Data entry

I have several Excel workbooks containing historical NYC Airbnb listings and need them transformed into a clean, analysis-ready dataset that reveals clear, reliable averages for rental prices across boroughs, neighbourhoods, room types, and time periods.

Here’s the flow I have in mind. First, import every sheet from the workbooks, standardise column names, parse date fields, and strip the text noise often found in host names, neighbourhood strings, and price columns. Next, handle missing values thoughtfully—no blanket deletions—so the final table truly represents the market. Once the data are tidy, I want a descriptive analysis that answers, at minimum, “What has the average nightly price looked like over time, and how does it differ by location and listing category?”

Python with pandas is my preferred toolkit, but R (tidyverse) is fine if that’s your strength. Visualisations created in Matplotlib, Seaborn, or ggplot2 should accompany the summary so the insights are instantly readable.

Deliverables
• Cleaned master Excel/CSV file ready for further modelling
• Reproducible script or Jupyter Notebook with well-commented code
• Short slide deck or PDF highlighting key findings and price trends

Acceptance criteria: the notebook runs end-to-end on my machine without edits, numerical summaries match the visual output, and all steps—from raw import to final chart—are clearly documented. I’m open to bringing in supplementary CSV or JSON sources if you believe they’ll refine the story, but the core work must succeed with the Excel data already on hand.