Mental Stress Detection in University Students using Machine Learning

PROJECT ABSTRACT:

Mental Stress Detection in University Students using Machine Learning: In various parts of the nation, a student commits suicide once per hour. According to a 2012 Lancet research, India has documented a significant number of suicides among young people between the ages of fifteen and twenty-nine. We can assist pupils in healing in the short or long term if we can determine their degree of stress. This project focuses on the development of a mental stress detection system using a machine learning algorithm, specifically the Random Forest Classifier.

The objective of this study is to accurately identify and analyze the mental stress levels of university students. The dataset used in this research was collected from kaggle. Participants were asked a series of questions pertaining to their emotional state and reactions in various situations they may have encountered. These answers were assigned weights to calculate a stress score, enabling a comprehensive analysis of individuals’ stress levels.

The Random Forest Classifier was employed as the machine learning algorithm for stress detection due to its ability to handle complex datasets and provide reliable predictions. The algorithm was trained using the collected dataset, achieving a remarkable training accuracy of 100%. Furthermore, the developed model exhibited a test accuracy of 93%, indicating its effectiveness in detecting mental stress among university students. By utilizing machine learning techniques, this project contributes to the field of mental health by offering a non-invasive and efficient approach to identify and assess stress levels.

The proposed system has the potential to provide timely interventions and support for individuals experiencing high levels of stress, thereby promoting overall well-being within the university community. We enhanced the system by developing with Flask web framework with the User Interface of asking questions among students and collecting the answers from the students and predicting the Mental Stress among the students using Random Forest Classifier.

PROJECT OUTPUT VIDEO:

ALGORITHM / MODEL USED for Mental Stress Detection in University Students using Machine Learning:

Random Forest Classifier.

SYSTEM REQUIREMENTS:

HARDWARE REQUIREMENTS:

System : Pentium i3 Processor.
Hard Disk : 500 GB.
Monitor : 15’’ LED
Input Devices : Keyboard, Mouse
Ram : 6 GB

SOFTWARE REQUIREMENTS:

Operating system : Windows 10 Pro.
Coding Language : Python 3.8
Web Framework : Flask.
Database : MYSQL

Python Projects

Mental Stress Detection in University Students using Machine Learning