SIMH(Self identifying the mental health of person)

Self Identifying the Mental Health

Self-Identifying Mental Health Project Inspiration The inspiration for this project stemmed from the increasing prevalence of mental health issues worldwide. With the rise of social media and digital communication, many individuals express their feelings and struggles through text. I wanted to create a tool that could potentially identify those at risk of mental health issues, particularly suicide, from their written words. The hope was to provide an early warning system that could prompt timely intervention and support.

What I Learned Throughout the development of this project, I learned a great deal about natural language processing (NLP) and machine learning (ML). Specifically:

Data Preprocessing: The importance of cleaning and preprocessing text data to improve model performance. TF-IDF Vectorization: How to convert textual data into numerical features using TF-IDF vectorization. Model Training and Evaluation: Training and evaluating different ML models, including Logistic Regression and K-Nearest Neighbors (KNN). Model Comparison: Comparing the performance of different models and selecting the most suitable one based on accuracy and other metrics. Streamlit: Building interactive web applications using Streamlit to make ML models accessible to end-users.