Inspiration
What initially inspired this project was the InfoTech challenge to use a dataset for public good. We found a dataset on Kaggle.com that provided results from a series of questions asked in an anxiety survey as well as the users self reported score. We decided to use the answers to the questions as the features and the self reported score as the label to create a machine learning model. Then we displayed the resulted prediction on a website.
What it does
This website allows a place for users to answer the questions and receive results about their predicted anxiety/depression. If the model prediction they are at risk for anxiety or depression resources are provided.
How we built it
The machine learning model was done in Jupyter Notebook and the website was built with Flask.
Challenges we ran into
One challenge was that as beginners, we are all fairly new to the technology used in this project. We weren't familiar with front end and had very minimal experience using machine learning.
Accomplishments that we're proud of
We are proud of getting this project finished due to the short amount of time we had.
What we learned
We learned to use a lot of the technologies I described that we weren't familiar with.
What's next for Anxiety-Depression Prediction
If this survey was used by a large number of people, after the the survey is completed and results are shown, we could offer the option to answer a few additional questions. These questions would determine whether the user has ever received treatment for anxiety/depression or been diagnosed with it (Y\N). We could use this to replace the label we have in the model, which is currently self reported. This could improve the accuracy of the model.
**Jess#9986 for the judging
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