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Top 25 Web Scraping Project Ideas for 2025

25 powerful web scraping project ideas with tools, tips, and use cases—perfect for beginners to advanced developers looking to build real-world scrapers.
20 min read
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Antonello Zanini
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In this blog post, you will discover:

  • Whether now is a good time to start a web scraping project
  • What technology stack you should use
  • 25 web scraping project ideas to help you start with a solid plan

Let’s dive in!

Is Developing a Web Scraping Project a Good Idea?

It has been almost a decade since The Economist published the article “The world’s most valuable resource is no longer oil, but data.” At the time, that was a bold claim. Nearly ten years later, it feels almost obvious.

Data is money, and it is no surprise that many of the world’s most valuable companies by market cap—like Google, Meta, Amazon, and Apple—are all deeply connected to data. Similarly, many startups, especially in the AI space, have built their success by quietly scraping web data and using it to train powerful models.

So, do we really need more proof that it is always a good time to start a web scraping project? Just look at how many companies have built their fortune around data—the answer is a resounding yes.

Now, you might be wondering what the best web scraping project ideas are. Well, that is exactly what this article is about—so keep reading!

Best Programming Languages and Stacks for Web Scraping

As we have already covered, Python and JavaScript are often considered the best languages for web scraping. That is because they are beginner-friendly, have strong community support, and offer a wide range of libraries tailored for scraping tasks.

That said, there is no one-size-fits-all stack for web scraping. The libraries, tools, and services you should use depend on the type of website you’re targeting. Below is a quick summary:

  • Static sites: ****Use an HTTP client like Requests or Axios along with an HTML parser like Beautiful Soup or Cheerio.
  • Dynamic sites: ****Use browser automation tools such as Playwright, Selenium, or Puppeteer.

Additionally, you can integrate:

  • AI models to simplify data parsing
  • Proxies to avoid IP bans
  • CAPTCHA solvers for advanced scraping challenges
  • And more…

For more in-depth web scraping guides and recommended tech stacks, refer to the following resources:

Best Web Scraping Project Ideas

Explore 25 of the most exciting projects on web scraping for this year. For each project, you will find a brief description followed by:

  • Level: Whether the project is for beginner, intermediate, or advanced web scraping users.
  • Examples: Real-world websites and applications where this scraping technique applies.
  • Recommended tools: A curated list of open-source libraries and premium tools to help you extract the data of interest.
  • Further reading: Links to helpful guides, articles, and tutorials to deepen your understanding of how to build the specific web scraping project.

Ready to get inspired? Let’s dig into some cool web scraping ideas!

Note: The web scraping projects listed below are in random order. Feel free to pick one and get motivated by the one you prefer!

Project #1: Automated Product Price Comparison

The idea here is to build a web scraper that tracks product prices across multiple online stores. The goal is to monitor price fluctuations over time to understand inflation and economic trends, or simply find the best deals.

By scraping e-commerce websites like Amazon, eBay, and Walmart, the price monitoring scraper can track product prices and shipping costs. Users should also be able to set up alerts for price drops, making it easier to make informed purchasing decisions.

🎯 Level: Intermediate to Advanced

🧪 Examples:

  • PriceGrabber
  • Shopzilla
  • camelcamelcamel.com

🛠️ Recommended tools:

🔗 Further reading:

Project #2: News Aggregation

A news aggregator scrapes headlines, article summaries, or full articles from multiple online news sources. Then, it presents them to users based on their specific preferences and configurations. Such an application targets particular topics, keywords, or categories from top news sites and extracts content either programmatically or using AI-powered content parsing.

By aggregating news content, users can analyze media trends, track breaking stories, or feed the data into a recommendation engine. Keep in mind that several popular news aggregators already exist, as this is one of the most common and widely built web scraping project ideas.

🎯 Level: Intermediate

🧪 Examples:

  • SQUID
  • Flipboard
  • NewsBreak

🛠️ Recommended tools:

🔗 Further reading:

Project #3: Job Search Portal Builder

This web scraping project involves collecting job listings from popular job search platforms like LinkedIn and Indeed. The goal is to create a tool that pulls job postings based on user-defined criteria such as location, industry, job title, and salary range.

With that data, you can build a job portal that aggregates job postings for all industries or focuses on a specific niche. Users could then use that platform to search for job opportunities, receive personalized recommendations based on their profiles or preferences, and analyze job market trends to make informed career decisions.

🎯 Level: Intermediate to Advanced

🧪 Examples:

  • Indeed
  • Hiring Cafe
  • Simplify Jobs

🛠️ Recommended tools:

🔗 Further reading:

Project #4: Flight Ticket Monitoring

This project involves creating a web scraper to track flight ticket prices, availability, and more from various airlines and travel websites. Flight data changes frequently based on factors like availability, demand, season, and weather. Therefore, the scraper should be fast enough to collect real-time pricing data.

A real-world flight ticket monitoring tool should also include advanced features for analysis, such as allowing users to track price fluctuations over time, take advantage of the best deals, and set up email or notification alerts.

🎯 Level: Intermediate to Advanced

🧪 Examples:

  • Expedia
  • Google Flights
  • Skyscanner
  • Kayak

🛠️ Recommended tools:

🔗 Further reading:

Project #5: Movie/TV Series Recommendation

A movie/TV series recommendation system can be devised by scraping data from popular movie and TV show databases, such as IMDb, Rotten Tomatoes, or Metacritic. The scraper collects relevant information such as titles, genres, user ratings, reviews, and release dates.

This data can then be utilized to build a recommendation engine powered by machine learning, which suggests movies or TV shows based on a user’s watch history, ratings, or preferences.

🎯 Level: Intermediate

🧪 Examples:

  • MovieLens
  • OneMovie
  • Taste

🛠️ Recommended tools:

🔗 Further reading:

Project #6: Sports Players/Teams Analytics

This web scraping project requires you to retrieve data from sports and federation websites. What you need to do is build an application or service that tracks the performance of teams and individual athletes, including metrics such as assists, injuries, and other statistics.

By analyzing this sports data, users can gain insights into player performance trends, compare athletes and teams across seasons, and predict future performance. Note that this concept can be applied to multiple sports, from basketball to soccer, boxing to tennis.

🎯 Level: Beginner

🧪 Examples:

  • Sports-Reference.com
  • Transfermarkt
  • Basketball-Reference.com

🛠️ Recommended tools:

🔗 Further reading:

Project #7: Equity Research and Stock Market Scanning

A popular web scraping project idea is gathering financial and equity data from stock market platforms, brokers, or official market websites. What you should do is develop a scraper that tracks and analyzes key metrics such as stock prices, earnings reports, market trends, P/E ratios, dividend yields, and more.

By collecting that data, users can analyze investment opportunities, track stock performance, and monitor the financial health of companies over time. Such a tool would be especially valuable for stock traders, investors, financial analysts, or anyone looking to make informed decisions based on market data.

🎯 Level: Intermediate to Advanced

🧪 Examples:

  • Investopedia
  • MarketWatch
  • TipRanks

🛠️ Recommended tools:

🔗 Further reading:

Project #8: SERP Scraping for RAG

Finding high-quality data for RAG (Retrieval-Augmented Generation) pipelines is not always easy. That is why many AI models rely on a simple but effective approach: feeding the model with the top search results from Google or other major search engines for a specific keyword.

Scraping SERPs (Search Engine Results Pages) is a powerful way to gather fresh, relevant web content for RAG systems—or any other application that needs data from trusted sources. The idea is to extract URLs, page titles, snippets, and even full-page content from sources like Google, Bing, DuckDuckGo, and other search engines.

This scraped data can fuel AI assistants, question-answering bots, or knowledge retrieval systems with up-to-date and contextually rich information.

🎯 Level: Advanced

🧪 Examples:

  • Perplexity
  • Google AI Overview
  • AI search agents

🛠️ Recommended tools:

🔗 Further reading:

Project #9: Travel Itinerary Generator

Travel data is available on multiple websites, including TripAdvisor, Yelp, Airbnb, Expedia, and Google Maps. By retrieving that data with a custom scraper, you could automatically generate travel itineraries for your users.

The goal is to scrape information on attractions, hotels, restaurants, and activities in a specified destination. By integrating traffic data from Google Maps, you can organize that information into a structured itinerary based on user preferences such as budget, duration, and interests.

Users could use such a platform to plan their trips, discover uncommon destinations, and create custom itineraries tailored to their travel needs.

🎯 Level: Intermediate to Advanced

🧪 Examples:

  • Wanderlog
  • TripIt

🛠️ Recommended tools:

🔗 Further reading:

Project #10: GitHub Repository and Codebase Retriever

This project asks you to create an automated script to gather metadata and code snippets from public GitHub repositories. The information you could scrape includes repository names, descriptions, stars, forks, contributors, languages used, README contents, and even code files.

That data is important for developers seeking inspiration, performing competitive analysis, or building datasets for machine learning or AI. Also, it also enables you to track and identify the best projects for specific domains like web development, data science, or DevOps.

Note that similar web scraping project ideas can be implemented for Bitbucket, GitLab, and other platforms.

🎯 Level: Intermediate

🧪 Examples:

  • Awesome Lists
  • GitHub Star History
  • GitHub Stats Generator

🛠️ Recommended tools:

🔗 Further reading:

Project #11: Online Game Review Analysis

The current project is about collecting user reviews and ratings from platforms like Steam, Metacritic, IGN, and similar game portals. That data can be used to analyze sentiments, detect trends, and gain insights about popular games or gaming genres.

By processing a large volume of reviews, you can uncover recurring themes such as performance issues, gameplay highlights, or overall user satisfaction. These insights can help inform purchasing decisions, track industry trends, or power personalized game recommendations.

🎯 Level: Beginner

🧪 Examples:

  • SteamDB
  • CriticDB

🛠️ Recommended tools:

🔗 Further reading:

Project #12: Web Scraping Crypto Prices

This project focuses on developing a web scraping bot that automatically collects cryptocurrency prices from exchanges and financial sites like CoinMarketCap, CoinGecko, or Binance. The scraper helps track price fluctuations, trading volumes, and market trends in real time.

With that data, users can analyze crypto performance, detect market movements, or power automated trading strategies. This type of web scraping project is especially useful for crypto investors, analysts, and developers building dashboards or financial tools. Note that a similar logic can also be applied for NFT scraping.

🎯 Level: Intermediate to Advanced

🧪 Examples:

  • CryptoCompare.com
  • Kraken

🛠️ Recommended tools:

🔗 Further reading:

Project #13: Book Recommendation System

A book recommendation system can be effectively built using web scraping. All you need is an automated script that collects book data—such as titles, authors, genres, user ratings, and reviews—from online bookstores, review platforms, or public catalogs.

The scraped data can then be used to power a machine learning–based recommendation engine that suggests books based on user preferences, reading history, or overall popularity trends. This type of scraping project provides readers with personalized recommendations. Additionally, it can be beneficial for developers exploring machine learning or recommender systems.

🎯 Level: Intermediate

🧪 Examples:

  • Goodreads
  • Bookshelf
  • StoryGraph
  • Bookly

🛠️ Recommended tools:

🔗 Further reading:

Project #14: Political Data Analytics

This scraper should retrieve data from government websites, political news outlets, election result pages, or social media platforms. The data to retrieve includes political trends, public sentiment, and election dynamics.

The objective is to build tools that help visualize or predict shifts in public opinion, voter behavior, or campaign effectiveness. By aggregating and analyzing this information, researchers, journalists, or just regular citizens can gain deeper insights into the political landscape.

Data scientists and web developers could also use that data to power dashboards and predictive models.

🎯 Level: Beginner to Intermediate

🧪 Examples:

  • 270toWin
  • PDI

🛠️ Recommended tools:

🔗 Further reading:

Project #15: Hotel Pricing Analytics

The idea behind this web scraping project is to automatically collect hotel room prices from booking platforms and hotel sites. The ultimate goal is to build a monitoring application that shows how prices change based on factors like location, season, demand, and availability.

Users could analyze price trends over time, compare rates across different platforms, and even forecast future prices. This is especially useful for budget travelers, travel bloggers, or businesses that want to integrate pricing intelligence into their services.

🎯 Level: Beginner

🧪 Examples: ]

  • Booking.com
  • Airbnb
  • Hotels.com
  • Agoda

🛠️ Recommended tools:

🔗 Further reading:

Project #16: Recipe Recommendation System

We have all found ourselves with an empty stomach and a nearly empty fridge, wondering, “What can we make with what we’ve got?” AI could help, but only if it has been trained with recipe data from popular recipe websites like Allrecipes, Food Network, or Epicurious.

The objective is to create a recommendation system that suggests recipes to users based on the ingredients they have on hand, dietary restrictions, preferred cuisines, or meal types. By scraping recipe details such as ingredients, instructions, ratings, and nutritional information, you can feed this data into a recommendation engine.

Users will be able to search for recipes based on their preferences, create shopping lists, and even get suggestions for meals based on the ingredients they already have in their fridge.

🎯 Level: Beginner to Intermediate

🧪 Examples:

  • SuperCook
  • RecipeRadar

🛠️ Recommended tools:

  • Beautiful Soup
  • Puppeteer
  • TensorFlow or PyTorch for deep learning-based recommendation systems

🔗 Further reading:

Project #17: Event Aggregator for Local Meetups and Conferences

This web scraping project idea involves extracting event data from local meetup platforms, conference websites, event listings, or even social media channels. The objective is to aggregate events based on user preferences such as location, industry, date, and ticket availability.

By collecting this data, users can browse upcoming events, receive personalized recommendations, and even track conferences or networking opportunities in their fields of interest.

🎯 Level: Intermediate

🧪 Examples:

  • Meetup.com
  • Eventbrite

🛠️ Recommended tools:

🔗 Further reading:

Project #18: Company Financials Analysis

This scraping project involves scraping financial data from company reports, earnings statements, or financial news sources. The objective is to track and analyze key financial metrics such as revenue, profit margins, stock performance, and market trends.

By collecting this data, users can build financial models, analyze investment opportunities, and track the financial health of companies over time. Such an application would support financial analysts, angel investors, venture capitalists, or business professionals who want to stay updated with market performance.

🎯 Level: Beginner to Intermediate

🧪 Examples:

  • AngelList
  • Golden Seeds
  • Wefunder

🛠️ Recommended tools:

🔗 Further reading:

Project #19: Real Estate Market Analyzer

The idea here is to scrape data from real estate platforms and local MLS (Multiple Listing Service) listings. What you want to do is collect property information—such as prices, square footage, amenities, location, historical trends, and neighborhood data. You can then build a real estate exploration dashboard or analysis tool.

Your scraper should also be able to monitor property listings in real time, compare market prices across regions, and detect trends like emerging neighborhoods or price fluctuations. With this data, users can make informed decisions about buying, selling, or investing in property.

🎯 Level: Intermediate

🧪 Examples:

  • Zillow
  • Redfin
  • Idealista

🛠️ Recommended tools:

🔗 Further reading:

Project #20: Customer Review Analysis

A web scraping project that involves retrieving customer reviews from e-commerce platforms, review sites, or app stores. In this case, the scraper should extract details such as star ratings, review content, timestamps, and product names.

The collected data can then be analyzed to gain insights into user satisfaction, product performance, and overall sentiment. By applying NLP techniques, businesses and developers can identify trends, detect recurring issues, and make informed improvements and decisions.

🎯 Level: Beginner to Intermediate

🧪 Examples:

  • Birdeye
  • Tagembed
  • Reviewgrower
  • Review Bot

🛠️ Recommended tools:

🔗 Further reading:

Project #21: Social Media Analytics Tool

Social media platforms like X, Reddit, Instagram, and LinkedIn are rich sources of data on trends, hashtags, sentiment, and audience engagement.

What you should do is develop a scraper that collects public posts, comments, likes, shares, and follower statistics. Then, organize and visualize that data to monitor brand sentiment, track viral topics, or measure the impact of marketing campaigns across different platforms.

Such a tool would be especially valuable for marketers, researchers, influencers, and startups seeking insights from social media.

🎯 Level: Intermediate to Advanced

🧪 Examples:

  • Streamlit
  • Socialinsider

🛠️ Recommended tools:

🔗 Further reading:

Project #22: Influencer Database

This web scraping project idea is about gathering data from social media platforms to create a database of influencers. The social media should collect information such as names, social media handles, follower counts, engagement metrics, niches, and geographical locations.

Marketers or agencies can then take advantage of that data to identify the right influencers for campaigns or analyze influencer trends. Platforms to scrape data from include TikTok, YouTube, Facebook, Instagram, X, Reddit, and others.

🎯 Level: Intermediate

🧪 Examples:

  • Social Blade
  • Upfluence
  • AspireIQ

🛠️ Recommended tools:

🔗 Further reading:

Project #23: Research Paper Tracker

Artificial intelligence is not just a trend but a rapidly evolving scientific field. The same goes for data science and other scientific domains. The idea behind this project on web scraping is to retrieve academic papers and preprints from platforms like arXiv, Google Scholar, ResearchGate, and similar.

The goal is to build a tracker that keeps users updated with the latest publications, trends, and breakthroughs. Using that data, users could filter papers by topic, build a personalized reading list, or receive alerts for specific subfields like NLP, computer vision, or generative AI.

🎯 Level: Beginner

🧪 Examples:

  • Papers With Code

🛠️ Recommended tools:

🔗 Further reading:

Project #24: Language Learning Resource Hub

Learning a new language takes time—and the right resources. This web scraping project idea involves creating a centralized hub with content from language learning platforms, blogs, forums, and video sites.

Key resources in that area would be grammar tips, vocabulary lists, pronunciation guides, learning challenges, and media recommendations like videos or podcasts.

With that data, you are equipping learners with a curated feed of language resources tailored to their level, language of interest, or learning style. That is how you can build a tool for language learning students and educators.

🎯 Level: Beginner

🧪 Examples:

  • FluentU
  • Refold

🛠️ Recommended tools:

🔗 Further reading:

Project #25: Volunteer Opportunities Aggregator

There are thousands of non-profit organizations, charity websites, and volunteer platforms worldwide. This web scraping project involves collecting data from those sources and aggregating it into a centralized portal.

With the collected volunteer openings, users can search for opportunities based on their preferences—such as location, time commitment, skillset, and interests. Users could also receive personalized recommendations and track opportunities by deadline, organization, or cause.

🎯 Level: Beginner

🧪 Examples:

  • Idealist
  • VolunteerMatch

🛠️ Recommended tools:

  • Scrapy
  • BeautifulSoup
  • Python Requests

🔗 Further reading:

Conclusion

In this piece, you saw several cool web scraping project ideas. One thing all these projects have in common is that most target websites implement anti-scraping measures, such as:

  • IP bans
  • CAPTCHAs
  • Advanced anti-bot detection systems
  • Browser and TLS fingerprinting

These are just a few of the challenges that web scrapers encounter regularly. Overcome them all with Bright Data’s services:

  • Proxy services: Several types of proxies to bypass geo-restrictions, featuring 150M+ IPs.
  • Scraping Browser: A Playright, Selenium-, Puppeter-compatible browser with built-in unlocking capabilities.
  • Web Scraper APIs: Pre-configured APIs for extracting structured data from 100+ major domains.
  • Web Unlocker: An all-in-one API that handles site unlocking on sites with anti-bot protections.
  • SERP API: A specialized API that unlocks search engine results and extracts complete SERP data.

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