Healthcare Prediction Project

A predictive healthcare dashboard that uses machine learning to analyze patient data and predict health outcomes. This interactive dashboard allows users to visualize data, view model predictions, and understand patterns in healthcare metrics.

🚀 Live Demo

Explore the live version of the dashboard here: Healthcare Prediction Project on Streamlit

📖 Project Overview

This project leverages data analytics and machine learning to create a user-friendly dashboard for healthcare predictions. Using historical patient data, the app predicts health outcomes, providing valuable insights into patient risk factors and supporting healthcare decision-making.

The dashboard is built with Streamlit and includes:

• Data exploration and visualization tools.
• A machine learning model that predicts healthcare outcomes.
• An intuitive interface for interactive user experience.

🏗️ Features

• Data Visualization: Display patient demographics and health metrics to explore trends and distributions.
• Predictive Modeling: Uses a trained machine learning model to predict health outcomes based on input data.
• Interactive Interface: Built with Streamlit for seamless navigation and interaction.

🔧 Installation

To run this project locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/Pointfit/healthcare-prediction-project.git
   cd healthcare-prediction-project
   

2. Set up a virtual environment:

   python -m venv env
   

3. Activate the virtual environment:

   • Windows:

     env\Scripts\activate

   • Mac/Linux:

     source env/bin/activate

4. Install dependencies:

   pip install -r requirements.txt
   

5. Run the Streamlit app:

   streamlit run dashboard.py
   

📊 Model and Data

The model used in this project is a machine learning classifier trained to predict healthcare outcomes. It was trained on historical patient data, leveraging features such as demographics, health metrics, and clinical history to make predictions.

Key Libraries Used:

• Streamlit: For building the interactive web application.
• Pandas: For data manipulation and analysis.
• Matplotlib & Plotly: For data visualization.
• Scikit-learn: For model training and evaluation.

❓ FAQ

Q: How do I know what info to put into the prediction tool?

A: Just hover over the little question mark next to each input for more details.

🔗 Links

• Live App: Healthcare Prediction Project on Streamlit
• GitHub Repository: GitHub - Healthcare Prediction Project

👤 Author

Chris Chalfoun

• LinkedIn Profile