I have several CSV files containing customer records gathered over time, and I need clear, actionable trend insights extracted from them. The work begins with consolidating the raw CSV files, cleaning inconsistent or missing values, and structuring the data so it is ready for analysis. From there, the core task is to uncover how key customer-related metrics have shifted—month-over-month, quarter-over-quarter, or year-over-year—and to highlight any significant growth or decline patterns that surface. Python with pandas (or R if you prefer) is perfectly fine for the data preparation steps; feel free to bring in SQL for joins or look-ups if it expedites the process. For the analysis itself, I expect visualisations—line graphs, heat-maps, or cohort-style charts—generated in Matplotlib, Seaborn, Power BI, Tableau, or a comparable tool. Choose whichever gives the clearest story and is easiest for me to update later. Deliverables • A cleaned, consolidated dataset in CSV format • An editable notebook or script that reproduces the cleaning and trend calculations • A concise report (PDF or slide deck) explaining the trends, complete with annotated charts and a short narrative of key findings Acceptance criteria • No missing or duplicate customer IDs in the final dataset • All steps fully reproducible from raw CSV import to final visuals • At least three meaningful trends called out with supporting evidence Once the files and report are validated on my end, the project is complete and payment will be released immediately.