Diabetes is a group of metabolic disorders characterized by high blood sugar levels over a prolonged period. Symptoms of high blood sugar include frequent urination, increased thirst, and increased hunger. If untreated, diabetes can lead to several complications, including diabetic ketoacidosis, hyperosmolar hyperglycemic state, cardiovascular disease, stroke, chronic kidney disease, foot ulcers, eye damage, or even death.

This dataset, originally from the National Institute of Diabetes and Digestive and Kidney Diseases, aims to diagnostically predict whether a patient has diabetes based on certain diagnostic measurements. All patients in this dataset are females of at least 21 years old from Pima Indian heritage.

Develop a machine learning model to accurately predict whether or not the patients in the dataset have diabetes.

The dataset includes several medical predictor variables and one target variable, Outcome. The predictor variables are:

Pregnancies: Number of times pregnant

Glucose: Plasma glucose concentration after 2 hours in an oral glucose tolerance test

BloodPressure: Diastolic blood pressure (mm Hg)

SkinThickness: Triceps skin fold thickness (mm)

Insulin: 2-hour serum insulin (mu U/ml)

BMI: Body mass index (weight in kg/(height in m)^2)

DiabetesPedigreeFunction: Diabetes pedigree function

Age: Age (years)

The target variable is:

Outcome: Class variable (0 or 1)

Number of observations: 768 Number of variables: 9

The model created as a result of XGBoost hyperparameter optimization achieved the lowest Cross Validation Score value of 0.90.

Check out the demo of the application here.

Exploratory Data Analysis

Data Preprocessing

Feature Engineering

One Hot Encoding

Base Models

Model Tuning

Comparison of Final Models

Reporting