Multi-Source Customer Trend Analysis

I have several years of customer-related data split across a range of CSV and Excel files. My goal is to combine these flat-file datasets, clean and normalise the fields, then surface clear purchasing-pattern trends that can guide upcoming marketing and stock decisions.

The work centres on:
• Consolidating and de-duplicating every CSV / XLS(X) source while preserving data integrity.
• Running exploratory analysis to spot seasonality, repeat-purchase cadence, product affinities and any other statistically significant trends.
• Visualising the findings in an intuitive format—interactive dashboards or well-annotated charts are both acceptable—as long as the insights are immediately actionable.
• Summarising key take-aways in a concise report that a non-technical stakeholder can grasp quickly.

Python (pandas, NumPy, matplotlib / seaborn), R (dplyr, ggplot2) or a comparable toolset is absolutely fine; the emphasis is on accuracy, reproducibility and clear storytelling through data. All original notebooks / scripts, cleaned datasets and visual assets should be handed over so that I can rerun or extend the analysis later on.