# Interpretation of PCA visualisation

I am trying to build a classifier to predict the ratings of a show during a specific time.

I have extracted around 109 features, some relating to the time field namely,

• Day of Year
• Month of year
• Is on a weekend?
• Public Holiday?

I also included some categorical features and used a label binariser for which channel it appeared on, and the broadcaster.

I wanted to check the linearity of the dataset, which would inform me as to whether a linear regressor could be used or something non-linear like a neural network. I decided to do dimensionality reduction using PCA in order to visualise if the dataset was linearly separable in 2D.

from sklearn.decomposition import PCA
pca = PCA(n_components=2)
data_scaled = pca.fit_transform(df[cols])
plt.plot(data_scaled[:,0], data_scaled[:,1], 'ro')
plt.xlabel('first component')
plt.ylabel('second component')
plt.show()


I am very confused by the result and am not able to interpret this.

The plot of the first component:

The plot of the second component:

What could the PCA results tell? What would cause these graphs?