Create an instance of StandardScaler called scaler.
Create a PCA instance called pca.
Use the make_pipeline() function to create a pipeline chaining scaler and pca.
Use the .fit() method of pipeline to fit it to the fish samples samples.
Extract the number of components used using the .n_components_ attribute of pca. Place this inside a range() function and store the result as features.
Use the plt.bar() function to plot the explained variances, with features on the x-axis and pca.explained_variance_ on the y-axis.

Dimension reduction with PCA