This GitHub project hosts a predictive model for determining whether an individual is likely to have diabetes based on a dataset containing key health metrics. The dataset comprises four columns: Glucose concentration from an oral test within 2 hours, Insulin levels within 2 hours, Age of the person, and an Outcome label indicating the presence or absence of diabetes (0 for No Diabetes and 1 for Diabetes).
The primary goal of this project is to build and evaluate predictive models that can accurately predict the likelihood of diabetes using two out of the three available features: Glucose, Insulin, and Age. Through the use of machine learning algorithms, we aim to empower healthcare professionals and individuals with a tool that can assist in early diabetes detection, leading to more proactive health management and improved overall well-being.
We provide comprehensive data exploration and analysis to gain insights into the characteristics of the dataset.
You can experiment with different combinations of the three features (Glucose, Insulin, and Age) to determine which pair results in the most accurate predictions.
We offer a variety of machine learning algorithms and techniques for building predictive models, allowing you to compare their performance.
We provide robust evaluation metrics to assess the performance of your chosen model(s) and help you make informed decisions.
Visualize the data, model performance, and results to gain a deeper understanding of the predictions.