Overview: This project aims to predict lifestyle diseases at an early stage using supervised machine learning techniques. By analyzing symptoms and health-related data, the models provide predictions for potential lifestyle diseases, enabling proactive healthcare measures.
About: The project leverages supervised machine learning algorithms (Random Forest, Naive Bayes, SVM) to predict lifestyle diseases based on collected symptom data. The Flask web application serves as the interface for users to input symptoms and receive disease predictions.
Features: Predicts lifestyle diseases based on symptoms provided by the user. Utilizes Random Forest, Naive Bayes, and SVM models for prediction. Provides a user-friendly interface for entering symptoms and viewing predictions.
Models and Data: The machine learning models used for prediction (Random Forest, Naive Bayes, SVM) are trained on a dataset containing symptom data and corresponding lifestyle diseases. Pickled models and necessary components are stored in the models.pkl file.
Contributing: Contributions are welcome! If you'd like to contribute to this project, feel free to submit a pull request or open an issue for discussion.
License: This project is licensed under the MIT License.