Agricultural Crop Yield Data Analysis

I have several seasons of raw crop-yield figures that need to be turned into clear, actionable insight. The core focus is on yield performance, but I also want to overlay price information drawn from two sources I already hold—government databases and a set of private market reports—to see how production trends line up with market movements.

Your assignment is to clean and normalise the data, run exploratory and comparative analyses, build meaningful visualisations, and hand back a concise, well-structured report that explains the key drivers you uncover. Python (Pandas, NumPy, Matplotlib, Seaborn) or R is fine, so long as everything is fully reproducible in a notebook or script.

Deliverables
• Cleaned, well-documented dataset
• Reproducible code/notebook of the full analysis
• 3–5 compelling charts or tables suitable for a presentation deck
• Brief written summary (around 2–3 pages) highlighting insights and recommendations

Please include examples of past work that demonstrate your experience handling similar agricultural or commodity data analyses; that portfolio will guide my selection.