.
- Heart_Rate: Resting heart rate in beats per minute, an indicator of cardiovascular health.
- Cigarettes_Per_Day: Number of cigarettes smoked per day (only relevant for smokers).
- Cholesterol_Level: Cholesterol level of the individual (measured in mg/dL), an important indicator for cardiovascular health.
Distribution:
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Usage:
This dataset is useful for a variety of applications, including:
Healthcare Research: To explore relationships between lifestyle factors (such as smoking and cholesterol levels) and heart rate.
Educational Training: To practice data cleaning, transformation, and visualization techniques specific to healthcare data.
Predictive Modeling: To develop models that predict health risks or outcomes based on various health indicators like cholesterol levels and smoking status.
Correlation Heatmap of Numerical Variables:
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Coverage:
This dataset is synthetic and anonymized, making it a safe tool for experimentation and learning without compromising real patient privacy.
License:
CCO (Public Domain)
Who can use it:
- Researchers and educators: For academic studies or teaching purposes in healthcare analytics and data science.
- Data science enthusiasts: For learning, practising, and applying healthcare data manipulation and analysis techniques.
- Healthcare professionals: For analyzing and predicting risk factors in health, particularly related to cardiovascular conditions.
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