Health Data Visualizer 📊

A clean and intuitive web application for visualizing health-related data. Upload your CSV files and explore your data through interactive charts, correlation analysis, and statistical summaries.

Features

• Easy File Upload: Simple drag-and-drop CSV file upload
• Interactive Visualizations: Create bar charts, line charts, scatter plots, and histograms
• Correlation Analysis: Visualize relationships between numeric variables with heatmaps
• Statistical Summary: View comprehensive statistics including mean, standard deviation, min, max, and missing values
• Responsive Design: Clean, modern interface with soothing colors
• Real-time Updates: Instant visualization updates as you change parameters

Demo

Live Demo

Installation

Prerequisites

• Python 3.8 or higher
• pip (Python package installer)

Local Setup

1. Clone the repository:

git clone https://github.com/RyanSantoshJoseph/Health-Data-Visualizer.git

2. Navigate into the cloned repository:

cd Health-Data-Visualizer

3. Create a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

4. Install dependencies:

pip install -r requirements.txt

5. Run the app:

streamlit run app.py

6. Open your browser and navigate to http://localhost:8501

Usage

1. Upload Data: Click on "Choose a CSV file" and upload your health dataset
2. Preview: View the first 10 rows of your data
3. Create Visualizations:
   • Select X-axis (categorical data)
   • Select Y-axis (numeric data)
   • Choose chart type (Bar, Line, Scatter, or Histogram)
4. Analyze Correlations: Scroll down to see the correlation heatmap for numeric variables
5. Review Statistics: Check summary statistics including missing values

Sample Data Format

Your CSV file should contain health-related data with:

• Text/Categorical columns: Patient ID, Gender, Diagnosis, etc.
• Numeric columns: Age, Blood Pressure, Glucose Level, BMI, etc.

Example:

Patient_ID,Age,Blood_Pressure,Glucose,BMI,Diagnosis
P001,45,120,95,24.5,Normal
P002,52,140,110,28.3,Pre-diabetic
P003,38,115,88,22.1,Normal

Technologies Used

• Streamlit: Web application framework
• Pandas: Data manipulation and analysis
• Plotly: Interactive data visualization
• Python: Core programming language

File Structure

Health-Data-Visualizer/
├── app.py                 # Main application file
├── requirements.txt       # Python dependencies
├── README.md             # Project documentation
└── .gitignore            # Git ignore file

Dependencies

streamlit
pandas
plotly

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

1. Fork the project
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

Project Link: https://github.com/RyanSantoshJoseph/Health-Data-Visualizer

Support

If you find this project helpful, please give it a ⭐️!

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