Pickup

Ever wanted to play pickup sports or join a game but struggled to find people to play with? Look no further — "Pickup" got you covered! We know what you like, because we care about your enthusiasm!

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Inspiration In our digital-first world, we wanted to build something that brings people back to the joys of real-life connections. Our inspiration was simple: give people a way to meet, connect, and get moving together! By creating a platform centered around sports and activities, we’re hoping to help people find community in a fun, active way.

What it does Pickup is like having a buddy who always knows what’s going on nearby. Users can jump into a variety of sports and recreational activities or even take the lead by hosting their own events. It’s all about creating a place where you can find new friends, have fun, and enjoy the thrill of joining in or starting something awesome yourself.

How we built it Our tech stack is the secret sauce! We used React.js to make the app super responsive and fun to navigate. Node.js powers the backend, while Firebase handles user authentication and data in a way that feels smooth and secure. Plus, Python runs our ML algorithms to make sure users get matched with the right activities. It’s all about creating an environment that’s both safe and easy to use, so our users can focus on the fun.

Challenges we ran into 1) We definitely hit some snags along the way! Both frontend and backend gave us a run for our money, especially when it came to querying the database and designing an ML model that “gets” our user data. But we tackled each roadblock together, coming up with solutions that made our app stronger and better.

2) We thought of Recommending best thing to the user ,So we designed the whole Recommendation System We didn't have the data so we manually labeled the whole dataset and Designed Machine Learning algorithm from scratch. We did hybrid recommendation system combining collabrative and content system to recommend user what data they like over their prefrences. After running Algorithm over our Dataset we stored machine learning waits and retrieve it after hosting in O(1) as I hashed those weights

Accomplishments that we're proud of Seeing Pickup come to life has been so rewarding. Every tweak, every late-night brainstorm, and every “aha” moment brought us closer to a community-driven app that we’re truly proud of. And knowing that we’ve created something that can bring people together in the real world? That’s the best feeling!

What we learned From managing data like pros to aligning ML models with our users' needs, we picked up tons of knowledge that will stick with us. We also learned just how powerful teamwork can be in creating something meaningful and fun!

What's next for Pickup We’re just getting started! Next up, we plan to roll out options for more sports and activities, making Pickup a go-to space for anyone looking to get active and connect with others who share their interests.

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