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Marine Biometric Age Regression Data
Synthetic Biology & Genetic Engineering
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About
Predicting the age of Abalones using their physical characteristics. Abalones are economically valuable sea snails often farmed around the world, making physical measurements essential for determining their age. Historically, the age is determined by a boring and time-consuming traditional method: the shell is cut, stained, and the growth rings are counted under a microscope. This collection of data offers an opportunity to devise a Machine Learning model that predicts age efficiently by utilizing measurements that are easier to obtain, such as length and various weight attributes.
Columns
The dataset contains 10 columns detailing physical and biological attributes:
- length: Length measurement.
- diameter: Diameter measurement.
- height: Height measurement.
- whole-weight: The weight of the entire abalone specimen.
- shucked-weight: The weight of the meat after being shucked.
- viscera-weight: The weight of the viscera.
- shell-weight: The weight of the shell.
- sex_F: Sex, represented as a one-hot encoded field for Female.
- sex_I: Sex, represented as a one-hot encoded field for Infant.
- sex_M: Sex, represented as a one-hot encoded field for Male.
Distribution
The data file is usually in CSV format, and a sample file,
test_dataset.csv, is approximately 23.69 kB in size. The structure includes 10 columns and 627 total valid records. The data is exceptionally clean, showing 100% validity across all columns, with 0% mismatched or missing values. For instance, the 'length' feature ranges from a minimum of 28 to a maximum of 154, and 'whole-weight' ranges from 2.9 to 499.Usage
This collection of data is ideally suited for devising a Machine Learning model. The primary task is using regression techniques to help predict the age of abalones based on physical measurements. This allows for the development of alternative, non-destructive methods for marine biology research and optimisation of aquaculture practices. Solving the prediction problem may be enhanced by incorporating additional information, such as weather patterns or geographic location (which can affect food availability).
Coverage
The data covers physical measurements taken from 627 abalone specimens, detailing biometric parameters like length, height, diameter, and multiple weight categories. The demographic scope includes sex, classified using one-hot encoding for Female, Infant, and Male specimens. The expected update frequency for this data is Annually.
License
CC0: Public Domain
Who Can Use It
- Data Scientists and Machine Learning Practitioners: Focused on applying regression models, especially those using tools like
sklearn, to solve biological prediction challenges. - Beginner Analysts: Working with clean, tabular datasets to gain experience in feature engineering and model development.
- Aquaculture Researchers: Seeking faster, proxy methods for age determination, moving away from time-intensive manual inspection.
Dataset Name Suggestions
- Abalone Age Prediction Parameters
- Marine Biometric Age Regression Data
- Physical Measurements for Abalone Age Estimation
Attributes
Original Data Source: Marine Biometric Age Regression Data
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