erdivyang10 / pima-indian-diabetes-model-using-keras

Diabetes Model Implemented in Keras Deep learning library. Here Pima-Indian-Diabetes data set is used.

pima-indian-diabetes-model-using-keras

Diabetes Model Implemented in Keras Deep learning library. Here Pima-Indian-Diabetes data set is used.

I have used Keras Sequential to create the model, https://keras.io/models/sequential/

Data set CSV file link : https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.data.csv

Data set Link: https://raw.githubusercontent.com/jbrownlee/Datasets/master/pima-indians-diabetes.names

There are eight input variables and one output variable (the last column) in the Dataset CSV file. We will be learning a model to map rows of input variables (X) to an output variable (y), which we often summarize as y = f(X).

Input Variable (X)

• 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 (weight in kg/(height in m)^2)
• Diabetes pedigree function
• Age (years)

Output Variables (y)

• Class variable (0 or 1)

Keras Diabetes Model Implementation Steps

1. Load Data.
2. Define Keras Model.
3. Compile Keras Model.
4. Fit Keras Model.
5. Evaluate Keras Model.
6. Make Predictions