I’ll be sharing a large dataset—well over 10 k rows—in both CSV and Excel formats. My priority is to turn this raw material into an analysis-ready asset and an easy-to-digest set of insights. Here’s what I need: • Rigorous data cleaning in Python (Pandas preferred): deal with missing values, detect and handle outliers, and standardise any inconsistent formats or units. • A structured exploratory data analysis that surfaces patterns, trends, and correlations, using familiar tools such as Matplotlib or Seaborn. • Clear, well-labelled visualisations that tell the story without overwhelming the reader. • A concise written summary that explains the findings in plain language. Deliverables: 1. Cleaned dataset saved back to both CSV and Excel. 2. Reproducible Jupyter Notebook (or .py script) documenting each step. 3. 5–10 key charts exported as image files. 4. Brief insight report (Markdown or PDF). Keep the workflow transparent and well-commented so I can re-run it on future data drops. Let me know your turnaround time and any questions you have.