Steven Savarin

Project Overview

Web Scraping for Amazon Products is a Python project focused on extracting product information such as title, price, rating, review count, and availability.

The project leverages BeautifulSoup and Requests to automate the collection of structured data from Amazon product pages. Extracted data can then be processed with Pandas and NumPy for deeper analysis, reporting, or integration into larger systems.

This tool is ideal for researchers, e-commerce analysts, or developers who need to automate the collection of real-time product information.

Key Features

Automated Data Collection: Extracts product details without manual copy-paste
Structured Data: Retrieves title, price, ratings, reviews, and availability
Data Cleaning: Uses Pandas for preprocessing and organizing information
Scalable: Can be extended to scrape multiple categories or pages
Export Ready: Data can be saved in CSV or integrated into dashboards

Technologies Used

Python: Core language for implementation
BeautifulSoup: For parsing HTML and extracting product details
Requests: For sending HTTP requests and retrieving Amazon pages
Pandas: For organizing and analyzing collected data
NumPy: For numerical operations and data handling

Key Sections

Scraper Script: Extracts product data from Amazon pages
Data Cleaning: Normalizes prices, ratings, and review counts
Analysis Layer: Summarizes and organizes information for reports
Output: Saves cleaned data to CSV or JSON for easy use

Challenges & Solutions

Dynamic Content: Amazon frequently changes its structure; solved with flexible selectors
Rate Limiting: Added delays between requests to avoid being blocked
Data Consistency: Implemented cleaning functions for uniform formatting

Demo

👉 Check out the code on GitHub: Web Scraping Amazon

Web Scraping