This repository contains a Flask and machine learning-based web application that predicts the possibility of multiple diseases. It leverages a trained machine learning model to provide accurate predictions based on input data.
- Predicts the possibility of multiple diseases based on input data.
- User-friendly web interface for easy interaction.
- Efficient and accurate machine learning model.
- Easy to deploy and run.
To run this application locally, follow these steps:
- Clone the repository:
git clone https://github.com/your-username/flask-ml-disease-prediction.git
cd flask-ml-disease-prediction- Create and activate a virtual environment (optional but recommended):
python3 -m venv venv
source venv/bin/activate- Install the required dependencies:
pip install -r requirements.txt- Run the Flask application:
python app.pyThe application will be accessible at http://localhost:5000 in your browser.
-
Open the web application in your browser by visiting http://localhost:5000.
-
Fill in the necessary information in the input fields provided.
-
Click the
Predictbutton to obtain the prediction for the possibility of diseases. -
The application will display the predicted diseases along with their corresponding probabilities.
The following technologies and libraries were used to develop this application:
- Python
- Flask
- HTML/CSS/JavaScript
- Machine Learning (ML) libraries (e.g., scikit-learn, TensorFlow, PyTorch)
The application employs a carefully curated dataset that was used to train the machine learning model. The dataset contains relevant features and labeled instances of various diseases.
The machine learning model was trained on the provided dataset using appropriate algorithms and techniques. The training process involved feature engineering, model selection, and performance optimization.
This project is licensed under the MIT License.
For any questions or inquiries, please contact:
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent