Inspiration
The inspiration for Collease came from the challenges many students face when trying to figure out which college or path is the right fit for their goals. With so many options and complex application processes, students often feel overwhelmed and unsure of where to start. Collease was designed to simplify this process and provide a clear, data-driven pathway for students to follow.
What it does
Collease is a personalized college recommendation platform designed to help high school, graduate, and transfer students navigate the complex application process. By analyzing a student’s academic data, interests, and preferences, Collease generates a tailored list of universities and an actionable plan to improve their chances of acceptance. Our backend calculates standardized scores and compares profiles with other students, ensuring each recommendation is data-driven and accurate. With Collease, students receive personalized guidance on university choices, deadlines, and future course planning, all in one place.
How we built it
We built Collease using AWS for cloud infrastructure and S3 for storing files. The frontend was developed with React and styled using CSS3, while the backend, written in JavaScript, processes user input, communicates with external APIs, and generates personalized university recommendations. We used Git and GitHub for version control, ensuring smooth collaboration and deployment on AWS to allow for scalability and broad access.
Challenges we ran into
One of the main challenges we encountered was establishing efficient communication between the frontend and backend to ensure smooth data flow, especially during user input processing and API requests. Additionally, making sure the platform could handle large volumes of data while maintaining performance required careful optimization and debugging.
Accomplishments that we're proud of/What we learned
We’re proud of building a seamless integration between the frontend, backend, and API systems, which made Collease a highly responsive and user-friendly platform. Learning to optimize performance and manage large datasets while creating a personalized recommendation engine was a significant achievement for our team.
What's next for Collease
We plan to scale Collease further by expanding its capabilities to better serve transfer students and graduate students, offering personalized recommendations tailored to their unique academic paths. Additionally, we aim to enhance the platform's data handling and integrate more universities and programs to provide even more comprehensive guidance.
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