Mental Health Survey

Using machine learning, we created a classification model with an accuracy of 100% and then deployed it using Django framework.

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Inspiration

When we heard about the prompt we knew that we wanted to use. machine learning in some way with our project. So after talking a little bit we came up with the idea of teaching our computer how to assess the mental state of a user by giving it a large amount of data. After doing some more research we knew that it was something that we could actually do so we finalized our decision.

What it does

Our project includes a website that has a survey which asks the user 17 questions about their mental health. When the user submits their answers, our model will assess them and output the mental state of the user.

How we built it

We build using Sklearn, a machine learning library and Django Framework.

Challenges we ran into

We initial trained a neural network using tensorflow but wasn't able to deploy it, so we had re-trained the model using sklearn. We also ran into multiple frontend development problem and took us quite a time to connect every website and the model using django.

Accomplishments that we're proud of

We are proud that this is our first hackathon, and we were able to complete a project in just 2 days that includes machine learning.

What we learned

We learned how to use Django framework properly and we also learned frontend coding which have never specialised in. Apart from that, we learned how to collaborate and work together as a team.

What's next for Mental Health Survey

We will look to prettify our result page and also make our code efficient. We will look to deploy the site in one of the python-friendly servers and we may even extend our website to new features related to well-being.

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