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https://github.com/yelamankarassay/personal-health-wellness-dashboard

A Streamlit-based dashboard for visualizing and analyzing personal daily data—weight, mood, meals, sleep, and more. This project uses pandas, plotly, matplotlib, seaborn, scikit-learn, and wordcloud to present insights about your health and daily habits.
https://github.com/yelamankarassay/personal-health-wellness-dashboard

matplotlib pandas plotly scikit-learn seaborn wordcloud

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A Streamlit-based dashboard for visualizing and analyzing personal daily data—weight, mood, meals, sleep, and more. This project uses pandas, plotly, matplotlib, seaborn, scikit-learn, and wordcloud to present insights about your health and daily habits.

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README 

        
# Personal Health & Wellness Dashboard



Link: [Dashboard](https://daily-wellness-dashboard.streamlit.app/)



A **Streamlit**-based dashboard for visualizing and analyzing personal daily data—weight, mood, meals, sleep, and more. This project uses **pandas**, **plotly**, **matplotlib**, **seaborn**, **scikit-learn**, and **wordcloud** to present insights about your health and daily habits.



## Table of Contents



1. [Project Overview](https://chatgpt.com/c/67888c4b-bbc4-800d-99d4-f73c88385192#project-overview)

2. [Features](https://chatgpt.com/c/67888c4b-bbc4-800d-99d4-f73c88385192#features)

3. [Project Structure](https://chatgpt.com/c/67888c4b-bbc4-800d-99d4-f73c88385192#project-structure)

4. [Installation & Setup](https://chatgpt.com/c/67888c4b-bbc4-800d-99d4-f73c88385192#installation--setup)

5. [Usage](https://chatgpt.com/c/67888c4b-bbc4-800d-99d4-f73c88385192#usage)

6. [Future Developments](https://chatgpt.com/c/67888c4b-bbc4-800d-99d4-f73c88385192#future-developments)

7. [License (MIT)](https://chatgpt.com/c/67888c4b-bbc4-800d-99d4-f73c88385192#license-mit)



---



## Project Overview



This dashboard helps you visualize various personal metrics—such as **Weight**, **Feelings (Morning/Evening)**, **Sleep**, **Meals**, **Weather**, and more—on a daily, weekly, and monthly basis. It also includes correlation analyses, word clouds for categorical data (e.g., meals, weather), and a basic “optimal values” section that uses polynomial regression to suggest an ideal sleep duration for improved mood.



---



## Features



1. **Daily (Overall) Overview**

    

    - Line charts for Weight, Feel Morning, Feel Evening, Feel Average, and Air Quality.

    - Phrase Clouds for Weather, Breakfast, Lunch, and Dinner.

    - Bar chart for Sleep Duration and Sleep Debt.

2. **Weekly Overview**

    

    - Aggregated weekly charts showing the same metrics as Daily, but averaged or summed by week.

3. **Monthly Overview**

    

    - Similar to Weekly but aggregated by month.

4. **Correlation Analysis**

    

    - Correlation matrix (heatmap) for numeric features.

    - Scatter plots with an optional regression line for any selected pair of variables.

5. **Optimal Values**

    

    - A simple polynomial regression model that estimates the optimal Sleep Duration for maximizing Feel Average.



---



## Project Structure



```

├── app.py               # Main Streamlit application

├── daily_data.csv       # Example CSV file with user data

├── README.md            # Project README

└── requirements.txt     # (Optional) List of Python dependencies

```



- **app.py**: Contains the primary code for loading, preprocessing, and visualizing data in Streamlit.

- **daily_data.csv**: Example data file; replace or update it with your own.

- **requirements.txt**: (Optional) For listing dependencies.



---



## Installation & Setup



1. **Clone this repository** or download the source code.

    

2. **Install Python 3.7+** (if not already installed).

    

3. **Install dependencies**:

    

    ```bash

    pip install streamlit pandas numpy plotly seaborn matplotlib scikit-learn wordcloud

    ```

    

    Or, if you have a `requirements.txt`, do:

    

    ```bash

    pip install -r requirements.txt

    ```

    

4. **Place your CSV data** (e.g., `daily_data.csv`) in the project root or edit the file path in `app.py` to point to your data.

    



---



## Usage



1. **Open a terminal** in the project directory.

    

2. **Run Streamlit**:

    

    ```bash

    streamlit run app.py

    ```

    

3. **Open your browser** (if it doesn’t open automatically) at the address shown in your terminal, typically `http://localhost:8501`.

    

4. **Explore the dashboard**:

    

    - **Daily (Overall) Overview**: Detailed daily charts and phrase clouds.

    - **Weekly Overview**: Aggregated weekly charts.

    - **Monthly Overview**: Aggregated monthly charts.

    - **Correlation**: Correlation matrix and scatter plots.

    - **Optimal Values**: Displays a polynomial regression to find an approximate best sleep duration for mood.



---



## Future Developments



You might consider enhancing this project with:



- **Time-Series Forecasting** (Prophet, ARIMA, LSTM) to predict future weight, mood, or sleep.

- **Anomaly Detection** (Isolation Forest, DBSCAN) to spot unusual days.

- **Mood Prediction** with Machine Learning (Random Forest, Gradient Boosting, etc.).

- **Nutritional Analysis** if you add calorie or macro data for each meal.

- **Goal/Target Tracking** for personal habit consistency.

- **Advanced Weather Integration** (e.g., temperature, humidity, AQI) for deeper correlation insights.

---



Thank you for exploring this Personal Health & Wellness Dashboard. If you have any questions, issues, or suggestions, feel free to open an issue or submit a pull request! Enjoy your data exploration.