This project is designed to predict potential diseases based on three symptoms provided by the user. The system analyzes the symptoms and predicts which disease might be present, along with a description of the disease. This project demonstrates how machine learning can be used to help in early disease detection.
The project is built using Flask for creating a web-based application. The purpose of this project is purely academic, aiming to demonstrate disease prediction and symptom analysis.
- Anaconda Environment with Python 3.10 version
- Required Libraries: All required Python libraries can be installed via the
requirements.txtfile.
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Create an Anaconda environment with Python 3.10:
conda create --name disease-prediction python=3.10
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Activate the environment:
conda activate disease-prediction
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Install the required dependencies using pip:
pip install -r requirements.txt
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Run the Flask application by executing the following command:
python app.py
This will start the Flask server, and you can access the application in your browser at
http://127.0.0.1:5000.
The dataset used in this project is available on Kaggle. It contains various symptoms and their corresponding diseases, allowing the system to make predictions based on the symptoms provided by the user.
- Dataset source: The dataset can be found on Kaggle (no direct link available).
- The dataset used for training the prediction model is available on Kaggle.
- This project was built for academic purposes to demonstrate disease prediction using machine learning and web development.
This project is for academic purposes only. Feel free to use the code for educational purposes and modify it as needed.