The problem Da Vinci Mental Health Test WebApp solves

To create an informative AI-based tool that should be able to give alerts and identify cases of mental health issues in children and also track the improvement status in identified cases. The Da Vinci Mental Health Test uses a questionnaire approach to evaluate a test user’s mental health. The questions are generic and empathetic, describing different thoughts and feelings related to mental disorders. The options vary in intensity of feeling and thus, determine the severity of the disorder that the test user is facing.
From the questionnaire, the selected options are passed to the python model. The model uses machine learning (ML) to predict the severity of depression being faced by the test user. We have employed a Classifying Algorithm (Random Forest), which is a supervised learning technique. The dataset used for training and testing has been acquired via an internet survey.
We may update the dataset by adding the user data that we get to make the model more extensive in the future.

Challenges we ran into

Due to lack of research in the field, appropriate datasets were extremely difficult to find.
Working on technological assistance for mental health support is also hindered by its "subjectiveness". It is difficult to quantify the practical aspects of mental health.
The difficult part was to integrate our ML model with the web interface. To handle it we made use of Flask to create an API