Multiple Disease Prediction System with Streamlit 🧬
Zain Manna
Project Description:
The project involves developing a web application for multiple disease predictions using machine learning models. It encompasses building a user-friendly interface that allows prediction for diabetes, heart disease, and Parkinson's disease based on input features tailored to each disease type.
Project Steps:
1. Model Training and Saving:
- Machine learning models were trained for diabetes, heart disease, and Parkinson's disease prediction using respective datasets.
- ML models: Support Vector Machine - Logistic Regression.
2. Web App Development:
- Utilized Streamlit to create a user interface with sidebar navigation for disease selection.
- Implemented specific input fields for each disease type to collect required features for prediction.
- Integrated functionality to load saved models using `pickle` for disease prediction based on user input.
3. UI Development and Deployment:
- Developed the user interface with Streamlit's sidebar for disease selection.
- Implemented conditional rendering to display disease-specific input fields.
- Integrated prediction logic to provide instant disease prediction results based on user input.
Tools Used:
- Python (Streamlit, pickle)
- Machine learning libraries (scikit-learn, pandas)
- Streamlit options menu for creating sidebar navigation
