Comparison of Machine Learning Algorithms for Shelter Animal Classification

Establishing characteristics of the shelter animal which determine its outcome is an important task for solving the problem of homeless and abused animals. The main goal of this research was to identify which machine learning algorithm can provide the most accurate prediction of the outcome for an animal, based on its main features. The first step in this research was the transformation of data into a proper form for the implementation of the algorithms. Furthermore, several machine learning algorithms were trained in order to achieve the best possible classification results. The results of the algorithms were compared and the most suitable algorithms were selected based on their performance metrics. This research proposes using a combination of multiple data preprocessing techniques, imbalanced data and machine learning algorithms for predicting the outcome for shelter animal based on its characteristics. K-Nearest Neighbors and C4.5 algorithms provided the best classification results in this research.