Chapter 5: Artificial Neural Networks: Predict Annual Income

Activity 14: Training a Multilayer Perceptron for our Census Income Dataset

1. Using the preprocessed Census Income Dataset, separate the features from the target, creating the variables X and Y:

   X = data.drop("target", axis=1)
   Y = data["target"]

   As explained previously, there are several ways to achieve the separation of X and Y, and the main thing to consider is that X should contain the features for all instances, while Y should contain the class label of all instances.

2. Divide the dataset into training, validation, and testing sets, using a split ratio of 10%:

   from sklearn.model_selection import train_test_split
   X_new, X_test, Y_new, Y_test = train_test_split(X, Y, test_size=0.1, random_state=101)
   X_train, X_dev, Y_train, Y_dev = train_test_split(X_new, Y_new, test_size=0.1111, random_state=101)

   The shape of the sets created should be as follows:

   X_train = (26048, 9)
   X_dev = (3256, 9)
   X_test = ...