📖 Project Overview: BayeSurgeon

Welcome to BayeSurgeon, a hackathon project on Breath Cancer Prediction and Dataset Visualization that combines age, gender, surgeon type, and other professional factors to predict the chances of survivial based on patients' health data. We aim to make clinicians' life easier! By providing a data-driven prediction of the patient's status, clinical strategies can be improved and optimized. This project is powered by Python (Flask), React.js, and Machine Learing Strategies, and it’s all tied together with some cat-tastic vibes 🐱.

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🛠️ Tech Stack

• Backend: Flask (Python) for handling API call and model predictions.
• Frontend: React.js with Tailwind CSS for styling.
• Machine Learning Model: Support Vector Machine trained on BRCA data.

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🚀 Features

• 📊 Calculate patients'chance of survival based on data like:
  • Age, Gender
  • Surgeon Type, Tumor Stage
• 💻 User-friendly interface to display correlation of breast cancer survival rates and related factors based on BRCA dataset.

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📂 File Structure

Here’s how the project is organized:

project/
├── backend/
│   ├── app.py                  # Main Flask app
│   ├── model.pkl               # Trained Machine Learning Model on the Breast Cancer Dataset
├── frontend/
│   ├── src/
│   │   ├── components/         # React components (Input Form, Dashboard Charts, etc.) && POST call to the trained model
│       App.js                  # Main app entry point

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⚙️ Installation Guide

1. Clone the Repository

git clone https://github.com/yourusername/purrfect-odds.git
cd purrfect-odds

2. Backend Setup

Navigate to the backend/ folder:

cd backend/

Install dependencies:

pip install -r requirements.txt

Start the Flask server:

python app.py

3. Frontend Setup

Navigate to the frontend/ folder:

cd ../frontend/

Install dependencies:

npm install

Start the development server:

npm start

The app will be available at http://localhost:3000.

📃 License

This project is open-source under the MIT License.