For operational and environmental reasons, it is an important consideration to estimate the age of the abalones when they go to market. Determining an abalone's age involves counting the number of rings in a cross-section of the shell through a microscope. Since this method is somewhat cumbersome and complex, this project aims to help the farmers estimate the age of an abalone using its physical characteristics.
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Use machine learning to predict the age of an abalone based on its characteristics.
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