In this chapter, we have designed and implemented an MLP that is capable of predicting the onset of diabetes with ~80% accuracy.
We first performed exploratory data analysis where we looked at the distribution of each variable, as well as the relationship between each variable and the target variable. We then performed data preprocessing to remove missing data and we also standardized our data such that each variable has a mean of 0 with unit standard deviation. Finally, we split our original data randomly into a training set, a validation set, and a testing set.
We then looked at the architecture of the MLP that we used, which consists of 2 hidden layers, with 32 nodes in the first hidden layer and 16 nodes in the second hidden layer. We then implemented this MLP in Keras using the sequential model, which allows us to stack layers on one another. We then trained our MLP...
