E-Commerce Price Tracker Project with Browser Extension and Price-Drop Alerts

A price tracker browser extension is a small browser add-on that watches e-commerce prices for you and tells you when to buy. This project builds that: PriceHawk reads the price on any supported product page, records one reading per check, and charts six months of daily history so inflated "sale" prices are visible at a glance. Shoppers lose money to fake discounts — a strikethrough MRP of ₹34,990 next to a ₹26,990 price looks like a deal until history shows the product sold for ₹22,742 two months earlier. Sites rarely publish that history, and prices can swing 10–20% in a week around festival sales. PriceHawk automates it: set a target price or a drop percentage once, and the extension checks in the background every six hours, then pushes a browser notification and email the moment your rule triggers. A dashboard ranks every tracked product by current price, lowest ever and 90-day average, turning bargain hunting from guesswork into data.

E-commerce price tracking is also a strong engineering topic: it combines browser extension development (Manifest V3, content scripts, background service workers), resilient web extraction across changing page markup, time-series storage in SQLite, canvas-rendered data visualisation, and a rule-based alerting engine with push and email delivery. The built-to-order build ships the extension, the dashboard web app, the checker backend and a complete report.

The pipeline runs in six stages. First, a content script injected into the product page extracts the price: the extractor keys off stable attributes like data-asin rather than fragile CSS class names, because marketplace markup changes weekly (a builder note from the demo build: Amazon serves different markup to headless clients, so the extractor never trusts class names). Second, the reading is sent to the backend, which validates it (rejecting obvious misreads like ₹0) and appends it to that product's time series in SQLite. Third, the dashboard aggregates the series on demand — lowest-ever, 90-day average, drop-from-peak — and renders the canvas chart. Fourth, after every scheduled check the alert engine evaluates each product's rules: below-target triggers immediately, and drop-percentage rules compare today's reading against yesterday's. Fifth, triggered rules produce a browser push notification via the Web Push API and queue an email through the digest sender. Sixth, the notification log records every firing for a full audit trail.

1. The user pastes a product URL or clicks the extension icon on a product page; the popup parses the URL pattern and registers the product.
2. A content script runs on the product page and extracts the price using data-attribute-keyed selectors, resilient to weekly markup changes.
3. The background service worker re-checks every tracked product every 6 hours and posts each reading to the backend API.
4. The backend validates each reading (rejecting ₹0 misreads), appends it to the product's 180-day time series in SQLite, and timestamps the check.
5. The dashboard aggregates lowest-ever, 90-day average and drop-from-peak, and renders the 181-point canvas chart with target and sale markers.
6. The alert engine evaluates every rule after each check — below-target and single-day drop-percentage — and fires matching ones.
7. Fired alerts go out as browser push notifications plus a queued email digest, and are recorded in the notification log.

Tech stack:

• JavaScript (ES2020), HTML5, CSS3
• Chrome Extension Manifest V3 (content scripts, service worker)
• Node.js + Express (checker API)
• SQLite (price time-series store)
• Canvas 2D (history chart rendering)
• Web Push API (browser notifications)
• Nodemailer (email digest)
• Git

PriceHawk's four headline capabilities are one-click tracking from any product page, a 180-day price-history chart, target-price and drop-percentage alert rules, and background scheduled checks. One-click tracking means pasting or auto-detecting a product URL adds it to the watchlist with its first price captured immediately — no account needed. The history chart renders six months of daily readings with the target-price line, lowest-ever line and sale-event markers, and hovering any point shows the exact price and date. Alert rules are the heart of the product: notify below a target price, or on any single-day drop above a chosen percentage, delivered by browser push and email digest. Behind it all, a background service worker runs the checker every six hours without the browser tab being open, keeping every series fresh — the same cadence a human would need but never sustain.

• [One-click product tracking] Paste a product URL (or click the extension on a product page) and the item joins the watchlist with its first price captured immediately; the demo parses Amazon /dp/, Flipkart /p/ and generic slugs.
• [180-day price-history chart] Canvas-rendered chart of daily readings with target-price line, lowest-ever line and sale markers; hover shows the exact price and day. The demo draws 181 points in ~5 ms.
• [Target-price alerts] Set a target price per product; the engine fires a browser push notification and queues an email the moment a check comes in below it.
• [Drop-percentage alerts] Get notified on any single-day drop above your threshold (e.g. ≥10%), catching flash sales even when the price stays above your target.
• [Background scheduled checks] A service worker re-checks every tracked product every 6 hours (4 checks/day) without any tab open; the dashboard shows last-check and next-check times.
• [Tracked-products dashboard] Every product with current price, 7-day change badge, lowest-ever, 90-day average and drop-from-peak in one sortable view.
• [Notification log] Full history of every alert fired — what dropped, to what price, when — with push + email delivery records.

• Browser extension source (Manifest V3: popup, content script, background service worker)
• Price-checker backend API (Node.js + Express) with SQLite schema
• Price-history dashboard web app with interactive canvas chart
• Alert engine with target-price and drop-percentage rules, push + email delivery
• Working single-file demo app with 4 tracked products and 180 days of simulated history
• Project report PDF (background, architecture, extraction strategy, evaluation)
• PPT presentation for final review
• Viva Q&A preparation document (extensions, service workers, web extraction, time-series storage)