Inspiration
Our expectation was to tackle a complex problem which Braven's problem statement provided us. The matching process between interviewee and student's career interests gives us an opportunity to develop a robust algorithm.
What it does
The web application provides a single stop for students to add their career interests; interviewers to add their skillsets and admins to add events and manually match some special requests. Once this data is provided, the algorithm matches students to interviewers based on the responses provided by either of them.
How we built it
Front-end was developed in React JS and Backend was developed in Node JS. The front-end is hosted on Heroku, whereas the backend on EC2 instance. The application uses a Mongo Atlas (MongoDB) as database.
Challenges we ran into
Maintaining quality factors while handling all the complex features.
Accomplishments that we're proud of
The matching algorithm that we developed
What we learned
Collaborative efforts always helps us take big leaps while progressing together. There's always something that will not go as expected.
What's next for Braven's One Stop for All
Factors like Diversity and Inclusion on the basis of Gender, accent etc. for better matching. Using text-to-speech to transcribe the interview for future reference. Once considerable amount of data is gathered, Machine learning models can help in better data analysis and provide more means for matching.
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