Summary In today's episode, I walk through part two of cleaning and prepping Twitter data for analysis, using a real dataset to demonstrate feature engineering, quality checks, and initial model testing. Here's what this means for you. You'll understand why thorough upfront data preparation saves significant time and money when running machine learning models. You'll…
Summary In today's episode, I explore how to define a bot on Twitter by inspecting the inner workings of an open-source machine learning package rather than blindly trusting its output. Here's what this means for you. You gain the critical habit of questioning every algorithm you encounter and recognizing that software encodes opinions just as…
David asks, “I’m working on a Twitter performance report. I’m trying to determine the best metric for demonstrating our performance (assuming the thing that matters for the moment is engagement, and not necessarily conversions). My feeling is that engagement rate could potentially be the most valuable – albeit imperfect – metric, since absolute numbers like…
