model_save_for_pima_diabetes
In this project,
1- We simply apply Multilayer Perceptron (MLP) Algorithm and save the model to model.json file. We saved the model by calling the save() function on the model and specifying the filename.
In our case, we demonstrates this by first fitting a model, evaluating it, and saving it to the file model.h5.
2- The saved model can then be loaded later by calling the load_model() function and passing the filename.
The function returns the model with the same architecture and weights.
In our case, we load the model, summarize the architecture and evaluate it on the same dataset to confirm the weights and architecture are the same.
3- Moreoer, we'll use Decision Tree and Support Vector Machine (SVM) algorithms for the prediction of diabetic in pregnant women using R Programming.
The plots are created while the deployement on R.