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Domesticated Animal Image Classification with Python

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About this project

In this use case, my colleagues and I collaborated on creating and optimizing a convolutional neural network in order to identify domesticated animals versus non-domesticated animals to assist in funding suggestions for local animal shelters. Using the CIFAR-10 dataset with over 60,000 images, we filtered, cleaned, performed EDA, and optimized our model to achieve an 82% accuracy and 82% recall overall.