Comparison of Machine Learning Models in Animal Adoption (Comparação de Modelos de Machine Learning na Adoção de Animais)

This work explores the use of artificial intelligence and machine learning to optimize the adoption of rescued animals. The methodology involved preprocessing the provided dataset, which included cleaning and transforming the variables to ensure that the models received relevant and structured data. After this step, several machine learning models, such as KNN, Decision Tree, Random Forest, and Gradient Boosting, were executed to predict the adoption time of the animals. The performance of each model was evaluated, and their metrics such as precision, recall, and others were compared in order to identify the most efficient approach. It is expected that the results will help rescue organizations allocate resources more intelligently and reduce overcrowding by prioritizing animals with a higher chance of quick adoption.