Machine Learning - Zoo Animal Classification
This website is the result of our final project for the University of Minnesota's Data Visualization Bootcamp. In which we were tasked with creating a machine learning model. The dataset we chose to examine was from the University of California Irvine's Machine Learning Repository and contains information on 101 different species of zoo animals. There are 16 different attributes, indicated by boolean values, that help determine which of the 7 different classes that these animals may fall under. Our team consists of Rachel Kerr, Eric Shaffer, Nick Hoyer, and Alex Zapuchlak.
Here is a link to the exact dataset we used to complete this machine learning model.
The images below show sample images of our dataset we used, in which you can get a better idea of how our machine learning models worked
• 101 different animals which had a specific list of attributes
• 7 different classes for the animals to be sorted into
•16 different attributes/features that helped determine which animals would go where
