🫀 Project_Exb_1 – Multiple Disease Prediction Web App

<- click here to check out the DEMO.

An end-to-end web application for predicting multiple diseases using machine learning, complete with datasets, trained models, Jupyter notebooks, and an interactive frontend. This project also includes documentation and presentation materials for academic or demo purposes.

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📋 Project Structure

Project_Exb_1/
│
├── Datasets/        # Raw and processed datasets used for modeling  
├── Notebooks/       # Jupyter Notebooks: EDA, preprocessing, model training  
├── Paper/           # Written documentation of project methodology  
├── Presentation/    # Slides or visuals summarizing the project  
├── models/          # Saved trained models for inference  
├── static/          # Static assets: CSS, JavaScript, images  
├── templates/       # HTML templates for rendering web pages  
├── app.py           # Main backend application (Flask)  
└── flow.txt         # Outline of the project workflow

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💪 Features

• Predicts multiple diseases using ML models
• Interactive web interface (Flask + HTML templates)
• Organized project workflow with datasets, notebooks, and models
• Includes paper and presentation for academic submission
• Scalable architecture for adding more diseases in the future

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💻 Tech Stack

• Frontend: HTML, CSS, JavaScript
• Backend: Python (Flask)
• Machine Learning: scikit-learn, pandas, NumPy
• Visualization & Analysis: Jupyter Notebooks, matplotlib, seaborn

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⚡️ Getting Started

1⃣ Clone the repository

git clone https://github.com/mkp151203/Multiple_Disease_Prediction_Webapp.git
cd Multiple_Disease_Prediction_Webapp

2⃣ Install dependencies

pip install -r requirements.txt

(If requirements.txt is missing, install Flask, pandas, scikit-learn manually.)

3⃣ Run the backend server

python app.py

4⃣ Access the web app

Open your browser and go to:

http://localhost:5000

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📊 Workflow

• Load dataset → preprocess → train ML models → save models (.pkl)
• Web app loads saved models for predictions
• User inputs health parameters via frontend forms
• Predictions are displayed instantly on the web app

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🏰 Future Improvements

• Add more diseases for prediction
• Deploy on Heroku / AWS / GCP
• Enhance UI/UX with React or TailwindCSS
• Integrate real-time data from wearables

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📜 License

This project is licensed under the MIT License.

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✍️ Created with ❤️ by Mehul Kumar Patel