MentalWell Connect

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Inspiration

Moving to a foreign country as an international student presents numerous challenges, such as building new social connections, discovering on-campus events, and effectively managing stress. The quest for a tight-knit community becomes especially crucial, as it offers a sense of belonging and support during challenging moments.

What it does

MentalHealth Connect is a versatile application that offers a wide range of services. It includes stress level assessments for students and individuals, helping them maintain healthy relationships with friends and family. Moreover, Connect offers a personality questionnaire that pairs users with like-minded individuals, fostering the development of new and meaningful friendships and relationships. Personal chat functionality allows users to initiate conversations with new like-minded individuals, identify common interests, and exchange contact information. Additionally, the platform features a blogging option, enabling users to express their opinions anonymously and discover valuable ways to nurture friendships and relationships. The website offers a stress questionnaire as a helpful tool for users to assess their stress levels and learn strategies for managing it, which can ultimately contribute to the nurturing of their relationships. The stress questionnaire was unique as each individual could choose an appropriate level of stress on a scale of 1-5.

How we built it

To build our project, we employed a combination of various technologies and techniques, including K-Nearest Neighbors (KNN) algorithm, clustering, Flask, HTML, CSS, and JavaScript. We incorporated the KNN algorithm to perform tasks such as classification and recommendation. KNN is an effective machine learning algorithm that helped us calculate the similarity between two individuals based on their personality and questionnaires. We also deep dived into the concept of clustering, where we calculated the K nearest neighbors with the help of euclidean distance and accordingly we clustered them together as a recommendation. We used the Flask web framework to create the backend of our application, and HTML/CSS/JS to create the frontend.

Challenges we ran into

During the course of our project, we encountered several significant challenges. Git version control presented its own set of challenges. Coordinating changes made by multiple team members, managing branches, and resolving merge conflicts were some of the issues we had to address. Establishing a functional and efficient database was a complex task. This involved defining the schema, configuring the database management system, and ensuring data integrity. Transferring data between different Flask routes can be intricate. We had to design and implement a structure that allowed for smooth data flow while maintaining data consistency. Deploying a machine learning model can be a complex task. It involves setting up the infrastructure, integrating the model into the application, and optimizing it for real-time use.

Accomplishments that we're proud of

Finish the project with all the desired features. We were able to learn git control and were able to work as a team on individual parts and merge all individual features into one application. We also designed reusable components for the frontend, and followed the product lifecycle processes,such as brainstorming, ideation, building, and testing.

What we learned

This project provided us with a great opportunity to learn and refine a diverse range of skills, each contributing to our growth and expertise. We gained experience utilizing Git version control, integrating machine learning models with web interfaces, creating creative algorithms on the spot, and constructing validated mental health assessments. Throughout this journey, we learned about the product development cycle and the potential of collaborative teamwork. This gave us great experience to be able to pursue future opportunities and challenges.

What's next for MentalWell Connect

There are several exciting avenues for further development in this project. We can enhance the machine learning model by incorporating additional data to improve its accuracy. Moreover, the addition of supplementary mental health tools such as mood trackers and therapy finders can offer users more comprehensive resources. Additionally, we can explore the possibility of incorporating in-person and virtual events and workshops in partnership with mental health professionals to foster a safe community and provide expert guidance.