This project focuses on creating a web application using the Flask framework to assist users in predicting glucose levels and evaluating the risk of diabetes. The application is designed to provide two main functionalities:

1. Glucose Level Prediction:

   • Inputs: Users provide data on various health metrics, including:
     • Pregnancies: The number of times the user has been pregnant.
     • Blood Pressure: The user’s blood pressure measurement.
     • Skin Thickness: The thickness of the skin fold at the triceps, indicating body fat.
     • Insulin: The amount of insulin in the blood.
     • BMI (Body Mass Index): A measure of body fat based on height and weight.
     • Diabetes Pedigree Function: A score that indicates the likelihood of diabetes based on family history.
     • Age: The user’s age.
     • Outcome: A binary indicator (0 or 1) representing whether the user has diabetes.
   • Output: The app uses these inputs to predict the user’s glucose level, providing an estimate based on their health data.

2. Diabetes Risk Assessment:

   • Inputs: Similar to the glucose prediction feature, users input:
     • Pregnancies
     • Glucose: The user’s blood glucose level.
     • Blood Pressure
     • Skin Thickness
     • Insulin
     • BMI
     • Diabetes Pedigree Function
     • Age
   • Output: The app predicts the likelihood of the user having diabetes, giving a simple “Diabetes” or “No Diabetes” result based on the provided health metrics.

The application aims to offer an accessible and easy-to-use platform for individuals to monitor their health and understand potential diabetes risks. It leverages machine learning models trained on relevant health data to provide accurate predictions and insights.