DESCRIPTION
Problem Statement
NIDDK (National Institute of Diabetes and Digestive and Kidney Diseases) research creates knowledge about and treatments for the most chronic, costly, and consequential diseases.
The dataset used in this project is originally from NIDDK. The objective is to predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.
Build a model to accurately predict whether the patients in the dataset have diabetes or not.
Dataset Description
The datasets consists of several medical predictor variables and one target variable (Outcome). Predictor variables includes the number of pregnancies the patient has had, their BMI, insulin level, age, and more.
Variables Description
Pregnancies Number of times pregnant
Glucose Plasma glucose concentration in an oral glucose tolerance test
BloodPressure Diastolic blood pressure (mm Hg)
SkinThickness Triceps skinfold thickness (mm)
Insulin Two hour serum insulin
BMI Body Mass Index
DiabetesPedigreeFunction Diabetes pedigree function
Age Age in years
Outcome Class variable (either 0 or 1). 268 of 768 values are 1, and the others are 0
Inspiration
Predict whether or not a patient has diabetes, based on certain diagnostic measurements included in the dataset.
Build a model to accurately predict whether the patients in the dataset have diabetes or not.