Mental Health Risk Detection in Text Using NLP and Logistic Regression

–  This project presents a machine learning approach to detect mental health risks from user-generated text using Natural Language Processing (NLP) techniques. A dataset containing labeled mental health-related text is preprocessed with stopword removal, lowercasing, and token cleaning. The clean text is then vectorized using TF-IDF and classified using a Logistic Regression model. The model achieves strong performance metrics, including accuracy and precision, in identifying potential mental health concerns. A prediction function is also developed for real-time risk assessment of new input text. This tool aims to assist in early detection and intervention by flagging potential indicators of mental distress.