PREDICTING LENGTH OF STAY FOR DOGS AND CATS IN AN ANIMAL SHELTER

Authors

  • Ана Граховац Autor

DOI:

https://doi.org/10.24867/27BE24Grahovac

Keywords:

data science, machine learning, classification

Abstract

This paper explores the process of analyzing and processing data on adopted dogs and cats from an animal shelter. Various machine learning models were compared for the classification of animals based on the anticipated length of stay in the shelter.

References

[1] W.P. Brown, J.P. Davidson, M.E. Zuefle, „Effects of phenotypic characteristics on the length of stay of dogs at two no kill animal shelters“, Journal of Applied Animal Welfare Science, 16(1), 2-18, 2013.
[2] W.P. Brown, K.T. Morgan, „Age, breed designation, coat color, and coat pattern influenced the length of stay of cats at a no-kill shelter“. Journal of Applied Animal Welfare Science, 18(2), 169-180, 2015.
[3] J. Bradley, S. Rajendran, „Increasing adoption rates at animal shelters: A two-phase approach to predict length of stay and optimal shelter allocation“, BMC Veterinary Research, 17, 1-16, 2021.
[4] A. Zadeh, K. Combs, B. Burkey, J. Dop, K. Duffy, Nosoudi, „Pet analytics: Predicting adoption speed of pets from their online profiles“, Expert Systems with Applications, 204, 117596, 2022.
[5] https://www.kaggle.com/datasets/aaronschlegel/austin-animal-center-shelter-intakes-and-outcomes (приступљено у новембру 2023)
[6] https://www.kaggle.com/datasets/yonkotoshiro/dogs-breeds (приступљено у новембру 2023)
[7] https://towardsdatascience.com/ml-intro-5-one-hot-encoding-cyclic-representations-normalization-6f6e2f4ec001 (приступљено у новембру 2023)

Published

2024-06-06

Issue

Section

Electrotechnical and Computer Engineering