Sales Data Modeling & Visualization

About the project

I’ll hand over the raw sales exports—mostly CSVs plus a couple of Excel files—and you’ll take it from messy to meaningful. First, clean and merge the data so duplicates, missing values, and wonky date formats are gone. From there, dive into a focused product performance analysis: show me which items pull in the most revenue, which ones are lagging, and any seasonality or demand patterns we can act on. Python is the backbone here, so work in Pandas, NumPy, and your preferred scikit-learn–compatible ML library to run exploratory analysis and build at least one predictive model that forecasts future sales or flags under-performers. Document each step in plain language; the report has to be friendly enough for non-technical stakeholders. Visuals are crucial. The final report must feature pie charts that break down revenue share and profit contribution by product category. If you think a supporting bar or line graph will sharpen the story, feel free to add it. I frequently query data in SQL and build dashboards in Power BI, so structure your outputs so I can drop them into those tools without a hitch. Deliverables: • Cleaned sales datasets (CSV and Excel) • A well-commented Jupyter notebook or .py script • Saved predictive model with a one-page rationale and metrics • PDF/PowerPoint report containing the visuals and clear, jargon-free insights • A short video or written walkthrough explaining the results When you reply, please share links or files from similar analytics work and an estimated timeline once you have the data. I’m ready to get started and can answer any structural questions right away.

Skills required