An analysis of data from the largest no-kill animal shelter in the United States unearthed interesting insights that could help shelters increase adoptions of dogs and cats.
Meet Ginger. Ginger is a cairn terrier with a sable tick coat. Ginger is only two months old and was recently fixed. Will you help Ginger find a loving home?
Regardless of whether or not Ginger is the right animal for you, chances are she will be adopted. A recent analysis conducted by Joanne Lin, a student in Thinkful’s data science bootcamp and a former GlobalGiving impact associate, concluded that dogs that meet Ginger’s description (name, coat color and pattern, and breed) are most “adoptable.”
Each year, approximately 6.5 million companion animals end up in shelters. Thanks to the great work by facilities like the Austin Animal Center, half of these animals (the majority of which are cats and dogs) are adopted. But there’s still a lot of work to do to help all animals find a loving home.
So what makes an animal more likely to be adopted? And how can shelters use this information to increase adoptions?
In support of National Adopt A Shelter Pet Day (observed on April 30), Thinkful leveraged the power of data science to understand and predict animal shelter outcomes.
To conduct her analysis, Joanne leveraged data made public by the Austin Animal Shelter. The Austin Animal Shelter houses more than 18,000 animals per year, making it the largest no-kill shelter in the country.
First, she first conducted an exploratory data analysis. This allowed her to better understand the data including the different variables or features that might impact adoption rates. She concluded that the data set required feature engineering to allow for a more nuanced analysis.
Feature engineering, a process that involves building new features into a data set, allows data scientists to process existing data in way that optimizes model performance.
After building out these new features, Joanne used two machine learning models to conduct her analysis. First, she used random forest classifier (explained here) to understand which features were most important to predicting adoption outcomes. Next, she verified her results using a more complicated model called a support vector machine (SVM). The support vector machine allowed Joanne to “draw a boundary” between animals that were adopted and animals that were not adopted along a multi-dimensional plane. Note: Joanne’s actual model included 15 dimensions!
In total, Joanne analyzed animal shelter outcomes for nearly 80,000 animals including approximately 44,000 dogs and 30,000 cats. Her analysis provides incredible new insights concerning adoptions including but not limited to:
Which features are most predictive of whether or not an animal gets adopted
Which animal names are the most desirable for adoptions
What days of the week result in the most adoptions
What are the most “adoptable” dogs and cats, respectively
The Ultimate Outcome
Joanne’s model allows us to understand which features matter most with respect to animal adoptions. While dogs and cats shared some features in common, whether or not an animal had a name was much more important for cat adoptions than dog adoptions. (Attention shelters: Name your cats!) Conversely, breed was much more important for dog adoptions than cat adoptions.
Illustration by Thinkful
Thanks to Joanne, we now know what cat and dog names are most common at shelters (at least in Austin) and which of those names are most desirable for adoptions.
Ginger was the most adoptable name for dogs. Cookie was the most adoptable name for cats.
We also know some adoption days are better than others. According to the model, animals are more likely to find forever homes on the weekend.
So which animals are most likely to be adopted? How old are they? What do they look like? According to data science, the most adoptable cat is a fixed, 2-month-old blue calico cat who is named Cookie. The most adoptable dog is a fixed, 2-month-old sable tick cairn terrier named Ginger.
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