Inspiration

Approximately 7.6 million companion animals move to shelter homes every year in United States but still there is no standardised application or website for adopting these animals. The websites which are already supporting the homeless animals adoption have really complex and cumbersome procedures. Discussing the problems and barriers in the current system inspired us to come up with an interactive application which will smoothen the adoption procedure. The android application tries to resolve the concern whether everyone will put in efforts to get the complicated and demanding process.

What it does

Buddy Finder is an Android application which provides an one stop solution for the adopters as well as the individuals/organisations to help to reduce the number of homeless dogs. The individuals/organisations can post the animals up for adoptions in order t reach out to the maximum audience. The adopters on the other hand will be to see the listed animals for adoption and request for adoption. This is the first application to provide home-to-home transfer of pets, reducing the pressure on shelter homes. The app is the first in the market to provide the history of the pet. The application provides an easy approach for request response for adoption. The individuals/organisations listing the dogs can even have an insight into adopters involvement with pets in past.

How we built it

We built the android application using Android Studio.We used Java as the primary language for building the application.We used firebase as the backend for storing the data and used the google cloud platform machine learning APIs.Besides these we used the facebook developer kit for the authentication of our application and we used the Facebook Graph API in our application for mining the information about the user.

Challenges we ran into

The major challenge was collecting data from different sources and presenting it on a uniform platform. Different shelter homes have different process for adoption application hence it was really difficult to pick up the key points which fit in for all the shelter homes. Also we had to run various iterations to come up with an effective way of showcasing the health records of the dog. The only way to make the process convenient is to make the adoption application simpler which poses a lot of problems for the relevant dog suggestion feature for the adopter. We had to work closely with possibilities for suggestion mechanism which consumed our major time portion.

Accomplishments that we're proud of

We had three out of four people participating for the first time in MLH hackathon. We could develop a proper coordination with team mates from different universities and parts of world. Since we are new to major APIs it was great really appreciable that we could learn and implement several API in just 36 hours. Our application is a whole new breakthrough for the cause and implements revolutionary features.

What we learned

Google Cloud APIs Facebook APIs Data Scraping Machine Learning
Android App Development Firebase UI Designing

What's next for Buddy Finder

Train the dog suggestion/recommendation for adopters implementation using advanced machine learning training for application. Introducing verified user feature to avoid extensive and cumbersome adoption procedure like home check. Fetch live data from the health trackers of dogs to provide real time health of dogs.

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