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Home > Data Science > Web Scraping Projects & Topics For Beginners 
In this article, we’ll take a look at some exciting web scraping project ideas. We have assorted a list of multiple projects of various industries and skill levels to choose one according to your liking.
Web Scraping has many names, such as Web Harvesting, Screen Scraping, and others. It is a method of extracting large quantities of data from websites and storing it at a particular location (a local file in your computer or a database in a table).
What is Web Scraping? Why Perform Web Scraping? Web Scraping Projects1. Scrape a SubredditHow to work on this project2. Perform Consumer ResearchHow to work on this project3. Analyse CompetitorsHow to Work on This Project4. Use Web Scraping for SEOHow to work on this project5. Scrape Data of Sports TeamsHow to work on this project6. Get Financial DataHow to work on this projectScrape a Job PortalHow to work on this projectConclusionWhat is the difference between web crawling and web scraping? What are the essentials that must be kept in mind while creating a consumer research project? How can web scraping be used for SEO purposes?
What is Web Scraping?
Whenever you want any information, you Google it and go to the webpage, which offers the most relevant answer to your query. You can view the data you needed, but what if you need to save it locally? What if you want to see the data of a hundred more pages?
Most of the webpages present on the internet don’t offer the option to save the data present there locally. To keep it that way, you’ll have to copy and paste everything manually, which is very tedious. Moreover, when you have to save the data of hundreds (sometimes, thousands) of webpages, this task can seem strenuous. You might end up spending days just copy-pasting bits from different websites. Check out our website if you want to learn data science.
This is where web scraping comes in. It automates this process and helps you store all the required data with ease and in a small amount of time. For this purpose, many professionals use web scraping software or web scraping techniques.
Read more: Top 7 Data Extraction Tools in the Market
Why Perform Web Scraping?
In data science, to do anything, you need to have data at hand. To get that data, you’ll need to research the required sources, and web scraping helps you. Web scraping collects and categorizes all the required data in one accessible location. Researching with a single, convenient location is much more feasible and more comfortable than searching for everything one-by-one.
Just as data science is prevalent in many industries, web scraping is widespread too. When you take a look at the web scraping project ideas we’ve discussed here, you will notice how various industries use this technique for their benefit.
Now that you’re familiar with the basics of web scraping, we should start discussing web scraping projects too
Web Scraping Projects
The following are our web scraping project ideas. They are of different industries so that you can choose one according to your interests and expertise.
1. Scrape a Subreddit
Reddit is one of the most popular social media platforms out there. It has communities called subreddits, for nearly every topic you can imagine. From programming to World of Warcraft, there is a community for everything on Reddit. All of these communities are quite active, and their members (on a side note: Reddit’s users are called Redditors)share a lot of valuable information, opinions, and content.
Learn more: 17 Fun Social Media Project Ideas & Topics For Beginners
How to work on this project
Reddit’s thriving communities are a great place to try out your web scraping abilities. You can scrape its subreddits for particular topics and figure out what its users say about it (and how often they discuss it). For example, you can scrape the subreddit r/webdev, where web development professionals and enthusiasts discuss the various aspects of this field. You can scrap this subreddit for a particular topic (such as finding jobs).
This was just an example, and you can choose any subreddit and use it as your target.
This project is suitable for beginners. So, if you don’t have much experience using web scraping techniques, you should start with this one. You can modify the difficulty level of this project by selecting a smaller (or bigger) subreddit.
2. Perform Consumer Research
Consumer research is a vital aspect of marketing and product development. It helps a company understand what their targeted consumers want, whether their customers liked their product or not, and how the general public perceives their product or services. If you’d use your data science expertise in marketing, you’d have to perform consumer research many times.
Researching potential buyers helps a company in many ways. They get to know:
What are the likings of their prospective clients
What are the things their prospective customers hate
What products they use
What products they avoid
This is just the tip of the iceberg; consumer research (also known as consumer analysis) can cover many other areas.
To perform consumer research, you can gather data from customer review websites and social media sites. They are a great place to start with.
Here are some popular review sites where you can start to get the necessary data:
These are just a few names. Apart from these review sites, you can head to Facebook to gather links as well. If you find any blogs that cover your company’s products, then you can include them in your web scraping efforts as well. They are an excellent source for getting valuable insight.
Doing this project will help you in performing many other tasks in data science, particularly sentiment analysis. So, pick a brand (or a product) and start researching its reviews online.
Learn more: Data Analytics Is Disrupting These 4 Martech Roles
3. Analyse Competitors
Competitive analysis is one of the many aspects of digital marketing. It also requires data scientists and analysts’ expertise because they have to gather data and find what their competition is doing.
You can perform web scraping for competitive analysis too. Completing this project will help you considerably in understanding how this skill can help brands in digital marketing, one of the most crucial aspects in today’s world.
How to Work on This Project
First, you should choose an industry of your liking. You can start with car companies, teaching companies (such as upGrad), or any other. After that, you have to pick a brand for which you’ll analyze the competitors. We recommend starting with a small brand if you are a beginner because they have fewer competitors than major ones.
Once you’ve picked the brand, you should search for its competitors. You’ll have to scrape the web for their competitors, find what they sell, and how they target their audience. If you’ve picked a tiny brand and don’t know its competitors, you should search for its product categories. For example, if you picked Tata Motors as your brand, you’d search for a phrase similar to ‘buy cars in India. ’ The search result will show you many cars of different brands, all of which are competitors of Tata Motors.
You can build a scraping tool that analyses your selected brand’s competitors and shows the following data:
What are their products?
What are the prices of their products?
What are the offers on their products (or services)?
Are they offering something which your brand isn’t?
You can add more sections, depending on your level of expertise and skill. This list is just to give you an idea of what you should look for in your selected brand’s competitors.
Such web scraping is particularly beneficial for new and growing companies. If you aspire to work with startups in the future, this is the perfect project idea. To make this project more challenging, you can increase the number of competitors you want to analyze. If you’re a beginner, you can start with one or two competitors, whereas if you’re a little advanced, you can start with three or four competitors.
4. Use Web Scraping for SEO
Search Engine Optimization (also known as SEO) is the task of modifying a website, matching the preferences of search engines’ algorithms. As the number of internet users is steadily rising, the demand for effective SEO is also increasing. SEO impacts the rank of a website when a person searches for a particular keyword.
It is a humongous topic and requires a complete guide. All you need to know for SEO is that it requires specific criteria that a website has to fulfill. You can read more on SEO and what it is in our article on how to build an SEO strategy from scratch.
You can use web scraping for SEO and help websites ranking higher for keywords.
You can build a data scraping tool that scrapes your selected websites’ rankings for different keywords. The tool can extract the words these companies use to describe themselves too. You can use this technique for specific keywords and assort a list of websites. A marketing team can use this list to use the best keywords out of that list and help their website rank higher.
While this is a simple application of web scraping in SEO, you can make it more advanced. For example, you can create a similar tool but add the function of getting the metadata of those web pages. This would include the title of the web page (the text you see on the tab) and other relevant pieces of information.
On the other hand, you can build a web scraper that checks the word count of the different pages ranking for a keyword. This way you can understand the impact word count has on the ranking of a webpage
There are many ways to make a web scraper for SEO. You can take inspiration from Moz or Ahrefs and build an advanced web scraper yourself. There’s a lot of demand for useful web scraping tools in the SEO industry.
If you are interested in using your tech skills in digital marketing, this is an excellent project. It will make you familiar with the applications of data science in online marketing as well. Apart from that, you’ll also learn about the multiple methods of using web scraping for search engine optimization.
5. Scrape Data of Sports Teams
Are you a sports fan? If so, then this is the perfect project idea for you. You can use your knowledge of web scraping to scrape data from your favorite sports team and find some interesting insights. You can choose any team you like of any popular sports.
You can choose your favorite team and scrape the websites of their official website, the organization that handles their sports, and relevant archives. For example, if you’re a cricket fan, you can use ESPN’s cricket statistics database.
After you’ve scraped this data, you’d have all the required information on your favorite team. You can expand this project and add more teams in your collection to make this project a little more challenging.
However, this is among the most suitable web scraping projects for beginners. You can learn a lot about web scraping and its applications in a fun and exciting manner.
6. Get Financial Data
The finance sector uses a lot of data. Financial data is useful in many ways as it helps investors analyze a company’s performance and reliability. Similarly, it helps a company in analyzing its position and where it stands in terms of finances. If you want to use your knowledge of data and web scraping in the finance sector, then you should work on this project.
There are multiple ways to go about this project. You can start by scraping the web for the performance of a company’s stock in a set period and the news articles related to the company of that period. This data can help an investor figure out how different things affected that particular company’s stock price. Apart from that, this data will also help the investor understand what factors affect the company’s stock price, which factors don’t.
Financial statistics are crucial for any company’s health. They help the stakeholders of a company understand how well (or how badly) their business is performing. Financial data is always helpful, and this project will allow you to use your skills in this regard.
You can start with a single company initially and make the project more challenging by adding the data from more companies. However, if you want to focus on one particular company, you can increase the timeline and look at the data of a year or more.
Scrape a Job Portal
It is among the most popular web scraping project ideas. There are many job portals on the web, and if you’ve ever thought of using your expertise in data science in human resources, this is the right project for you.
There are many job portals online, and you can pick anyone for this project. Here are some places to get you started:
In this project, you can build a tool that scrapes a job portal (or multiple job portals) and checks the requirements of a particular job. For example, you can look at all the ‘data analyst’ jobs present in a job portal and analyze its job requirements to see the most popular criteria for hiring one such professional.
You can add more jobs or portals in your search to add more difficulty to this project. It’s a fantastic project for anyone who wants to apply data science in management and relevant streams.
Also Read: Data Science Project Ideas & Topics
We hope you found this list of web scraping project ideas useful and exciting. If you have any thoughts or suggestions on this article or topic, feel free to let us know. On the other hand, if you want to learn more, you should head to our blog to find many relevant and valuable resources.
You can enroll in a data science course as well to get a more individualized learning experience. A course can help you learn all the important topics and concepts in a personalized approach so you can be job-ready in very little time.
If you are curious to learn about data science, check out IIIT-B & upGrad’s Executive PG Programm in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
What do you think of these project ideas? Which one of these ideas did you like the most? Let us know in the comments.
What is the difference between web crawling and web scraping?
Many people get confused between web crawling and web scraping and end up considering them as equivalent. Well, they are two separate terms with totally different meanings. The web crawler is artificial intelligence, also known as “the spider” that surfs the internet and searches the required content by following the links. Web scraping is the next step after web crawling. In web scraping, data is extracted automatically using artificial intelligence known as “scrapers”. This extracted data can be used for various processes like comparison, analysis, and verification based upon the client’s needs. It also allows you to store a large amount of data within a small amount of time.
What are the essentials that must be kept in mind while creating a consumer research project?
Consumer research is crucial for every product-based company and there are certain things that one must keep in mind while working on a project on consumer research. There is a lot more to research and analyze while working on a consumer research project. There are various websites that provide the necessary data on consumer preferences like Trustpilot, Yelp, GripeO, and BBB. Apart from these review sites, you can also visit Facebook to get the links.
How can web scraping be used for SEO purposes?
Search Engine Optimization or SEO is a process that improves the visibility of your site whenever someone’s search meets your website domain. For example, you have an e-commerce website and some search for a product that is available on your website as well as on your competitors’ websites. Now, whose website or webpage among you and your competitor will occur first will depend on the SEO. Web scraping can be used for SEO and help websites ranking higher for keywords. You can build a web scraper that checks the word count of the different pages ranking for a keyword. You can even add the functionality in your web scraper to get the meta description or metadata of those web pages.
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Web Scraping: Leave It All to AI or Add a Human Touch? – DZone Big Data
This article was originally written by Toni Matthews-El.
To say there’s a lot of data on the Internet is an understatement. As of 2020, it’s projected that the “digital universe” holds an estimated 40 trillion gigabytes or 40 zettabytes worth of information. To put this into perspective, a single zettabyte has enough data to fill data centers roughly one-fifth the size of Manhattan.
With such a vast amount of information available to analyze, it makes sense that so many tasks associated with gathering data get left to artificial intelligence. Bots can crawl through web pages at incredible speed, extracting as much relevant information as needed. And while many data scientists and marketers access and use this info in a perfectly ethical fashion, it’s an unfortunate fact that the growing presence of AI online brings with it a growing amount of stigma.
It would be easy to dismiss much of the negativity as an indirect result of Hollywood movies and sci-fi stories where AI is something to be wary of at the best of times. However, the consequence of unethical bot usage by certain web users means that there are crackdowns that affect even those who are working with data professionally and in good faith.
Web scraping remains an essential tool for many professionals, and especially AI. But what can be done about the bot-related stigma?
First, What Is Web Scraping?
For those just joining the conversation, the act of web scraping should be understood as data extraction. Although data scientists and other professionals use scraping to analyze very complex digital stacks of information, the act of copying and pasting text from a website could itself be considered a simple form of scraping.
But even if you can access every part of a website, there’s so much available information, it can take a very, very long time to gather data from just that source. For the most part, web scraping is left to AI, with humans then taking the retrieved data and thoroughly analyzing it for various purposes. But while this is a great convenience to the web scraper, website owners and onlookers are greatly concerned about the rampant use of AI in this way.
Is Web Scraping Better With Bots?
With so much information to analyze, it seems a no brainer turn to artificial intelligence (AI) to gather data. In fact, Google itself is one of the most trusted sources for providing web scraping tools to interested parties. For instance, you can use its dataset search engine to quickly access data deemed freely available for use. You can even customize your search to learn if the information is available for commercial use. All in a matter of a couple of seconds.
This wouldn’t be possible if Google AI wasn’t so incredibly efficient at examining every website within its reach for relevant data. It’s a perfect example of using AI to garner useful information for research or business in a purely ethical fashion. The speed of availability is also a testament to just how “bots” make it so easy to perform web scraping tasks.
That said, it’s hard not to ignore the implication of AI traffic becoming so commonplace, to the point of accounting for more than half of Internet traffic.
Bot Traffic Report
While some find that AI making up the majority of Internet traffic is worrying, the issue is made worse by having a slight majority of AI traffic being made up of “bad bots. ” Even when scraping intentions are good and the approach is ethical, AI stigma feels unavoidable.
Using bots to tackle an insane amount of data is a logical step. In addition to AI, it’s important to consider other essential tools while scraping.
How Proxies Can Help
As explained here, there are multiple advantages to using proxies while web scraping, namely anonymity. For example, if you wish to study a competing brand and use the information to figure how best to improve your own company, you probably don’t want to have it known that you visited their website. In a situation like this, it’s great to use proxies to access and examine data without giving away your identity.
Before we dive further, here’s a quick refresher on the topic of proxy servers:
Proxy servers are designed to act as a middleman between the user and the web server.
Their functionality is diverse: they can be used both by individuals and companies to address specific needs.
One common use of proxies is tied to web scraping: with a proxy server, it is possible to circumvent restrictions set up by webmasters and gather data en masse.
But why set up those restrictions in the first place? Isn’t this data freely available on the web? Yes — for human users. Here’s a typical example: price aggregators’ entire business model is built around accurate information; namely, providing the definitive answer to the question of “Where can I buy Product X for the lowest price? ”
Although this is a great opportunity for customers to save money, vendors aren’t too excited about other companies snooping around in their data: aggregators’ web crawling software (often called “bots” or “spiders”) introduce additional load on the website. Therefore, it’s not uncommon for webmasters to restrict access to their websites if they suspect that the given web activity isn’t carried out by a genuine user.
Another practical use for proxies is evading a censorship ban. Residential proxies, as the name suggests, allow you to appear as a genuine user from Country X — whichever country you prefer. The need for residential proxies is simple: (suspicious) bot activity usually comes from a set of countries, so even genuine users from these countries often encounter geo-restrictions.
Additionally, when you’re trying to gather data from sources that are kept from you for political reasons, proxy usage is especially helpful. There are many ways to use proxies while web scraping but for the sake of building trust within the digital community, we suggest sticking to methods that will build brand trust and authority.
Using Human Visibility and Trusted Brands to Combat AI Stigma
It’s true, for now, that AI outpaces the number of humans surfing the Internet. Still, there’s no telling how Internet usage will evolve in the coming years, and so there’s no reason to immediately assume this trend is irreversible or that it represents an inherently negative trend.
One of the best ways to upend the negative speech about so much AI traffic on the web is to find ways to restore a human touch to AI usage across the Internet. Additionally, it’s important to use AI in ways that build trust and doesn’t feed misplaced concerns.
Stick to trusted products and services offered by highly recognizable and trusted brands. Wondering which criteria make the vendor “trusted”? Our guide answers this question.
Adhere to ethical scraping practices. Don’t abuse trust by ignoring the file on a website or flood a site with a number of bots in a short window of time.
Use data in a responsible and professional manner. Verify that you have permission to use scrapped data for your intended purpose.
Be informative. Talk about how and why web scraping to build public awareness. The more informed others are about the benefits of using AI to get access to and study vast amounts of data, the less likely scraping and bots will constantly be viewed in a uniformly negative light.
As ideal as it would be to manually access website data through purely human efforts, there’s just too much information to make this a viable option. The amount of data available is practically limitless, and AI is our best means of navigating websites and analyzing their data as efficiently as possible.
For data scientists and other professionals aiming to make the most of their web scraping efforts, we strongly suggest using reliable proxies as they can protect your identity and privacy as you access the information you need for your analysis efforts.
Opinions expressed by DZone contributors are their own.
Web scraping project for machine learning and data …
Published Feb 23, 2021Last updated Feb 25, 2021 About me
I’m mechanical engineer fascinated by data, machine learning, data science, artificial intelligence, data visualization.
The problem I wanted to solve
This model was built in order to solve the challenge when identifying a real good (or even safe) franchise model for investing.
What is Web scraping project for machine learning and data visualization?
I built a web scraping project for machine learning and data visualization, so basically I made the web scraping in franchise website.
Then I applied machine learning to check which franchise model is worth investing in.
So finally I did the data visualization in power bi to analyse everything in charts.
Python: A great tool for many things, like web scraping, building machine learning models, etc.
HTML/CSS: Hard to do some deep web scraping without having knowlodge in both.
Power BI: Currently the best software for data visualization in the market.
The process of building Web scraping project for machine learning and data visualization
That’s may seem like a silly question, though we have many uses for web scraping, so I will break down by item:
Company or personal interests.
Company: This first item is obviously too generic, but lets make an imagination exercise. What if every company had the power to scrape properly tons of data from internet, for example: An e-commerce company know exactly how amazon system works, this surely will be a great advantage against its competitors, of course, it depends on sector in which the company operates.
Personal interest: Imagine that you want to buy a cheap flight ticket to London, how do you do to know the best price, in which day or hour will it be available to sell?
With the target/company/website defined, you can build your model with the data and answer that you got, and open the world of ML.
Why use BI-Business Intelligence after all the work to scrape, build the ML model (or even put it to production) and refine the model? In most cases, of course, if this is not a personal project, you’ll have to show what you did to your boss or whoever you’re trying to convince that your model or idea is good.
So here we have some good options for data visulization, like power bi, looker, tableau, etc.
I recommend power bi, as it has the most prizes in “data viz” competitions.
Challenges I faced
sites often have bad html construction and structure, because they are not usually planned, the construction occurs with necessity (like cities). So many tags, classes, etc are managed poorly, so when web scraping these tags, classes or anything inside HTML you will face a lot of issues.
For example, a website with a certain product can contain four data, like:
If the stock tag is not filled, sometimes you may have “out of stock” output, but sometimes you can have “None”, because there is nothing like the product never existed (just for html purposes).
None for tag triggers errors in frameworks, and this is just the first step of complexity.
Anyway, in this case you can solve with Try/Except in python.
Web scraping it is very useful for companies growth, anyone who uses wiselly can surely have great gains against its competitors, but it can be used as well for personal reasons like simply buying the chipest plane ticket.
Tips and advice
If you are starting in web scraping, machine learning or data visualization I recommend that first you try learning python or power bi, then you can go for web scraping/ML models.
Because when web scraping you require at least a medium knowlodge in python.
Final thoughts and next steps
The first part of this project is web scraping.
The second one is machine learning, and finally the third is visualization through power bi.
So I’ll keep going on in this project.
Enjoy this post? Give Renan Catan a like if it’s arepractical- Senior data analyst with 4+ years of experience
– Previously worked at BP (British Petroleum) with logistics performance/metrics
Engineer passionate about data analysis, mining, cleaning, treating etc.
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Frequently Asked Questions about machine learning web scraping projects
How machine learning can be used in web scraping?
Machine Learning is often used to create advanced scraping algorithms, as it is well suited for the task of generalizing. The two aspects of scraping with which machine learning can help in solving the thesis are; classification of the text data on the site and recognizing patterns within the HTML structure.
Is web scraping needed for machine learning?
The many uses of web scraping Web scraping is a great way for developers to gather large quantities of training data for their machine learning algorithms.
How do I find web scraping projects?
Web Scraping ProjectsScrape a Subreddit. Reddit is one of the most popular social media platforms out there. … Perform Consumer Research. Consumer research is a vital aspect of marketing and product development. … Analyse Competitors. … Use Web Scraping for SEO. … Scrape Data of Sports Teams. … Get Financial Data.