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Abalone Physical Characteristics and Age Data
Data Science and Analytics
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About
Data explaining the physical characteristics of abalone intended for use in estimating their age. Determining the age of an abalone traditionally involves the challenging and complex process of counting the rings visible in a cross-section of its shell under a microscope. This dataset was created to help seafood farmers or researchers develop models, such as those utilizing machine learning or deep learning, to estimate age quickly and efficiently using physical traits like length, diameter, height, and various weight measurements. The resulting age is derived by adding 1.5 to the ring count.
Columns
- sex: A nominal category indicating whether the abalone is Male (M), Female (F), or Infant (I).
- length: A continuous measurement detailing the longest shell dimension, measured in millimeters.
- diameter: A continuous measurement taken perpendicular to the length, measured in millimeters.
- height: A continuous measurement of the shell, taken while the meat is still inside, measured in millimeters.
- whole_wt: A continuous variable representing the weight of the entire abalone.
- shucked_wt: A continuous variable detailing the weight of the abalone meat.
- viscera_wt: A continuous variable for the gut-weight (or viscera weight).
- shell_wt: A continuous variable representing the weight of the dried shell (after being dried).
- rings: A continuous variable indicating the number of rings found in a shell cross-section.
- age: An integer variable representing the estimated age of the abalone, calculated as the number of rings plus 1.5.
Distribution
The data is structured in a tabular format and contains 10 columns of mixed data types. There are 4177 valid records available across all fields. The distribution of the
sex variable shows that Males (M) account for 37% of the records, while Infants (I) account for 32%. The mean age is approximately 11.4 years.Usage
This data product is highly suitable for building predictive models. It is ideal for machine learning and deep learning applications where the goal is to predict age based on physical characteristics. Specific analytical uses include exploratory data analysis, investigating how weight changes correlate with age across different sex categories, and applying linear regression techniques to determine which physical variables are the strongest predictors of abalone age.
Coverage
The dataset covers abalone characteristics across three distinct demographic groups: Male, Female, and Infant. The measurements include physical dimensions (length, diameter, height) and various weights (whole, shucked, viscera, shell). Specific geographical or temporal details regarding the sampling period are not detailed; however, the context suggests measurements obtained from a seafood farming environment.
License
CC0: Public Domain
Who Can Use It
- Data Scientists: For developing and tuning regression models aimed at predicting biological age.
- Aquaculture Managers: Interested in implementing faster, non-destructive methods for monitoring the growth and age structure of their stock.
- Academic Researchers: Focused on studies concerning marine organism growth, maturation, and life science metrics.
- Students: Learning techniques in exploratory data analysis and linear regression modeling.
Dataset Name Suggestions
- Abalone Physical Characteristics and Age Data
- Predicting Abalone Age Using Biometrics
- Seafood Biometric Dataset
Attributes
Original Data Source: Abalone Physical Characteristics and Age Data
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