Millions of animals either suffer on the streets or are euthanized each year due to the lack of a loving home. We've decided to look for a solution to this issue.

What It Does:

Animals that come into the system are rated by a machine learning model. The web app recommends a pet based on it's adoptability and helps the animal shelter to get more animals into caring homes. Approved parties can put a pet up for adoption on the site for others to view and adopt directly from their home. This will in turn reduce the number of strays and euthanized animals and increase the number of people who have found new companionship.

How We Built It:

Garrett and Brandon did a quick intro into Angular 3 to design the physical webpage alongside inputting all of the information we have gathered into the previously mentioned system. After agreeing on the idea, Jordan took to Python and Kaggle to find an appropriate dataset to download, and trained a machine learning model for the site. Brad learned some Javascript to create a search engine for an animal picture of the breed of the animal if it did not have a picture uploaded and developed the presentation materials.

Challenges :

Learning a huge framework such as angular in a night served very challenging, alongside learning to read Javascript because not all of us are very fluent in the language. Our original plan for a training dataset was not possible due to some restrictions, so we had to spend some time finding a new dataset. After getting the dataset, we also struggled when using machine learning to find a correlation between time animal characteristics and adoption rates. The last challenge we encountered was implementing Express with Node.js for the database management.

Accomplishments That We're Proud of:

We are proud to have made a functional website from the ground up. We are also extremely proud of the idea we have decided upon. We believe a site such as "Pawsitive Impact" would do exactly what it's name implies. Aside from all the coding and development, we are proud to support an issue to the extent that this one has reached.

What We Learned:

Building a website, while being a massive workload, is extremely fun with a team like the one we have gotten to work with. Each person had different qualities and abilities which helped this project come together in a way that we are pleased with.

What's next for Pawsitive Impact?

Graphing the difference we've made on the number of euthanized animals, automatic identification for breed and color using machine learning, more advanced searching, better mobile responsiveness, gathering more data, and leaving a "pawsitive impact" both in the lives of the animals and the lives of our users worldwide.

Share this project: