Overview

The Diabetes prediction dataset is a collection of medical and demographic data from patients, along with their diabetes status $($positive or negative$).$ The data includes features such as age, gender, body mass index $($BMI$),$ hypertension, heart disease, smoking history, HbA$1$c level, and blood glucose level. This dataset can be used to build machine learning models to predict diabetes in patients based on their medical history and demographic information. This can be useful for healthcare professionals in identifying patients who may be at risk of developing diabetes and in developing personalized treatment plans. Additionally, the dataset can be used by researchers to explore the relationships between various medical and demographic factors and the likelihood of developing diabetes.

If this is your first time creating a competition, please refer to our Getting Started Guide for help. Here is a handy link to Kaggle's competition documentation, which includes, among other things, instructions on submitting predictions.

Goal: The goal of this competition is for you to practice what you have learned so far about classification techniques to develop a model capable of predicting whether or not a patient has diabetes based on patient demographics and medical data.

Evaluation

Submissions are evaluated on using the Brier Loss Score which is the mean$-$squared error where the error is measure as the difference between the true class labels and the predicted probability of the positive class. For more details on the Brier Loss Score, see the Scikit$-$Learn User Guide