This is an example of models that I built on a fish dataset for weight prediction.
Look at the data set on Kaggle!
I recently read an article about two men who were caught cheating during a fishing tournament. Being an avid fisherman myself, I couldn't help but wonder if there could have been a way to detect the cheating with an algorithm.
When catching a fish, one may want to record the weight along with several other measurements. I can imagine visually inspecting a fish, and provide a best guess as to how much that fish would weigh. If I saw a rather small perch or walleye, and grabbed it and thought "wow this is heavier than I thought," then I may want to investigate further. And this is exactly how the two fishermen were caught! The fishing tournament organizer looked at the rather normal looking fish and thought they couldn't possibly weigh that much. If a human can come to that conclusion with their own intuition, what could a machine learning model do? Perhaps it could prevent things like this from happening...
This got me thinking and I scoured the internet for a relatable data set. Unfortunately, there aren't that many freely available data sets on fishing tournaments and fish weights, so I settled for getting creative with the fish market data set from Kaggle.
This analysis is just a first pass at a simple model, and of course one could always take it a step further. Feel free to let me know of any inconsistencies or questionable moments in the code, or if you may have any suggestions. I am always eager to learn and improve! ๐
To do:
- Add confidence intervals
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