I have a set of finance-related CSV files that need to be explored, cleaned, and summarised. The goal is strictly descriptive analysis—think clear statistics, trends, and visual snapshots—without venturing into predictive modelling or prescriptive optimisation. All raw data will arrive as comma-separated files. You are free to use Python (pandas, NumPy, Matplotlib, Seaborn), R, Excel Power Query, or a comparable toolkit, as long as the workflow is reproducible and well-documented. Deliverables: • A concise cleaning script or notebook that imports each CSV, handles missing or inconsistent entries, and outputs a tidy dataset • A written summary (PDF or Markdown) of key descriptive metrics—averages, distributions, correlations, outliers—tailored to a finance context • At least three intuitive visualisations (e.g., time-series plots, bar charts, heatmaps) ready for presentation Acceptance criteria: the code must run end-to-end on my machine, all figures should render correctly, and the insights need to align with the numbers in the cleaned dataset. Once complete, please package the cleaned data, code, and report in a single zipped folder or share via a Git repository.