Diabetes Prediction using Machine Learning: Predicting the presence of diabetes based on medical data, focusing on diagnostic measurements to improve health outcomes.

Introduction:

• Diabetes is a group of metabolic disorders characterized by prolonged high blood sugar levels.
• Common symptoms include frequent urination, increased thirst, and hunger.
• If untreated, diabetes can lead to acute complications like diabetic ketoacidosis and hyperosmolar hyperglycemic state, or even death.
• Long-term complications include cardiovascular disease, stroke, chronic kidney disease, foot ulcers, and eye damage.

Dataset Source:

• Data sourced from the National Institute of Diabetes and Digestive and Kidney Diseases.
• The dataset contains medical data for female patients of at least 21 years of age, specifically of Pima Indian heritage.
• The goal is to predict whether a patient has diabetes based on diagnostic measurements.

Objective:

• Build a machine learning model to predict the presence of diabetes in patients.

Dataset Details:

• The dataset includes medical predictor variables such as the number of pregnancies, BMI, insulin levels, age, and more.
• The target variable, Outcome, indicates whether the patient has diabetes or not.