In this project, we are going to use the Pima Indians onset of diabetes dataset. This is a standard machine learning dataset available for free download from the UCI Machine Learning repository. It describes patient medical record data for Pima Indians and whether they had an onset of diabetes within five years. It is a binary classification problem (onset of diabetes as 1 or not as 0). The input variables that describe each patient are numerical and have varying scales. Below lists the eight attributes for the dataset:
- Number of times pregnant.
- Plasma glucose concentration a 2 hours in an oral glucose tolerance test.
- Diastolic blood pressure (mm Hg).
- Triceps skin fold thickness (mm).
- 2-Hour serum insulin (mu U/ml).
- Body mass index.
- Diabetes pedigree function.
- Age (years).
- Class, onset of diabetes within five years.
The primary purpose in this project is learning how to create a sequential neural network and three main ways of evaluating the network such as automatic evaluation during learning, manual selection of data for assessing during training, and K-fold cross-validation.
tutorial source: Deep Learning With Python, Develop Deep Learning Models On Theano And TensorFlow Using Keras, Jason Brownlee