ReJuve

Inspiration

Our inspiration for our website was finding ways to help students feel less stressed and pressured. We wanted to help them recognize areas in their life that might not be very healthy and provide suggestions to help them feel more confident and ways to be supported.

What it does

Our website helps analyze our user's behavior and habits that contribute to their mental health and well-being and provides insight for improvement within those areas.

How we built it

We utilized a series of K-Nearest Neighbor models to map the similarity between test subjects and the user to score them with the values that was most mathematically similar to them. The models came from the Scikit Learn library in Python. We then pushed each value into an array and printed different messages based on the scores for each category. We used Websockets to transfer the data from a backend Python server to use for the website. Our frontend was built with HTML and CSS, as well as JavaScript for the calculations and button functionality.

Challenges we ran into

Initially, we designed our program with Figma but had to scrap our progress because we found it was difficult to create variables and translate it to html.

Accomplishments that we're proud of

We are proud of completing a full stack functional website. Most of our members were also able to experience learning new coding languages and working with

What we learned

We were able to learn about using html/css, machine learning, and web-socket. As well as other valuable skills like collaboration and punctuation.

What's next for ReJuve

Our future plans include hopefully incorporating an ai feedback bot to assist our users and interactive activity and tracker to motivating our users to complete their goals.

Built With

• css
• html
• javascript
• k-nearest
• ml
• node.js
• numpy
• pandas
• python
• scikit-learn
• websocket