Web scraping is an active area of research in the realm of machine learning. This technology allows developers who need more training data than they have employ a web scraping solution to extract the right kind of information from publicly available websites. Companies can use this data to train a machine learning algorithm, a deep learning algorithm or other types of algorithms. Such an approach requires less time, funds and human resources, compared to manual data processing — but you need to build or outsource instruments for it.
Web scraping is also known as web data extraction. This technology enables you to collect data from websites by directly accessing the World Wide Web using the Hypertext Transfer Protocol on a browser. Algorithms can extract all the data from a particular website or only specific datasets, such as price, images, address, comments or any other elements. The manual process of scraping would be too time consuming. Unlike a human being, an algorithm works much quicker and hardly makes any mistakes — and these are its primary values.
Web scraping software normally relies on the Python programming language.
We extract the data you need from any website to satisfy all your business requirements with 100% accuracy.
Machine learning algorithms can quickly process large amounts of data. Human specialists can use this data to create libraries of useful facts, diagnose health conditions, detect fraud, etc.
The larger quantities of training data you have, the more machine learning models benefit from them. If you download images, texts, tags and other types of content from the Internet manually to "feed" them to the algorithm, you would hardly be able to satisfy its appetite. Plus, any human professional will inevitably make mistakes when doing the job. You'd better launch a scraping project for your deep learning model or any other models that you might have. It will not only import the information from multiple sources and libraries — but also structure the HTML data so that ML models could use it for analysis. You won't need to open page by page in the browser yourself.
On the Internet, you should be able to find large sets of data, tailor-made for training purposes and available for free download. You won't need scraping solutions to access this data. But you can never be sure whether such sets of data will fit the models employed in your project for machine learning. This is why you need scrapings. This technology will create databases with the right values and you'll be able to use this information in a number of ways.
These are a few examples of machine learning projects that can benefit from web scraping:
Now, we'd like to focus on three particular cases that data science experts from all over the world find particularly promising.
The first is training predictive models. AI that is in charge of predictive analytics can recognize patterns in historical data. It can classify events based on their frequency and relationships. Based on that data, it can estimate the probability of an event happening in the future.
The second case is optimizing natural language processing models. NLP is the heart of conversational AI applications — yet it has to overcome multiple challenges. The meaning of a phrase that a live human being says does not always equal the sum of meanings of all the words that this phrase contains. Let's consider an example without context. A user might say something like "Wow, that's indeed the best medium for your project!". If we lack any comment or other people's responses, we can never be sure whether the user is honest or sarcastic. Depending on the intonation, this phrase might mean that it is the worst medium for the project!
AI needs to learn how to handle sarcasm, ambiguity, acronyms and a number of other peculiarities of human speech.
Also, artificial intelligence needs to excel at analyzing real-time data. Data experts can modify search requests so that crawlers will be collecting information at specific time intervals, such as every hour, day, week or month. If a volcano is erupting, a hurricane is approaching or a government election is going on, people might want to get accurate updates as frequently as possible. Such data will enable them to take timely measures to prevent damage or nefarious activities.
To scrape the data from a targeted URL, you should write the script of a web robot. It will consist of three steps.
We won't provide a code piece for a web scraping robot here. If you need a code snippet, you should be able to easily find it on the Internet.
However, many businesses are not ready to build tools that perform the scraping function. If this is your case, you can outsource a powerful solution at an affordable price with us. Your team members won't need to know the meanings of such terms as "regular expression", "interaction scores", "score feature", "inspect element" or "parse tree". You just let us know the characteristics of the data that you would like to collect and we will scrape it for you.
We'll send you an example of the collected data in a CSV file or any other format that you find suitable. You'll need to pay us only if you find this sample worthy. We'll listen to your comments and will collect the full dataset for you. For some personal reasons, we haven't entirely automated our workflow yet — but we can guarantee that you'll be able to import data from any URL you need in the shortest time.
Some clients might ask, is it legal to import data from a website that belongs to a third party? Especially if it's not just a blog post with a comment but a carefully curated collection of valuable data? The answer is yes, absolutely. If anyone can access a page to read an article, watch a video or listen to an audio record, it means the data is publicly available. We can import it legally to perform any analysis we find necessary. We will be glad to do this job for you!
Hopefully, you found this article informative and now you better understand the potential of web scraping in the realm of machine learning. If you're interested in training an ML model, you need to "feed" a lot of data to it — but you don't need to create an instrument for search engine scraping yourself. Instead, you can entrust this job to us and sign up for our excellent scraper. It can extract data from thousands of websites promptly and at a sensible price. Feel free to get in touch with us to ask questions! We'll be happy to consult you and provide you with large amounts of data.
Let us take your work with data to the next level and outrank your competitors.
1. Make a request
You tell us which website(s) to scrape, what data to capture, how often to repeat etc.
2. Analysis
An expert analyzes the specs and proposes a lowest cost solution that fits your budget.
3. Work in progress
We configure, deploy and maintain jobs in our cloud to extract data with highest quality. Then we sample the data and send it to you for review.
4. You check the sample
If you are satisfied with the quality of the dataset sample, we finish the data collection and send you the final result.
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