Disease Prediction ML Model App

This repository contains a machine learning model integrated into a Streamlit web application. The model predicts diseases based on input symptoms and provides treatment recommendations.

Files and Directories:

• symptom_precaution.csv: Dataset containing symptom-precaution pairs.
• symptom_Description.csv: Dataset containing symptom descriptions.
• model.cbm: Trained machine learning model.
• model_learning.ipynb: Jupyter notebook for model training.
• dataset_diseases.csv: Dataset with information on diseases.
• app.py: Main application file using Streamlit.
• ai_assistent.jpeg: Image file.
• catboost_info: Directory containing model information.

Usage:

1. Install the necessary dependencies by running pip install -r requirements.txt.
2. Run the application using streamlit run app.py.
3. Open the application in your web browser.

How It Works:

• The application takes input symptoms from the user.
• The machine learning model processes the symptoms and predicts potential diseases.
• Recommendations for treatment are provided based on the predicted disease.

Contribution:

Feel free to contribute to this project by opening issues or creating pull requests. Your input is highly valued!