E-commerce Price Monitoring Tool
Prashant Patil
E-commerce Price Monitoring Tool
Objective
Develop a robust and scalable tool to monitor and track product prices across multiple e-commerce platforms. The system will extract, store, and analyze pricing data, allowing users to stay updated on market trends, detect pricing anomalies, and make informed business decisions.
Key Features
1. Price Data Extraction
Scrape real-time product prices from multiple e-commerce websites like Amazon, eBay, Walmart, etc.
Support for dynamic and JavaScript-heavy websites using Selenium or Playwright.
Automate scraping schedules for periodic updates using task schedulers like Celery or Cron Jobs.
2. Product Information Tracking
Capture additional metadata such as product titles, descriptions, ratings, reviews, and stock availability.
Handle multiple product categories with structured output formats (e.g., JSON, CSV, database).
3. Historical Price Tracking
Maintain historical price trends in a database for analytical purposes.
Visualize trends using libraries like Matplotlib or Plotly.
4. Alert System
Set up user-defined price thresholds and trigger alerts via email, SMS, or notifications when conditions are met.
Integrate alert services using APIs like Twilio, SendGrid, or AWS SNS.
5. Data Dashboard
Create an intuitive web interface for users to view data insights using frameworks like Flask or Django.
Offer filtering options for products, prices, and time ranges.
6. Scalability
Use Scrapy for efficient crawling and handling of large-scale data scraping.
Employ cloud services (e.g., AWS EC2, Lambda) to manage high traffic and periodic data extraction.
Technology Stack
Programming Languages and Frameworks
Python: Core language for scraping, automation, and backend development.
Scrapy/Selenium/BeautifulSoup: For data scraping.
Flask/Django: For building the web interface and API.
Database and Storage
MySQL/PostgreSQL: For structured data storage.
MongoDB: For semi-structured data.
AWS S3: For storing large data exports.
Task Scheduling
Celery: For asynchronous tasks.
Redis: As a message broker.
Visualization
Plotly/Matplotlib: For generating graphs and charts.
Deployment
Docker: For containerized deployment.
AWS (EC2, Lambda, RDS): For scalable cloud infrastructure.
Like this project
Posted Dec 11, 2024
Prashant created an automated price tracking tool for an e-commerce platform using Django and BeautifulSoup.
