The problem Mental Health Prediction Model With Detection. solves

1. Identifying Biomarkers / Developing Treatment Plans.

2. Predicting Crises.

3. By identifying biomarkers and pressure points of a person an ML model can recommend certain activities that one shouldn’t engage in as it can result in one having a bad mood or even causing a major disorder .

4. Through reading the parameters the ML model can predict of an individual is prone to certain disorder such as depression or
   even a small episode of migraine.
   If the one is aware one take better care to mentain his/her mental health.

#The Following projects were made by our team during HACKVSIT2020 pertaining to the "Sustainable Development Goals" Theme and catering towards the Mental Health Sub-Problem.

The Projects Made Were as follows and have been attached along with their executing insturctions :

1. Emotion Detection Program (To detect change in person's facial expressions).

2. Study Neuron Responses Program (TO detect and study the reflex responses of a person being affected by mental health issues).

3. A Mental Health Prediction Model based upon the happiness_Index 2019 released by the World Health Organization (WHO).
   (The Prediction Model Was Made By the use of MICROSOFT AZURE MACHINE LEARNING STUDIO).

Challenges we ran into

1. A couple of years ago, voice-controlled jumping games took the internet by storm. However, we wanted to make a fun gesture-controlled game for people with mental disabilities (and of course people who don't like screaming). So we made the ClenchRex.

2.This project aims to classify the emotion on a person's face into one of seven categories, using deep convolutional neural networks.
The model is trained on the FER-2013 dataset which was published on International Conference on Machine Learning (ICML).
This dataset consists of 35887 grayscale, 48x48 sized face images with seven emotions - angry, disgusted, fearful, happy, neutral, sad and surprised.

3. It is the Deployed file which includes filtered data without the redundant categories and numerical value of data
   on which Linear Regression Algorithms are applied and the model is trained in order to achieve the Happiness index
   or " resultant score".
   The purpose of this file is to operate on the cleanesty dataset possible.

4.This model works on a sample data set, and it is used to

1. Depict Tablular Data set
   2)Calculate "Resultant score" or the Happiness Index Accurately.