Data Scraping in the Era of Machine Learning: Fuelling AI Models

Imagine you want to build a machine learning model to predict the variation of stock prices for the upcoming quarter. As stock prices can have complex variations, you have to provide the algorithm with a large amount of historical data so that the model can learn the trends and attempt to predict the prices for the future. However, collecting such data manually could take hours of recording and transcribing errors.

Data scraping is the ultimate method to obtain such training data from publicly available resources at scale effortlessly. This article will explore how data scraping can be used to collect high-quality training data. We will also discuss the challenges associated with data scraping and strategies to gather data by adhering to ethical and legal considerations of online resources.

Data scraping + machine learning — The perfect combination?

Data scraping automatically gathers data from online resources such as websites, databases, APIs, and documents. Information may be present in online resources in structured (CSV, tables), semi-structured (HTML, JSON, XML), or unstructured (Log files) format. Data scraping aims to parse through these data and transform them into a structured format for further processing, analysis, or storage.

Machine learning algorithms have undoubtedly become prominent in automating complex tasks in recent years. With the high amount of data availability, machine learning models have been built to learn patterns and conduct various tasks such as detecting fraud, analyzing stock prices, generating personalized recommendations, and even accurate medical diagnoses. However, the accuracy and generalizability of the output of machine learning models highly depend on the amount and quality of the training data.

Following are some of the advantages of using data scraping to gather training data for machine learning:

1. Access to a large volume of data: By leveraging data scraping programs, we can quickly go through a large number of resources, select the data elements such as metadata, text, or images that contain relevant information, and create a training dataset that is more representative, accurate, and diverse than a manually generated dataset.
2. Eliminate human error: As data scraping automates the data extraction process, it eliminates the errors that may occur when manually entering and copying data.
3. Access to updated data: Even though there are publicly available datasets to train machine learning models for specific tasks, it is crucial to have the historical and latest information to make accurate predictions. Therefore, data scraping can be used to obtain up-to-date information from different websites and databases.
4. Efficient and cost-effective data collection: As the data scraping process can be automated using tools and programs, training data can be acquired from multiple sources quickly with less cost than manual data collection.

But what are the challenges of data scraping?

While data scraping has many advantages, there are several legal and technical challenges faced by data scrapers that make the data collection process difficult:

1. Legal and Ethical Considerations: Collecting publicly available information on the Internet via data scraping is not covered as an illegal act by law. However, the data extracted from scrappers are regulated via privacy regulation policies such as the General Data Protection Regulations (GDPR) and copyright infringement laws.

One way to mitigate such legal implications is to go through the Terms of Use on the website and the robots.txt file, which contains information about permissions provided for scrapers. We can also use third-party scraping tools such as Bright Data’s Scraping Browser, which has built-in compliance with data protection laws to conduct data scraping legally and ethically.

2. Anti-scraping measures: Websites may use anti-scraping measures such as IP blocking, CAPTCHAs, honey pot traps, and session tracking to prevent scraping bots from making many parallel requests to collect data in bulk.

Some methods to bypass such blocking measures include proxy servers, CAPTCHA handling services, and IP rotation. For instance, Bright Data’s Scraping Browser is a powerful tool that helps navigate these hurdles. It offers a range of features, such as IP rotation, CAPTCHA handling, proxy networks, session management functionalities, and website unlocking technologies integrated with Bright Data’s Web Unlocker to tackle anti-scraping measures effectively.

3. Changing website structure and dynamic content: Even though scrapers are written with the website’s design in mind, developers often change the website’s UI to improve the user experience or add new features. Such changes to the website structure may result in broken scrapers, resulting in incomplete or inaccurate data. Furthermore, it is challenging to implement scraping techniques to extract information from websites that use JavaScript and AJAX to display data dynamically (delayed loading, infinite scrolling, etc.).

So, to avoid the parser from breaking, it’s best to run a thorough website test to detect any changes and employ mechanisms to ensure that the webpage is fully loaded (including the dynamic content) before scraping data. These steps can be easily implemented with the help of third-party tools such as Scraping Browser from Bright Data which ensures the web pages are fully loaded and rendered before data extraction, adapting to dynamic website changes, and providing complete and accurate data.

Level up your training data collection with data scraping

If you wish to utilize data scraping to level up your data collection game for your ML training process, consider the following guidelines shown below:

1. Select the resources containing relevant data: Perform comprehensive research to find online platforms that have relevant data for the machine learning model.
2. Ensure the legality of data scraping from the selected resources: Go through the terms of use in the targeted resources and ensure that scraping data from those resources is legal and ethical to avoid possible legal actions.
3. Inspect the structure of data in resources: Understanding how the data is displayed in different resources is essential to automate extracting the relevant data correctly. The scraping program must be written to identify the unique HTML tags, CSS selectors, or XPath expressions of the elements containing the information we need. As shown in the image below, developer tools in modern browsers can be used to inspect the website structure.

1. Develop the program for scraping: With the help of third-party libraries such as BeautifulSoup, Scrapy, Selenium, or Requests, write the code to extract the data needed from the selected sources. Special attention must be given to anti-scraping mechanisms. On the other hand, scraping tools such as Bright Data’s Scraping Browser can be used for hassle-free data extraction without writing code to build your scraper. It has built-in functionality to bypass anti-scraping measures with features such as proxy networks, session management, IP rotation, etc., making it easier to scrape and gather structured data reliably and ethically.
2. Extract and process data: Run the scraping code to extract the desired data and clean and validate the data.
3. Train the model: Once the data is prepared according to the format, feed the data to the model for training.
4. Monitor and maintain the scraping process: Since the website structure may change over time, it is essential to regularly check the functionality of the scraping process to ensure that the code is working correctly and correct data is being extracted reliably.

Concluding thoughts

Data scraping is a highly convenient method of obtaining large amounts of diverse training data to enhance the overall performance of AI models. However, programmers must adhere to the legal and ethical considerations, technological boundaries, and scalability challenges associated with data scraping. By utilizing the correct third-party tools, data scraping can be a powerful method to collect high-quality training data and drive the advancement of machine learning applications.

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