NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > R Visualization > How to Help Shelter Animals

How to Help Shelter Animals

Chuan Hong
Posted on Oct 25, 2016

Contributed by Chuan Hong. Chuan is currently in the NYC Data Science Academy 12 week full-time Data Science Bootcamp program taking place between September 26th to December 23rd, 2016. This post is based on her class project - R  Visualization.

Introduction

Each year, approximately 7.6 million companion animals enter animal shelters nationwide (ASPCA). Of those, approximately 3.9 million are dogs and 3.4 million are cats.  About 2.7 million shelter animals are adopted each year (1.4 million dogs and 1.3 million cats). Meanwhile, about 649,000 animals who enter shelters as strays are returned to their owners (542,000 dogs and 100,000 cats). Compared to these lucky cats and dogs finding their families to take them home, many shelter animals face an uncertain future. It is estimated that 2.7 million cats and dogs are euthanized in the US every year. Given the differences in outcomes for shelter animals, we can analyze the factors that make some cats and dogs more likely to get adopted.

 

Dataset

Two months ago, Kaggle hosted a competition to predict the outcome of shelter animals, in order to help shelters focus their energy on specific animals who need a little extra help finding a new home. The dataset was from Austin Animal Center.

In this dataset, there are ten variables, which are  "AnimalID", "Name", "DateTime", "AnimalType"(Dog/Cat), "SexuponOutcome"(Neuteraed Male/Spayed Female/Intact Male/Intace Female), "AgeuponOutcome", "Breed", "Color", "OutcomeType"(Return_to_owner/Adoption/Transfer/Euthanasia/Died), and "OutcomeSubtype"(Other/Foster/Offsite/Partner/Barn/SCRP/Suffering/etc.).

After a quick check of these variables, I decided that"Color" and "OutcomeSubtype" would not be included in this visualization project. This was because that there were 300+ unique colors in this dataset. It was way too many to visualize factor by factor. Meanwhile, based on the Sankey plot below, we can see that the "OutcomeSubtype" is a detailed explanation of the variable "Outcome". 

sanky

Sankey Plot of OutcomeType versus OutcomeSubtype

 

Exploratory Data Analysis (EDA)

In this project, I did some EDA to investigate the potential relationships between factors and animal outcomes, especially adoption situation.

  • Does animal type matter? Cats vs. Dogs

First, let's look at how many cats and dogs we have in this dataset and how different outcomes are distributed. From the two graphs shown below, we can see that both cats and dogs were commonly adopted, but dogs are much more likely to be returned to their owners than cats, and cats are transferred between shelters more often than dogs. It also appears that very few animals died or got euthanized overall.

catsvsdogs catsvsdogspct

  • Does name matter?

There are quite a few cats and dogs in this dataset who sadly donโ€™t have names. I was curious to see if having a name affected their fate. The graphs below indicate that the situation was different between cats and dogs. Cats with names were more likely to be adopted; while for dogs, the percentage of adoption was similar whether having a name or not.

name name

  • Does sex matter?

The "SexuponOutcome" (Neutered Male/Spayed Female/Intact Male/Intact Female) variable contains two types of information: if the cat/dog was male or female, and if it was neutered/spayed or intact. So, there are two distinctive features in fact. I then encoded this variable into two, "sex" and "isNeutered". It seems like the adoption count and percentage were similar between male and female in both cats and dogs.

sex sex

  • Does spaying/neutering matter?

The graphs below show that neutered (or spayed) was a potentially strong factor. Cats or dogs were more likely to be adopted if theyโ€™ve been neutered.

isneutered isneutered

  • Does mixed breed matter?

Further, we have information about "Breed" in this dataset. Some animals had pure or mixed breed. I wondered if breed purity has some positive impact on the fate of an animal. Then, I created three variables from the original variable โ€œBreedโ€, "isMix", "primarybreed", and "secondarybreed". However, there were no obvious differences between pure and mixed breeds ( see the graph of the percentage below).

ismixismix

  • Does breed matter?

The breed variable has way too many levels, so, for the breed analysis, I just selected the top eight most popular breeds in this dataset for cat and dog, respectively.

(1) For Top 8 cat breeds

From the graph of the count, we can see that the majority breeds of cats are Shorthair, Median hair, Longhair, and Siamese. But, the percentage graph shows that the adoption percentage is similar for these top four groups. So, the breed may not a strong factor affecting the fate of cats.

cat_top8

cat_top8_pct

(2) For Top 8 dog breeds

Likewise, the percentage of adoption among the top eight breeds of dogs are similar too.

breed_dog_top8breed_dog_top8_pct

  • Does age matter?

Another potential factor is "Age", but we have this variable in different units (i.e.  years, months, weeks, and days). So, we converted every "Age" into "Ageinyear" and "Ageinmonth", then explored whether there were some different trends related to age.

Based on the two pairs of graphs below, outcome by age in years and outcome by age in months, we can see that most of the animals in the shelter were  0-1 years old. Meanwhile, it seems like that young cats and dogs have much higher chances to be adopted, while older cats and dogs with approximately equal probability can be adopted.

(1) By year

ageinyear ageinyear

(2) By month

ageinmon ageinmon

  • Does outcome time matter?

Finally, one very important factor is "DateTime", which is the time when the outcome happened. It looks like that cats are more likely to be adopted during summer and winter and dogs are more likely to be adopted during winter too (based on the graph by month). Meanwhile, we assume that the adoption peaks are weekends and 4:00 pm to 6:00 pm (graph of by hour).

(1) By month

outcometimey

(2) By weekday

outcomew

(3) By hour

outcomeh

  • Heat map of adoption: weekdays and hours

To explore and understand the trends of adoption peak, two heat maps with the number of adoption vs. weekday and hour were created. We can see that adoptions are more likely happening during weekends and from 4:00 pm to 6:00 pm. The trend of cats is similar to that of dogs.

heatmap

 

Conclusions

  • "Age", "DateTime", and "isNeutered" might be driving factors.
  • "sex" and "isMix" might not be important.
  • "hasName" and "Breed" may result in different outcomes between cats and dogs.

Based on the findings, animal shelters may need to turn to unique promotions to encourage potential owners to take relatively older cats or dogs. Meanwhile, shelters can reduce the adoption fee for a cat or dog older than one-year-old, and they can bring only older cats and dogs during adoption peak, such as weekends, to highlight them.

 

Future Works

  • To look deep into the pattern of missingness and use proper ways to do imputation.
  • To do some statistical analysis (e.g. Chi-square test, ANOVA. etc.).
  • To apply multiclass classification (e.g. randomForest, XGboost, etc.) to investigate which potential factor is the strongest one.

You may also explore this project via Chuan's GitHub.

 

References:

The American Society for the Prevention of Cruelty to Animals (ASPCA), Pet Statistics

 

About Author

Chuan Hong

Chuan Hong is a Ph.D. Candidate majoring in Public Health at the University of South Carolina. Her main research areas are environmental health sciences, with a focus on environmental epidemiology. By using a series of data collection, statistical...
View all posts by Chuan Hong >

Leave a Comment

Cancel reply

You must be logged in to post a comment.

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

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 ChatGPT 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 football 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 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

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application