Machine Learning based Self Identification of Mental Health

Addressing the growing concern of mental health issues in the 21st century requires innovative solutions. This project proposes utilizing Machine Learning algorithms such as Decision Trees, Random Forests, Support Vector Machines, K-Nearest Neighbors, Convolutional Neural Network for early detection and intervention. By analyzing diverse data from individuals across different demographics, including age groups, professions, genders, and lifestyles, the system aims to recognize mental health. Voice recognition using PyAudio and Face recognition using CV2, PIL, CNN supplements the analysis, offering a more comprehensive understanding of the user's mental state. Through raising awareness and providing accessible diagnosis, the project aims to empower individuals to prioritize their mental well-being, ultimately mitigating the potential rise of a "mental health epidemic."