In today’s digital age, mastering web scraping is crucial for professionals aiming to automate structured data extraction from websites, especially when an API is unavailable or offers limited data access. The concept might seem daunting initially, but I’ve found that the best way to grasp web scraping is through hands-on experience with Python projects. 

In this guide, I’ll share insights into various web scraping project topics for beginners, tailored to suit a range of industries and skill levels. Whether you’re just starting or looking to deepen your expertise, you’ll discover a project that matches your interests and professional aspirations. 

Web scraping, also known as Web Harvesting or Screen Scraping, involves extracting large quantities of data from websites and storing it locally, either in a file on your computer or in a database. This technique is invaluable for professionals like me, aiming to leverage web data for analysis, insights, or decision-making in our respective fields. 

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. 

What programming languages are used for web scrapping, and which one is the best?

A bunch of programming languages can be used for web scrapping, and the list includes languages like Python, Ruby, Java, PHP and Perl. along with these, one can also use HTML or HTML markup or Javascript as their preferred programming language for web scrapping. 

On that note, the language that is preferred the most to perform web scrapping is Python. The main reason is its ease of use. If someone is a beginner in web scrapping, they should definitely choose web scraping python projects as it utilises an object-oriented programming language. The classes and objects in web scraping projects using Python are comparatively easier to use than any other language, making it beginner friendly.  

How to scrape data from a website by using Python?

In case you are an absolute beginner at web scrapping, before jumping into web scraping python projects, here is a brief overview of how web scrapping on python web scraping projects is done and what are the prerequisites to do that.

Step 1: Load the web pages with ‘request’. For this, the ‘requests’ module is used which sends HTTP requests using Python. This request will bring back a Response Object which will contain all the response data. 

Step 2: Extract the title. This is done by taking the help of  Python libraries like Selenium, BeautifulSoup or Pandas. All of these libraries have different purposes and as we are trying to extract data, the library that we need will be BeautifulSoup. 

Step 3: Extract the body content. Similar to extract the title, BeautifulSoup is used here to easily extract some of the sections of the body content. 

Step 4: Select DOM elements with BeautifulSoup methods like “.select” under the CSS selector inside the library. 

Step 5: Scrape the top items and store them in a list called “top_items”. Use the “strip” method in this step to remove extra whitespaces that are present in the output. 

Step 6: Extract the links. To be more precise, extract the “herf” attributes and link them with their “texts”. By doing this you will create a list named “all_links”. 

Step 7: Store the data in the required format. 

Now that you’re familiar with the basics of web scraping, we should start discussing web scraping projects too. Also, you can easily perform these web scraping projects using Python.

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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. Here is the source code to the project.

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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. 

How to work on this project

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:

• Trustpilot
• Yelp
• GripeO
• BBB

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. Here is the link to the source code.

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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. 

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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. 

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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. 

How to work on this project

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. You can find the source code for the project here.

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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. 

How to work on this project

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. Link to the source code is mentioned here.

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.

How to 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. Here is the link to the source code.

7.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:

• Naukri.com
• Indeed.co.in
• Timesjobs.com

How to work on this project

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

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8.Creating a job search platform utilizing web scraping 

We must apply for employment roles through many websites while applying for a job. This is a tiresome process at times. We might think about developing a platform to scrape job postings from many websites, such as Glassdoor, LinkedIn, and others. Using web scraping to create a job search engine involves collecting job listings from multiple websites and combining them into a single database. We will be able to view the jobs in one location, which will save us time and effort. This is one of the popular web scraping project topics for beginners. 

How to work on this project 

• Identify which job search portals to target for scraping, such as Indeed, Glassdoor, LinkedIn, Monster, etc.  
• Take note of the layout of these websites, including the HTML structure, pagination techniques, and the presentation of job listings.  
• Data such as the company name, industry, skills, degree of education, career stage, pay information, and job type can all be extracted. 
• Consider the features you want your job search engine to have, like filters, user accounts, alerts, and search capabilities. 
• Create and implement the job search engine’s user interface, including components such as a search bar, filters, job listing display, and pagination. 
• Create a database schema to store the job listings that are being scraped. 
• To handle frontend requests, create backend APIs (application programming interfaces). Integrate your web scraping code with the backend to add scraped job listings to the database. 

This GitHub Repository contains the source code that will help us use Python programming to scrape the job listing data from job portals like Glassdoor and Monster. 

9.Analyzing customer reviews and feedback 

Developing a web scraping project to evaluate customer reviews involves gathering reviews from different internet sites, evaluating their sentiment, identifying significant topics or themes, and providing organizations with insights. Businesses can improve products and services, acquire insightful information about customer opinions, and make well-informed decisions to increase customer happiness and loyalty by scraping data for individual products available on Amazon or Flipkart and analyzing customer reviews. 

How to work on this project 

• Choose a product to scrape reviews from by identifying target websites that provide consumer reviews, including Amazon, Flipkart, Myntra, etc. 
• Pull out the necessary information, such as review content, rating, product specifications, reviewer details, etc., using the web scraping tool. 
• Determine the sentiment (positive, negative, or neutral) of each review by applying sentiment analysis techniques using pre-trained sentiment analysis models. Based on the findings of the analysis, give each review a sentiment score. Additionally, we can use free versions of several APIs to determine the sentiment score. 
• Analyze the sentiment distribution across different products on the portal. Provide visualizations and summaries using a data visualization tool to present the analysis results effectively. 

This GitHub Repository contains the source code to extract customer reviews from Home Depot, Walmart, Amazon, and Lowe’s and to classify negative with a star rating of less than 3 for certain issues. 

10.Implementing alerts for price drops on E-commerce products 

Using web scraping to develop a Price Drop Alert system for E-commerce items involves tracking price changes across several online merchants and alerting customers when prices drop below a certain value. Everyone desires to purchase their preferred items at the greatest discount possible. However, there’s a chance of missing out on optimal deals. Alternatively, by monitoring price shifts of items in our wish list, we can capitalize on the most opportune moment to make a purchase. 

How to work on this project 

• Find popular marketplaces like Amazon, eBay, Walmart, and others. Select the products you wish to watch for price drops on these sites. 
• Use online scraping tools such as Scrapy to retrieve product details from the target websites, such as the name, price, URL, and availability status. 
• Create a database schema to hold the product data that has been scraped and set up a system to update the database with the most recent information regularly. 
• Provide an intuitive online interface that enables consumers to enter their selected products and create alerts for price drops. 

This GitHub Repository contains the source code that provides a user interface to interact with an automated price-tracking web scraper. 

11.Scraping news websites and aggregating their content 

It’s getting more and harder to stay up to date with all the news that highlights noteworthy events happening across the world, with so many new channels appearing all the time. The goal of this project is to gather news stories from multiple internet sources, compile them into a single platform, and give consumers an easy method to view and browse the most recent information on a variety of subjects and categories. We will extract news articles from a variety of news websites by using web scraping techniques and obtaining data such as article names, authors, publishing dates, content, and URLs. The project offers users a centralized platform to access and explore news articles from various sources. 

How to work on this project 

• Pick the preferred news websites from where we need to scrape data to gather news. 
• Create a database structure to effectively store the news articles that have been scrapped. 
• Create algorithms to classify and arrange news items according to sources, keywords, or subjects. Give people the option to browse news by category so they can explore and navigate with ease. 
• Provide an intuitive user experience for the news aggregation platform that will consist of design features such as showcasing top news stories, category pages for browsing news by topic, and individual article pages for reading detailed content. 

This GitHub Link contains source code for different news scrappers that are built to crawl the web and extract information about news articles that we want. 

12.Scraping data of companies from the web 

The project’s goal is to use web scraping techniques to gather and compile company-related data from several online sources. The objective is to build an extensive collection of business data that may be utilized for lead generation, market research, competitive analysis, etc. This offers a strong tool for gathering, combining, and evaluating data about businesses from several web sources. Utilizing online scraping techniques will make it easier to access and examine corporate data for a range of uses. Analyzing a company’s financial statements, for instance, is essential if you intend to invest in them. 

How to work on this project 

• Identify relevant websites and online platforms such as Glassdoor, Ambitionbox, Crunchbase, or LinkedIn that contain information about companies. 
• Create web scraping scripts to retrieve business information from the identified sources. 

• This covers details about the organization, including its name, location, revenue, industry, stock price, and size. 
• It also includes contact information, website URLs, social media profiles, and any other relevant information. 
• Create an effective data storage schema and select a suitable database management system to store and arrange the company’s data that has been scrapped. 

You can head over to this YouTube link, which demonstrates how to use BeautifulSoup, Request, and Pandas to web-scrape a website to get a list of companies’ data in an Excel spreadsheet. 

13.Analyse IT Salaries to Suggest Optimal Job Change Times 

This project is last on the list of our web scraping projects with source code. The goal of this web scraping project is to track and assess pay trends within the IT sector to suggest the best times for professionals to think about changing jobs. The project aims to provide useful insights to people looking for prospects for career progression or pay rise by utilizing data on IT salaries. This will assist IT professionals in knowing when to negotiate a raise or look for a new position. 

How to work on this project 

• Locate multiple sites, such as AmbitionBox, PayScale, and Glassdoor, to obtain information on salaries. 
• Use web scraping methods or APIs to gather current pay data for various IT positions and regions. 
• Build a scraper that systematically extracts relevant information from selected websites, such as job titles, pay ranges, and locations (ideally in Python using libraries like BeautifulSoup or Scrapy). 
• Compute the average and the median salary for your role and experience and compare it with your current salary. 

This GitHub link contains the source code that scrapes salary data from Glassdoor. 

Conclusion

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.

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What do you think of these project ideas? Which one of these ideas did you like the most? Let us know in the comments.