Using Data to Get Cats Adopted on

Posted on Dec 2, 2021

The skills the authors demonstrated here can be learned through taking Data Science with Machine Learning bootcamp with NYC Data Science Academy.

Background is a website that unites rescue groups and shelters with people looking to adopt animals. This analysis focuses on cats. I adopted two cats myself from a group on the site.

Rescue groups and shelters put a lot of time, money, effort and heart into caring for cats and finding them good homes. Some rescue groups trap, neuter and release feral cats usually from cat colonies. In New York City, when a feral cat is fixed, the vet tips the cat's ear and animal control is supposed to leave them alone.  A cat colony is just a bunch of cats living together.  The rescue groups try to find homes for the friendly cats as not all the cats in a colony are feral. Also, when a feral cat has kittens, if the kittens are socialized by human beings, they can be put up for adoption.

Other groups rescue animals from the city pound preventing euthanasia from overpopulation.

These organizations have limited resources.  For many is their sole means of getting their cats adopted.


The goal of this analysis is to provide rescue groups and shelters with information that they can use to better "market" their cats and improve adoption rates.

Factors know to influence adoption rate.

  • Once cats reach a certain age it is much more difficult to find them homes.
  • Kittens are typically put up for adoption at eight weeks of age. The younger they are the greater the demand.
  • Cats grow very fast and are usually full grown at one years of age.
  • The data was collected in September which is in kitten season. Kitten season is the time between April and October when most cats are born.

The analysis focuses on time listed on The assumption is that cats listed on the site less than thirty days are more likely to get adopted.  People looking to adopt cats can use time on the site as a search criteria.


Using Selenium, data for cats within twenty-five miles of New York City was scraped.  The site was scraped with six different search criteria reflected in the table below.

The cat name and associated rescue group or shelter was used to uniquely identify each cat.  Special needs cats were excluded because they would need to be analyzed separately and there isn't enough data to do that.


The analysis was conducted in two stages.  First duplicate listings were analyzed.  After that, duplicates were removed and cats on the site less than thirty days were compared with those on the site for more than thirty days.

Duplicate listings of cats by rescue groups and shelters

A lot of cats are relisted.  Most of the time, it's just twice

Most duplicate listings fall into the less than thirty days category.

It seems that most  groups are relisting cats when they hit the fourteen-day mark.  183 cats were listed as being on site for both seven days and fourteen days.

Most duplicate listings are kittens.  Rescue groups and shelters are anxious to get them adopted when they are very young.

Comparison of cats on for less than thirty days with those on site for thirty days plus.

There are more kittens on the site for 30 days plus

However, there are more kittens in the less than thirty day category relative to other ages.

Kittens comprise the overwhelming majority of cats listed on the site for less than thirty days.

There seems to be a bit of a preference for male cats.  However, I am highly skeptical of this.  I worked as a statistician in the fundraising area of an animal welfare organization for two years and I visited the cats in the shelter in my spare time and talk to the people who volunteered there.  I never heard of a gender bias.

There aren’t videos of most of the cats on the site.  I think if you look at things from a marketing perspective, videos help.  Videos are under utilized.

A pet narrative is a paragraph about the cat.  It often tells where the cat came from, if the cat is shy or really  friendly, if it is currently fostered in someone’s home., if it is bonded with another cat that also needs a home etc.  It doesn’t seem like longer pet narratives will help a cat get adopted.  Narratives are important because people want to know about the personality cat , but being as concise as possible is important.

Pictures are underutilized on the site especially for cats listed as less than thirty days.  I conducted an informal survey of people I know and most potential adopters won't look at any other information about a pet if a picture is not there.

Longhaired cats seem to get adopted faster.  Longhair is recessive.  Usually demand for what is rare is greater.

Recommendations to rescue groups and shelters.

  • Relist kittens who have been on the site for more than thirty days if they have not already been adopted.  Rescue Groups are responsible for maintaining their listings.
  • Make sure there is a picture of each cat.  It is important to think about things from the point of view of potential adopters.  They want to see the cat.
  • Include a video of each cat on their webpage.
  • Be concise in pet narratives.


If you would like to see the details of how I conducted my analysis, this is the Github link.


About Author

Denise Garbato

I am a Statistician and Business Analyst who supports strategic decision making in digital and traditional marketing channels by discovering insights, applying statistical and programming skills with a results-focused approach. I am skilled in data analysis and predictive...
View all posts by Denise Garbato >

Related Articles

Leave a Comment

No comments found.

View Posts by Categories

Our Recent Popular Posts

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI