Comparison of Machine Learning Models in Animal Adoption (Comparação de Modelos de Machine Learning na Adoção de Animais)
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| Formato: | Recurso digital |
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2025
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| _version_ | 1866902024880128000 |
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| author | Godoy, Enzo L. G. Pacheco, Erik S. Souza, Lucas M. Talarico, Américo Miranda, Marise |
| author_facet | Godoy, Enzo L. G. Pacheco, Erik S. Souza, Lucas M. Talarico, Américo Miranda, Marise |
| contents | <p><span lang="EN-US">This work explores the use</span><span lang="EN-US"> </span><span lang="EN-US">of artificial intelligence and</span><span lang="EN-US"> </span><span lang="EN-US">machine</span><span lang="EN-US"> </span><span lang="EN-US">learning</span><span lang="EN-US"> </span><span lang="EN-US">to optimize the</span><span lang="EN-US"> </span><span lang="EN-US">adoption </span><span lang="EN-US">of rescued animals. The methodology involved preprocessing the provided dataset, which included<span> </span>cleaning<span> </span>and<span> </span>transforming<span> </span>the<span> </span>variables<span> </span>to<span> </span>ensure<span> </span>that<span> </span>the<span> </span>models<span> </span>received<span> </span>relevant<span> </span>and structured data. After this step, several machine learning models, such as KNN, Decision Tree, <span>Random</span><span> </span><span>Forest,</span><span> </span><span>and</span><span> </span><span>Gradient</span><span> </span><span>Boosting,</span><span> </span><span>were</span><span> </span><span>executed</span><span> </span><span>to</span><span> </span><span>predict</span><span> </span><span>the</span><span> </span><span>adoption</span><span> </span><span>time</span><span> </span><span>of</span><span> </span><span>the</span><span> </span><span>animals. </span>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.</span></p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_15707701 |
| institution | Zenodo |
| language | |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Comparison of Machine Learning Models in Animal Adoption (Comparação de Modelos de Machine Learning na Adoção de Animais) Godoy, Enzo L. G. Pacheco, Erik S. Souza, Lucas M. Talarico, Américo Miranda, Marise Animal Adoption Artificial intelligence Machine Learning Resource Management Predictability Overcrowding Rescue Institutions Abandoned Animals Overcrowding in Rescue Institutions <p><span lang="EN-US">This work explores the use</span><span lang="EN-US"> </span><span lang="EN-US">of artificial intelligence and</span><span lang="EN-US"> </span><span lang="EN-US">machine</span><span lang="EN-US"> </span><span lang="EN-US">learning</span><span lang="EN-US"> </span><span lang="EN-US">to optimize the</span><span lang="EN-US"> </span><span lang="EN-US">adoption </span><span lang="EN-US">of rescued animals. The methodology involved preprocessing the provided dataset, which included<span> </span>cleaning<span> </span>and<span> </span>transforming<span> </span>the<span> </span>variables<span> </span>to<span> </span>ensure<span> </span>that<span> </span>the<span> </span>models<span> </span>received<span> </span>relevant<span> </span>and structured data. After this step, several machine learning models, such as KNN, Decision Tree, <span>Random</span><span> </span><span>Forest,</span><span> </span><span>and</span><span> </span><span>Gradient</span><span> </span><span>Boosting,</span><span> </span><span>were</span><span> </span><span>executed</span><span> </span><span>to</span><span> </span><span>predict</span><span> </span><span>the</span><span> </span><span>adoption</span><span> </span><span>time</span><span> </span><span>of</span><span> </span><span>the</span><span> </span><span>animals. </span>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.</span></p> |
| title | Comparison of Machine Learning Models in Animal Adoption (Comparação de Modelos de Machine Learning na Adoção de Animais) |
| topic | Animal Adoption Artificial intelligence Machine Learning Resource Management Predictability Overcrowding Rescue Institutions Abandoned Animals Overcrowding in Rescue Institutions |
| url | https://doi.org/10.5281/zenodo.15707701 |