Only 78.5% of small businesses survive the first year. The top reasons for the failure of startups are insufficient market research, poor business plans, and inadequate marketing.
As a business owner, you can overcome these obstacles through access to quality and reliable information about the market, which you can find on the web.
Web scraping, also known as web data extraction, is the process of retrieving or “scraping” data from a website. This information is collected and then exported into a format that is more useful. As its name suggests PRAW is a Python wrapper for the Reddit API, which enables you to scrape data from subreddits, create a bot and much more. In this article, we will learn how to use PRAW to scrape posts from different subreddits as well as how to get comments from a specific post.
The internet is a rich source of data in areas such as:
Category archive for Web Scraping. Author(s): John Bica Web Scraping, Programming, Natural Language Processing Multi-part series showing how to scrape, preprocess and apply & visualize short text topic modeling for any collection of tweets Disclaimer: This article is only for educational purposes. Web Scraping Crypto Prices With Python. This is the most beautiful soup. Every Thursday, the Variable delivers the very best of Towards Data Science: from hands. In this project, I built a web scraper in Python to collect data from an online course review site. I then conducted some EDA on this data to find some of the best data science courses based on user reviews. Skills demonstrated: Web Scraping, Python, EDA, data visualization.
Trends in the market
Customers' needs and wants
Competitors' strengths and weaknesses
By collecting data from relevant websites, you can develop workable business plans, develop effective marketing strategies, and create customer-responsive products.
Manually collecting these data requires a lot of human resources, time, and could result in numerous omissions and errors. You can improve this process with data scraping.
What is Data Scraping?
This is an automated technique of gathering data from the web using a scraper. The scraper is set to extract specific data from targeted websites. For instance, it can collect contact details of small business owners from the Yellow Pages or prices of any particular product from Amazon.
Once it extracts the data, the scraper parses it and stores it in a spreadsheet or database in a readable format.
Most websites do not allow scraping on their sites. This is because it slows down the site and compromises the users' experience. Scrapers also give the impression of real traffic, which interferes with the accuracy of web analytics.
Web scrapers make use of proxy servers to bypass this hurdle.
What is a Proxy?
A proxy server acts as a go-between, preventing direct communication between the device using the scraper and the webserver. The proxy comes with an IP address attached to a specific location. Any request made by the device or response from the website goes to the proxy first, hiding the device's real IP and location.
There are two main types of proxies
1) Data Center Proxies
These are an artificial kind of proxies that are created in data centers. They do not rely on an internet service provider or an internet service. Data center proxies are fast, making it possible to scrape large amounts of data in a short time.
2) Residential Proxies
These are proxies issued to homeowners by internet service providers. They are not as fast as data center proxies, but the chances of being detected when using these proxies are low. Residential proxies are legit and reliable, guaranteeing an uninterrupted scraping project.
Proxies can be private or shared. A private proxy is issued to a single user, who assumes control over the proxy. A shared proxy is where a number of users share proxies and their costs.
Web Data Scraping Software
Although shared proxies are cheaper, they are slow, especially during peak times. They are also less secure. This is because you cannot control the websites that the other users access with the proxy.
Why do Businesses Need Data Scraping?
Here are the benefits that an analysis of the information collected through scraping can bring to your business.
Collecting pricing information makes it possible to set more competitive prices
Using data scraping to monitor your competitors ensures that you do not lose your market share
Scraping data on the most effective keywords improves your SEO and draws organic traffic to your site
It makes it possible to gather quality leads in a short time, improving your marketing strategy
You can collect data on your target market and use it to develop products that meet their needs.
Is Data Scraping Legal
Many business owners often question the legality of data scraping. But data scraping is legal, as long as you stick to two rules.
1) Scrape public data
2) Use the data collected to gain insight and not for making a profit
Public data is any information available on the web that does not require any login information to access. A simple search query should reveal the information you need.
The data extracted should be used to gain insight into market conditions, make better decisions, and develop better strategies.
Most businesses provide guidelines on how you should scrape the website, which will be available in the robots.txt file. Follow the guidelines provided.
Avoid scraping the website too fast or making too many requests at a go. It will slow down the site. You can resolve this by using rotating IPs and adding delay periods on your scraper. Adding some random clicks and mouse movements will also give the impression of a regular user, and prevent you from being detected.
Conclusion
So, what is data scraping? This is an automated data collection technique that is transforming the way businesses make decisions. It enables startups and small businesses to remain relevant in the market and grow their customer base by using insights from information extracted from the web.
Scrape publicly available data and avoid using it for commercial gain. Follow the scraping rules provided on the website. And ensure that your scrapers do not affect the website's performance. If you are looking for scraping tools try Zenscrape.
Web Scraping Using Python Towards Data Science
Towards Data Science Web Scraping Solutions
I am a data scientist with a passion for storytelling. I believe that words and data are the two most powerful tools to change the world.
Most of my time is spent staring at a computer screen. During the day, I am usually programming, working to derive insight from large datasets. My skills include data analysis, visualization, and machine learning. I have developed a strong acumen for problem solving, and I enjoy an occasional challenge. I often work on end-to-end data science projects that usually begin from collecting data from third party sources and end with delivering business insight in the form of customer segments.
At night, I take some time off to work on things I'm passionate about. I write articles and publish them on the Internet. Sometimes, I create personal projects and write tutorials on them. I also enjoy going on sites like HackerRank and trying out their programming challenges.
You can take a look at some of my projects and articles in the section below. I will link my work to their GitHub repositories, so feel free to download my code and play around with it. Most of my education has come from online platforms. I have downloaded e-books, audited courses on edX and Coursera, and spent countless hours on sites like HackerRank and FreeCodeCamp. I am grateful to online educators who have given me the opportunity to learn these things, and for democratizing education.
To give back to the community, I create tutorials detailing things I have learnt. I create starter code for data science and visualization projects and publish it for everyone to read. If you are a data science aspirant, please feel free to check out these tutorials on my blog site.