I’m working through a series of college assignments that move from raw, messy data all the way to polished, interactive dashboards in Power BI. The data sets combine both numbers and free-text fields, so the very first step is a solid cleaning routine in Python using Pandas. Once the data is tidy, I have to model it logically—setting relationships, defining calculated columns or measures—and finally translate everything into a clear, insight-driven Power BI report. Here’s what I need from you: • A well-commented Python/Pandas notebook (or script) that imports the mixed data, handles missing values and inconsistencies, and outputs a clean table ready for analysis. • A concise explanation (inline comments are fine) of the transformations you apply, so I can reference them in my write-up. • A simple yet scalable data model that links the cleaned tables correctly and highlights any assumptions. • A Power BI Desktop (.pbix) file featuring interactive visuals—think slicers, drill-downs, and a couple of DAX measures—to demonstrate key insights. The assignments are graded on clarity and reproducibility, so all steps must be replicable on another machine running the latest versions of Python, Pandas, and Power BI Desktop. If this sounds like your wheelhouse, let’s get started right away—my submission deadline is approaching fast.