/diabetes-detection

Identify patients who have diabetes, based on diagnostic measurements. (Supervised Learning Task)

Primary LanguageJupyter Notebook

Project Captone: Supervised Learning

Building a Diabetes Detection System

Install

This project requires Python 2.7 and the following Python libraries installed:

You will also need to have software installed to run and execute an iPython Notebook

It is recommended that you install Anaconda, a pre-packaged Python distribution that contains all of the necessary libraries and software for this project.

Code

Code has been created in the notebook diabetes_detection.ipynb

Run

In a terminal or command window, navigate to the top-level project directory capstone_project/ (that contains this README) and run one of the following commands:

ipython notebook diabetes_detection.ipynb
jupyter notebook diabetes_detection.ipynb

This will open the iPython Notebook software and project file in your browser.

Data

The dataset used in this project is included as diabetes.csv. This dataset has the following attributes:

  • pregnancies : Number of times pregnant
  • glucose : Plasma glucose concentration a 2 hours in an oral glucose tolerance test
  • blood_pressure : Diastolic blood pressure (mm Hg)
  • skin_thickness : Triceps skin fold thickness (mm)
  • insulin : 2-Hour serum insulin (mu U/ml)
  • bmi : Body mass index (weight in kg/(height in m)^2)
  • diabetes_pedigree_function : Diabetes pedigree function
  • age : Age (years)
  • outcome : Class variable (0 or 1)