This is a Streamlit web application that predicts your daily mood score (1-10) based on your personal daily habits. It uses a pretrained machine learning model to estimate mood from habit metrics such as sleep, workout, reading, meditation, water intake, screen time, and more.

• Input sliders for daily habit metrics including:
  • Sleep Hours
  • Workout Duration (minutes)
  • Reading Duration (minutes)
  • Meditation Duration (minutes)
  • Water Intake (liters)
  • Screen Time (hours)
• Automatically fills missing features (e.g., journaling and daily expense) with default values for compatibility with the pretrained model.
• Predicts mood score interactively on button click.
• Clean and responsive UI powered by Streamlit.
• Includes social links and a footer for branding.

• Python 3.7+
• Streamlit
• Required Python packages (listed in requirements/base.txt or install manually):

pip install streamlit numpy scikit-learn xgboost pandas

1. Clone the repository

git clone https://github.com/danaelshrbiny10/Habit-Tracker.git
cd Habit-Tracker

2. Create virtual environment

python -m venv venv
source venv/bin/activate  # or venv\Scripts\activate on Windows

3. Install dependencies

pip install -r requirements/base.txt

4. Run the application

streamlit run dashboard.py

Visit http://localhost:8501/ in your browser.

The dataset used in this project is available at: 90 Day Habit Tracker for Personal Growth - Kaggle

For questions or contributions, feel free to reach out at [email protected].

This project is open-source under the MIT License.