This final project aims to develop a web application using Streamlit that helps users analyze and track their mental health over time. The application incorporates Time Series Analysis to identify trends, seasonal patterns, and fluctuations in mental health data, alongside Natural Language Processing (NLP) techniques to analyze free-text journal entries. The project focuses on gathering daily self-reported data, such as mood, stress levels, and journal entries, to monitor mental health. Users can visualize their mood trends over time and gain insights into the emotional context of their journal entries. The application integrates Python libraries, including Pandas for data management, Plotly for data visualization, and specialized libraries for Time Series and NLP analysis. The primary goal of the project is to empower users to take control of their mental health by offering an accessible, user-friendly tool to track and analyze emotional well-being.
Tools :
- Streamlit for web application development.
- Python for data manipulation and visualization.
- Time Series Analysis for identifying trends.
- Natural Language Processing (NLP) for analyzing journal entries.
- Plotly for interactive visualizations.
- CSV Files for data storage.