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Fitness Activity Calories
Data Science and Analytics
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About
This dataset is designed for developing machine learning models to predict the number of calories a person has burnt during a workout. It includes various biological measures and workout-related features, making it suitable for regression tasks in fitness and nutrition analysis. The dataset provides key variables to understand the relationship between physical attributes, exercise intensity, and calorie expenditure, aiding in the development of predictive analytics tools.
Columns
- User_Id: A unique identifier for each user. The range of IDs observed is from approximately 10,001,159 to 19,999,647.
- Gender: Specifies the user's gender, with an equal distribution of female and male entries.
- Age: Represents the user's age, ranging from 20 to 79 years, with a mean of 42.8 years.
- Height: Indicates the user's height in centimetres (cms), varying from 123 cms to 222 cms, with an average of 174 cms.
- Weight: Records the user's weight in kilograms (kgs), spanning from 36 kgs to 132 kgs, with a mean of 75 kgs.
- Duration: Denotes the duration of the workout, ranging from 1 to 30 units (likely minutes), with an average duration of 15.5.
- Heart_rate: Represents the user's heart rate during the workout, with values from 67 to 128, and an average heart rate of 95.5.
- Body_temp: Records the user's body temperature, ranging from 37.1 to 41.5 units (likely degrees Celsius), with a mean of 40.
- Calories (Target): The target variable, representing the number of calories burnt, with values from 1 to 314, and an average of 89.5.
Distribution
The dataset is provided in a CSV file format, named
calories.csv, with a file size of 742.29 kB. It consists of 9 columns and approximately 15,000 valid records across all features. There are no mismatched or missing values reported for any of the columns, indicating a clean dataset. The gender distribution is evenly split, with 50% female and 50% male entries.Usage
This dataset is ideal for:
- Developing machine learning models to predict calorie burn.
- Training regression models such as Random Forest and XGBoost.
- Conducting data analysis in the fields of nutrition and fitness.
- Researching the impact of biological measures on calorie expenditure.
- Building applications that estimate workout calorie burn.
Coverage
The dataset covers a diverse demographic range based on the included features:
- Age: Individuals from 20 to 79 years.
- Gender: An equal representation of female and male participants.
- Height: Ranging from 123 cms to 222 cms.
- Weight: Ranging from 36 kgs to 132 kgs.
- Workout Duration: Workouts ranging from 1 to 30 units.
- Heart Rate: Readings from 67 to 128.
- Body Temperature: Readings from 37.1 to 41.5. There is no specific geographic or time range information provided.
License
CC0: Public Domain
Who Can Use It
- Machine Learning Engineers/Data Scientists: For building and training predictive models.
- Fitness App Developers: To integrate calorie prediction features into their applications.
- Nutritionists and Fitness Coaches: For understanding factors influencing calorie expenditure.
- Researchers: For studies on exercise physiology and metabolic rates.
- Students and Beginners in ML: As a practical dataset for learning regression techniques in Python.
Dataset Name Suggestions
- Workout Calorie Predictor
- Human Calorie Burn Data
- Fitness Activity Calories
- Bio-Metrics Calorie Prediction
Attributes
Original Data Source: Fitness Activity Calories
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