|Adoption||Died||Euthanasia||Return to owner||Transfer to outside org|
|65.4% chance||0.0% chance||0.3% chance||25.1% chance||9.3% chance|
This machine learning model predicts outcomes for animals delivered to a municipal shelter based upon information collected at intake and when they leave.
The concept and data for this project comes from a Kaggle competition. The original data source is the Austin Animal Center.
The highest scoring (i.e., lowest log-loss) model uses a cross-validated gradient tree boosting classifier.
Features used include: age upon outcome, gender upon outcome, animal type (dog or cat), named or unnamed, date and time of intake, purebred or mix.