Elon's tweets

In this exercise, you will attempt the impossible: detecting patterns in Elon Musk's tweets!

You will apply two unsupervised learning algorithms:

Dimensionality reduction, to translate your text data into a 2D space.
Clustering, to find groups of similar tweets.

The go-to model for dimensionality reduction is Principal Component Analysis (PCA), while the KMeans algorithm represents the same in the domain of clustering.

Tweets in their raw form were loaded into the variable named tweets_raw.
They have also been translated into a machine-digestible, vectorized form, contained in the variable tweets_matrix.
To write less code, we want you to use the functions for combined fitting and transformation/prediction - .fit_transform() and .fit_predict()

Be aware that this is real data from Twitter and as such there is always a risk that it may contain profanity or other offensive content (in this exercise, and any following exercises that also use real Twitter data).

Set the algorithm for dimensionality reduction and the number of dimensions to 2.
Apply dimensionality reduction to the vectorized dataset tweets_matrix.
Configure the clustering model to find two clusters in the input data.
Find clusters within the reduced dataset and display the results. This latter step has already been done for you.

Exercise

Elon's tweets

Instructions