wtf is diabetes-prediction-system?

This project is a machine learning app that estimates a patient's likelihood of having diabetes based on medical measurements such as glucose level, BMI, age, and insulin. It uses a decision tree classifier, a simple type of model that makes predictions by asking a series of yes or no questions about the input data, to sort a patient into a risk category. The repository itself is set up specifically to be deployed and hosted online, not to walk through the full process of building the model. The author trained the model separately in a Google Colab notebook, which is linked in the README, and that notebook is where the data exploration, feature selection, removal of unusual data points, and actual model training happen. This repository just contains the already trained model and the code needed to serve it as a live website. The web interface is built with Gradio, a tool that turns a Python script into a simple form-based website without needing separate frontend code. The trained model is saved with a library called Joblib and loaded back in when the app starts, and data handling in the background uses Pandas and NumPy, two standard Python libraries for working with tables of numbers. The README doubles as a tutorial for hosting your own version of a similar project for free. It walks through cloning the repository, training your own model in Colab, exporting it as a pkl file, updating the app's code to match your model's expected inputs, and then connecting the repository to Render, a cloud hosting service, to get it running on a public web address. A working demo of this specific diabetes predictor is already live and linked in the README. The README does not mention a license, so it is unclear under what terms the code can be reused.