Drug Effectiveness Predictor

This project is a Streamlit application designed to predict the effectiveness of a drug based on its name and the condition it treats. The application uses an XGBoost model to make these predictions.

Note: The predictions are based on a simplified model and should be taken with caution. The model uses limited information and random values for many features, which may affect the accuracy of the predictions.

Features

• Predicts drug effectiveness based on the drug name and the condition.
• Provides a visual representation of the predicted effectiveness.
• Includes a sidebar with information about the limitations of the model.

Installation

Prerequisites

• Python 3.6 or higher
• Streamlit
• NumPy
• pandas
• joblib
• xgboost
• matplotlib

Steps

1. Clone the repository: bash git clone <repository-url> cd <[repository-folder]>

2. Install the required packages: bash pip install -r requirements.txt

3. Ensure you have the following files in your project directory:

   • xgboost_model.joblib: The pre-trained XGBoost model.
   • Drug.csv: The dataset containing drug names and conditions.

4. Run the Streamlit application: bash streamlit run app.py

Usage

1. Open the application in your web browser. The default URL is http://localhost:8501.
2. Enter the drug name and the condition in the input fields.
3. Click the "Predict" button to see the predicted effectiveness.

Hosting

Local Server

To host the application on a local server, follow the installation steps above and run the application using the streamlit run app.py command.

Cloud Deployment

You can deploy the application on various cloud platforms like Heroku, AWS, GCP, or Azure. Here is a general guide using Heroku:

1. Install the Heroku CLI: bash curl https://cli-assets.heroku.com/install.sh | sh

2. Log in to your Heroku account: bash heroku login

3. Create a new Heroku app: bash heroku create your-app-name

4. Add a Procfile to your project directory with the following content:

   web: streamlit run app.py

5. Commit your changes: bash git add . git commit -m "Initial commit"

6. Deploy the app to Heroku: bash git push heroku main

7. Open your app in the browser: bash heroku open

Youtube Demo of Application

https://www.youtube.com/watch?v=c6eVNhdf1U0

License

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgements

• This application uses the XGBoost library for machine learning.
• Streamlit is used for creating the web interface.
• Thanks to the contributors of the open-source libraries used in this project.