E-commerce Price Comparison: Real-Time Price Checking with Python

Introduction:

Have you ever wondered if the “big discount” flashing on your screen is the best deal available?
I have. And like many shoppers today, I was tired of guessing.

Shopping in 2025 is a thrilling yet confusing experience. Prices bounce up and down, discounts come and go, and no one has time to compare dozens of pages every day.
So I decided to let Python do the hard work for me.

Project Overview:

It is no secret that online shopping platforms sometimes display wildly different prices for the same item. With dynamic pricing algorithms and personalized deals, prices for the same product often vary more than most people think. The truth is that comparing prices manually is time-consuming.

What if we could:

Extract live prices from different stores
Clean and match messy product names
Compare them instantly
See where the real deal is hiding

This brief project demonstrates how Python can be used to retrieve actual data and perform a price comparison.

Project Code

https://github.com/EvalynTheAnalyst/E-Commerce-Price-Comparison.git

Price Comparison Overview

https://e-commerce-price-comparison-3v4hksca8brzaudtn2jkuc.streamlit.app/#e-commerce-price-tracker

Methodology:

The solution uses Python and a few powerful libraries. The goal is simple: collect product details from two well-known e-commerce sites, clean the data, match similar products, and display the price comparison with clear visuals.

Here's a step-by-step breakdown:

Importing necessary Libraries:

The following libraries will be used to retrieve data from e-commerce sites, clean it, and visualize the price comparison.

2. Scraping Data

Using BeautifulSoup and requests, the script visits product pages and extracts information such as name, price, discount, and reviews.

3)Cleaning Data

Pandas cleans price data and handles missing values, crucial for reliable data analysis.

4) Matching Products

The same phone or accessory may appear with slightly different names on each site. RapidFuzz helps match them smartly using fuzzy matching.

5) Comparing Prices

Once matched, prices are compared to see which site offers a better deal for the same product.

6.) Visualizing Insights

Finally, the project wraps all this into a Streamlit app. With one click, it scrapes fresh data, cleans it, matches products, and shows a clear table and charts.

Features include:

Matched products with side-by-side prices
Histogram of price differences
Average price comparison
Top 10 largest price gaps
Option to download results as CSV

Conclusion:

In today’s market, algorithms quietly adjust prices all the time. Discounts can be real or just marketing. Reviews influence buying decisions, but sometimes the lowest price does not come with the best trust signals.

By scraping data responsibly and presenting it clearly, shoppers get back control. The tool does not rely on guesswork. It checks actual listings, compares the same products, and shows where you can save money. For anyone who runs an online store, this same idea can help keep an eye on the competition.