Customer Data Analysis in Python
Budget: ₹12,500 – ₹37,500 INR
I need a concise, well-documented Python workflow that turns raw customer data into clear, decision-ready insights. The files arrive as CSV exports from our CRM and contain purchase history, demographic fields, and basic engagement metrics. I want the code to:
• load and clean the datasets (handle missing values, standardise field names)
• explore key patterns—frequency of purchase, average order value, cohort retention, and any other meaningful customer behaviour metrics
• generate charts or interactive visuals that I can drop straight into presentations (matplotlib, seaborn or Plotly are fine)
• export a short written summary of findings, ideally as a Markdown or PDF report
Pandas, NumPy, and a mainstream visualisation library are expected; if you prefer a Jupyter Notebook that’s perfect, but a standalone .py script with comments also works. Keep the workflow modular so I can feed in new monthly data without rewriting sections.
Once delivered, I should be able to run everything locally with a single command, point it at fresh CSVs, and receive both the cleaned dataset and the updated analytical outputs.
• load and clean the datasets (handle missing values, standardise field names)
• explore key patterns—frequency of purchase, average order value, cohort retention, and any other meaningful customer behaviour metrics
• generate charts or interactive visuals that I can drop straight into presentations (matplotlib, seaborn or Plotly are fine)
• export a short written summary of findings, ideally as a Markdown or PDF report
Pandas, NumPy, and a mainstream visualisation library are expected; if you prefer a Jupyter Notebook that’s perfect, but a standalone .py script with comments also works. Keep the workflow modular so I can feed in new monthly data without rewriting sections.
Once delivered, I should be able to run everything locally with a single command, point it at fresh CSVs, and receive both the cleaned dataset and the updated analytical outputs.