ML Project

Project Title

Predictive Health Diagnosis System

Project Description

This machine learning project aims to develop a system that predicts diseases based on user-provided symptoms. The system will ask users about their symptoms and use a trained machine learning model to predict possible diseases. This project will leverage data preprocessing, feature selection, and classification algorithms to provide accurate health diagnostics.

Features

• User Interface: Simple and interactive interface to input symptoms.
• Disease Prediction: Utilizes machine learning models to predict diseases based on input symptoms.
• Data Handling: Efficient handling and processing of user input data.
• Model Training: Uses datasets of symptoms and diseases to train the model.

Technologies Used

• Programming Language: Python
• Libraries:
  • scikit-learn
  • pandas
  • numpy
  • Flask (for web interface)
• Database: SQLite for storing user input and model data

Project Structure

.
|-- data
|   |-- symptoms_diseases.csv
|
|-- models
|   |-- trained_model.pkl
|
|-- app
|   |-- templates
|   |   |-- index.html
|   |
|   |-- static
|   |   |-- css
|   |   |   |-- style.css
|
|-- main.py
|-- requirements.txt
|-- README.md

Installation

1. Clone the repository:

   git clone https://github.com/username/utsav_pal_ml_project.git
   

2. Navigate to the project directory:

   cd utsav_pal_ml_project
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

1. Run the application:

   python app.py
   

2. Open a web browser and go to http://localhost:5000.
3. Enter symptoms as prompted and receive predicted diseases.

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature_branch
   

3. Commit your changes:

   git commit -m 'Add some feature'
   

4. Push to the branch:

   git push origin feature_branch
   

5. Open a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for more details.

Acknowledgments

• Inspiration for this project came from the need to provide accessible health diagnostics.
• Data used for training the model is sourced from publicly available health datasets.

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Devloper :- utsav pal here 1