Your question is Process Unstructured Text Datasets. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
You are working with a dataset made up of raw, unstructured text such as emails, support notes, PDFs converted to text, and free-form comments. Before you can model anything useful, you need to turn that text into structured signals that can support downstream tasks like labeling, search, routing, or analytics. A strong approach usually combines preprocessing, feature extraction, and task-specific modeling depending on what the business needs from the data.
How do you work with natural language processing on unstructured datasets?