Health Analytics Dashboard

Overview

The Personal Health Analytics Dashboard is a simple web application that allows users to upload their health data (in CSV or Excel format) and predicts whether they will lose or gain weight, along with an estimate of the weight change in pounds. The app leverages Streamlit for an interactive front-end and employs basic linear regression ML models to provide predictions.

Features

• 📂 Upload Health Data: Users can upload CSV/Excel files containing health data.
• 📊 Data Visualization: Displays trends for sleep, step count, exercise minutes, and heart rate.
• 🔍 Weight Prediction: Uses a simple linear regression model to predict weight change.
• ⚡ Streamlit UI: A minimalistic and intuitive interface for user interaction.
• ☁️ Deployment Ready: Can be deployed on Streamlit Sharing, AWS, or Hugging Face Spaces.

Tech Stack

• Frontend: Streamlit
• Backend: Python, Pandas
• Machine Learning: Scikit-learn (Linear Regression)
• Data Handling: Pandas, NumPy

Installation

Prerequisites

Ensure you have Python 3.8+ installed.

Steps to Run Locally

1. Clone the repository:

   git clone https://github.com/nagakirankasi/Health-Analytics-Dashboard.git
   cd Personal-Health-Dashboard
   

2. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use 'venv\Scripts\activate'
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run the Streamlit app:

   streamlit run app.py
   

Usage

1. Upload your health data in CSV or Excel format.
2. The app will process the data and display visualizations for:
   • Sleep patterns
   • Step count trends
   • Exercise minutes
   • Heart rate changes
3. The ML model will predict:
   • Weight change (gain/loss) in pounds over the next month.
4. The results will be displayed with an interactive dashboard.

Model Details

• Algorithm Used: Linear Regression
• Features: Sleep Hours, Steps, Exercise Minutes, Heart Rate
• Target Variable: Weight Change (in lbs)
• Training Dataset: Public health datasets + synthetic data

Demo

https://huggingface.co/spaces/nkirankasi/HealthAnalyticsDashboard

Deployment

The app can be deployed using:

• Streamlit Community Cloud
• AWS Lambda
• Hugging Face Spaces

Contributing

Feel free to open issues or submit pull requests if you’d like to contribute!

License

This project is licensed under the MIT License.

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