WConnectU

WConnect is a social prescribing website providing comprehensive information to help students with difficult situations.

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Inspiration

The inspiration for WConnectU came from a desire to create a platform that connects people to community resources through social prescribing. Witnessing the positive impact of social connections on mental and physical health, we wanted to build a tool that could facilitate these connections and improve overall well-being.

What it does

WConnectU is a platform designed to link individuals with community resources based on their specific needs and interests. It provides users with personalized recommendations for activities, groups, and services that can enhance their physical, mental, and social health.

How we built it

Idea Generation:

  • Brainstormed the concept of a social prescribing platform.
  • Conducted research on existing solutions and identified gaps.

Design:

  • Created wireframes and mockups for the website layout using Figma.
  • Focused on a clean, user-friendly interface. Development:
  • Used HTML and CSS for front-end development.
  • Integrated a backend system using Python and Flask to manage user data and community resources.
  • Integrated en_core_web_sm model to analyze the user's mood description for recommendations

Challenges we ran into

Team Size and Experience: Our team consisted of only two people with limited experience in web development and Figma.

Accomplishments that we're proud of

Despite this, we managed to finish the project with a rough prototype.

What we learned

Throughout this project, we learned the importance of user-centered design and the impact of social connections on health. We gained valuable insights into web development, user experience design, and the integration of community resources into a digital platform.

What's next for WConnectU

  • Advanced Web Design: Improve the website's design and functionality with more advanced web design techniques.
  • Web Scraping for More Information: Implement web scraping to gather additional information and resources for users.
  • Machine Learning: Find datasets and use machine learning to train a more customized recommendation system.
  • Deployment: Work on actually deploying the platform to a broader audience for real-world use.
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