Feature Engineering Made Easy

Applied facial recognition

Now, we can move on to the machine learning pipelines that will be used to create our facial recognition models:

1. We can start by creating train, test, and split in our dataset, as shown in the following code block:

# let's split our dataset into training and testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=1)

2. We are ready to perform a Principal Component Analysis (PCA) on our dataset. We will want to instantiate a PCA first and ensure that we scale our data before applying PCA in our pipeline. This can be done as follows:

# instantiate the PCA module
pca = PCA(n_components=200, whiten=True)

# create a pipeline called preprocessing that will scale data and ...