Identifying Depression Rate Among Undergraduate and Master Students using Machine Learning

Depression is a serious mental health issue for people world-wide irrelevant of their ages, genders and races but most commonly it is reported among university students in many regions of the world and has a great impact on quality of life, academic attainment and learning abilities. To address this

Project Title

Identifying Depression Rate Among Undergraduate and Master Students using Machine Learning

Project Area of Specialization

Software Engineering

Project Summary

Depression is a serious mental health issue for people world-wide irrelevant of their ages, genders and races but most commonly it is reported among university students in many regions of the world and has a great impact on quality of life, academic attainment and learning abilities. To address this grave and gnarly issue, mental health professionals need a scientific basis to devise methods to counter depression among undergraduate students. In this age of modern communication and technology, people feel more comfortable sharing their thoughts in social networking sites almost every day. This public data will help to analyze the data and identify depression rate among undergraduate and masters’ students.

Project Objectives

The objective of this project is to propose a data-analytic based model to detect

• depression rate of students in undergraduate and post graduate phase
• Depression level of students based on his posts on social media.

Project Implementation Method

• The data Collection part of this research based project will done by 2 approaches which are:

1. Questionnaires and surveys both online and in hard copies) having proper algorithm at the backend (phq19) will be circulated among different University students
2. Social network (facebook) will help us in data acquisition using web scrapping.

• Machine learning will be used to process the scrapped data collected from spcial network sites users.
• Different Python libraries :Natural Language Processing (NLP ) and Naïve Bayes algorithm will be used to detect depression  potentially in a more convenient and efficient way

Benefits of the Project

As the ratio of students being depressed is increasing rapidly, this research based project after detecting the ratio of the students suffering from mental health will allow the government to take further steps to detect the things and reasons that are ruining the mental health of students and similarly institutes will be aware and will try to solve the defects in their education system.

Technical Details of Final Deliverable

The end product will show the students detected with depression, stage of their depression and overall ratio of depressed undergraduate and postgraduate students.

Final Deliverable of the Project

Software System

Core Industry

Medical

Other Industries

Core Technology

Others

Other Technologies

Sustainable Development Goals

Good Health and Well-Being for People, Quality Education

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com