In this section, we will predict diabetes using Classification and Regression Trees
📌 The aim is to transform the complex structures in the data set into simple decision structures.
📌 It is desired to develop a machine learning model that can predict whether people have diabetes when their characteristics are specified. You are expected to perform the necessary data analysis and feature engineering steps before developing the model.
📌 The dataset is part of the large dataset held at the National Institutes of Diabetes-Digestive-Kidney Diseases in the USA. Data used for diabetes research on Pima Indian women aged 21 and over living in Phoenix, the 5th largest city of the State of Arizona in the USA. The target variable is specified as "outcome"; 1 indicates positive diabetes test result, 0 indicates negative.
Pregnancies: Number of pregnancies
Glucose: 2-hour plasma glucose concentration in the oral glucose tolerance test
Blood Pressure: Blood Pressure (Small blood pressure) (mm Hg)
SkinThickness: Skin Thickness
Insulin: 2-hour serum insulin (mu U/ml)
DiabetesPedigreeFunction: Function (2 hour plasma glucose concentration in oral glucose tolerance test)
BMI: body mass index
Age: Age (years)
Outcome: Have the disease (1) or not (0)