Diabetes Predictor
- Web application that predicts the chances of a person getting diabetes using the Random Forest Classifier algorithm.
- The application takes in eight inputs: Number of Pregnancies, Glucose Level, Blood Pressure, Skin Thickness, Insulin, Body Mass Index (BMI), Diabetes Pedigree Function, and Age. The user can adjust these inputs using sliders provided in the web interface.
- Once the user inputs are provided, the model predicts the chances of the person getting diabetes. The predicted result is displayed on the web interface as well as advice on how to stay healthy based on the predicted result.
- The web application is built using the Streamlit library, which provides a simple way to build web applications in Python. It also makes use of the Pandas library for data manipulation, the Scikit-learn library for the Random Forest Classifier algorithm, and the StandardScaler function for data scaling.
- The web application is designed to be user-friendly and interactive, with expandable sections and slider inputs. It provides valuable insights to users on how to stay healthy based on their predicted chances of getting diabetes.
