A practical data analytics project to measure how Daniel's real grocery costs change over time using receipts from Walmart, Publix, and other household grocery purchases.
Official inflation numbers are useful, but a family budget is shaped by specific stores, products, quantities, substitutions, and shopping habits. This project turns personal receipts into a practical household price index.
The project demonstrates a junior data analyst workflow: extract receipt data, normalize item names and unit sizes, compare prices over time, group by category, and publish clear visual summaries.
The visual below is a roadmap of dashboard modules, not fabricated results. Real charts will be added only after receipt data is cleaned and validated.
The first version focuses on a defensible pipeline rather than flashy charts. Data quality comes before conclusions.
Collect receipt data from Walmart, Publix, and other grocery purchases with dates, items, quantities, prices, and totals.
Clean item names, store names, categories, sizes, and units so the same product can be compared over time.
Calculate unit prices, basket changes, monthly trends, store differences, and category-level inflation pressure.
Publish dashboard views that explain the results clearly without exposing private receipt details.
Use the analysis to understand shopping changes, budget pressure, substitutions, and practical savings opportunities.
Public portfolio content will show methodology, anonymized examples, and aggregate insights only. Raw receipts and sensitive purchase details are not public.