Steele, e portfolio manager develops a machine learning $($ML$)$ model to predict downgrades of publicly traded firms by bond rating agencies. The model currently relies only on structured financial data collected from different sources. Steele plan to incorporate sentiment data derived from textual analysis of news articles and Twitter content relating to the subject companies. Steele believe that the preprocessing of the raw text data can be completed in the following steps

Step$1$: Cleanse the raw test data

Step$2$: Split the cleansed data into a collection of words from Step$2$ and create a distinct set of tokens from the normalized words.

Step$3$: Normalize the collection of words from Step$2$ and create a distinct set of tokens from the normalized words

With respect to Step$1,$ Steele believe that she should remove all html tags, punctuation, numbers and extra white spaces from the data before normalizing them. is Steele's belief regarding Step$1$ of the preprocessing of raw text data correct?

O Mes

No$,$ because her suggested treatment of punctuation is incorrect.

No$,$ because her suggested treatment of extra white spaces is incorrect.

Void