Attached is a qualitative data set on presenters

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Attached is a qualitative data set on presenters. It may look familiar, since it's edited answers to a question I asked you a few weeks ago about who was a good presenter and what made them good. Use this data to provide a quantitative answer the question, "What are the three most common features that are mentioned in the answers?" There are ~60 entries; you can take a sample of at least 30 if you so choose. Two points for describing the method us used to answer this. One point for uploading the quantitative data set you created (i.e., the one you use in part 2 below). One point for listing the answer. Create one quantitative visualization of the data using Python. (This is the only part of the assignment that requires using Python. The other parts you can use Python or not, your call.) This does NOT have to visualize your answer from [1]. For example, if you found ten features in the data, you don't have to limit yourself to the three you listed in [1]. Three points; one for the code and 2 for the graph. Create one alternative visualization of the dataset. You have two options here. You can make: A different type of data visualization entirely. That need not be done in Python. You could even do something by hand and photograph it. A variation of the graph you created in [2] using Python. If you pick this option, the goal is to show me that you can manipulate the graph using code. Your data visualizations in [2] and [3] need to differ in at least three parts of the graph (e.g., different colours, aspect ratio, legend placement, fonts in labels, etc.) Three points, one for each difference. To help marking, make a note about what the differences are. Upload all of these in a single PowerPoint or Word file. Checklist: Description of how you analyzed data. The quantitative data you extracted. The three most common features of good speakers. Copy of Python code that created data visualization #1. (This can be a Colab notebook or text.) Data visualization #1. Data visualization #2.