Mastering Web Scraping: Building a Product Price Analysis Dashboard
Web scraping stands as one of the most powerful tools for extracting information from the vast expanse of the internet. It’s an essential skill for data analysts who know where data exists but struggle to access it efficiently. While AI tools like ChatGPT can assist with information gathering, web scraping offers precision and the satisfaction of building something yourself.
This article outlines the first phase of building a product dashboard that analyzes data from Amazon and eBay, processes this information, and visualizes valuable insights including popular products, highest rated items, and even price predictions.
The Technical Framework
The project combines web scraping, data analysis, and predictive modeling to evaluate product prices across Amazon and eBay. Here’s a breakdown of the key libraries used:
- Requests and Beautiful Soup: Used for gathering information from websites
- Selenium: Helps automate browsing on Amazon to mimic human behavior and avoid bot detection
- Pandas: Handles data manipulation and analysis
- Matplotlib: Creates data visualizations
- Scikit-learn (SK learn): Implements linear regression models for price predictions
- NumPy: Provides support for mathematical operations
Implementation Process
The implementation begins with setting up request headers that simulate a real browser to avoid being blocked by websites. The code is structured into several key functions:
- A function to scrape eBay using Requests and Beautiful Soup, storing the results in a products variable
- A dedicated function for Amazon scraping that utilizes Selenium for timing control, making the scraping process appear more like a regular user’s activity
- Visualization functions that transform the collected data into informative charts and diagrams
- A price prediction function that analyzes recent price data to forecast potential future prices
Initial Results and Future Development
In this first phase, the code searches for products with the exact term “headphones” across both platforms. The initial visualization shows price consistency over time for this specific search.
The next development phase will expand the functionality to include products containing “headphone” as part of their name, not just exact matches. This refinement will provide a more comprehensive dataset for analysis.
Additionally, the follow-up work will focus on breaking down prices in more detail and building a complete dashboard that displays all products, their price histories, and predictive insights in an intuitive interface.
Conclusion
This project demonstrates how web scraping can be leveraged to create valuable business intelligence tools. By combining web scraping with data analysis and predictive modeling, it’s possible to gain insights into product pricing trends across multiple e-commerce platforms.
The approach outlined here goes beyond simple data collection, showing how extracted information can be transformed into actionable business intelligence through visualization and prediction techniques.