Expense Dashboard

A browser-only dashboard that turns an Excel/CSV expense export into a visual breakdown of where money is going — and flags the categories that are eating up a disproportionate share of spend so you can rein them in.

Live on GitHub Pages once merged to the default branch; open index.html directly (or serve the folder) to run it locally. All parsing happens client-side with SheetJS — no file is ever uploaded to a server.

Using it

1. Open the page and click Upload Excel / CSV (or drag a file onto the drop zone). .xlsx, .xls and .csv are supported.
2. The app guesses which columns are Date, Category, Description and Amount. Fix the mapping if needed, then click Build dashboard.
3. No file handy? Click Try sample data to explore the dashboard with a built-in example dataset.

What the dashboard shows

• Summary stats — total spend, transaction count, average transaction, top category, and how many categories are flagged.
• Major spend alerts — any category consuming more than a configurable share of total spend (default 15%, adjustable via the slider) is called out with its amount, share of total, and transaction count.
• Category charts — a bar chart ranking categories by spend and a doughnut chart showing each category's share of the total.
• Monthly trend — a line chart of total spend per month, and a month filter to drill into any single month.
• Top transactions — the 15 largest individual transactions.
• Category table — every category with its totals and a status badge (Under control / Watch / Needs control) based on the threshold.

Expected input format

Any spreadsheet with at least a numeric amount column works; a Date and Category column make the dashboard far more useful. Typical headers:

Date	Category	Description	Amount
2026-06-02	Rent	Monthly rent	32000
2026-06-03	Groceries	Supermarket order	3200

Notes:

• Amounts are treated as expenses regardless of sign (negative/parenthesized values are read as positive spend), so keep income rows out of the file.
• Dates in YYYY-MM-DD, DD/MM/YYYY or native Excel date cells are all supported.