In the previous notebook analysis, we identified several likely candidate features and variables that could be significant in predicting a cat's outcome as it enters the shelter. Using that information and scikit-learn, we can train a machine learning model to predict if a cat will be adopted or transferred to a partner facility. For this first task, we are only interested in the adoption and transfer outcomes to see if our assumptions based on experience and the information we learned from the previous analysis align with predicted results. Adoptions and transfers represent over 90% of all the outcomes in the Austin Animal Center shelter system, therefore focusing on these outcomes and their more specific subtype outcomes and building a model to predict these outcomes is still quite valuable.
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