Web scraping, also known as data extraction or web harvesting is the method of gathering massive amounts of data from multiple online sources. Web scraping can be done manually, which is time-consuming, or automated through a tool like ParseHub.
Web scraping is very useful, and can be used for a variety of tasks such as:
- Data collection for research
- Lead Generation
- Market research
- Competitor analysis
- Price comparisons
Web scraping, especially when done using a tried and tested tool like ParseHub, is a considerable way to gather high-quality data. When it comes to machine learning, algorithms need these correct and high-quality data to produce appropriate results and predictions.
What is Machine Learning?
Machine learning is a popular and growing subject in computer science and artificial intelligence. Machine learning combines the use of accurate data combined with sophisticated algorithms which result in an AI that learns and continuously improves, similar to how a human would.
You might have heard of neural networks and deep learning. Deep learning is a subset of neural networks, which is a subset of machine learning. All of these algorithms are a part of artificial intelligence. Therefore, enterprise web scraping can be used for any sort of algorithm in artificial intelligence, which requires big data.
What is Machine Learning Used For?
Machine learning algorithms can be used in predictive modelling, which can be used to predict future outcomes. These predictions are based on past data and trends and can be useful for enterprise financial corporations and price data analysis.
Another use of machine learning is computer vision, which is important for self-driving cars and facial recognition systems. Machine learning can also be used for stock, asset and cryptocurrency trading; using data and algorithms to predict future trends using fundamental analysis.
Social media companies use machine learning and big data to give curate and present the most relatable and viral content for the specific user’s interests. YouTube and Netflix use machine learning algorithms to give relevant recommendations and content to the user, even without them asking for it. This reduces the need for a user to search for specific videos or movies, which is great for short attention spans!
Ecommerce giants such as Amazon, Alibaba and big-box retailers use machine learning for similar reasons. When you are in your shopping cart, you will notice relevant products that compliment or upsell your order. Also, the front page will almost always have products that you will be interested in, based on previous purchases or search history.
There are many other uses of machine learning, such as fraud detection, advertising, decision making, AI assistants, and many more. Now that you know what machine learning is, and how it can be used, let’s discuss how ParseHub can help with machine learning.
ParseHub for Machine Learning
As discussed before, relevant and accurate data is extremely important for machine learning. Using incorrect or faulty data will throw off the AI’s learning and can result in bad decisions and predictions. Therefore, having an emphasis on properly extracted data is required.
Manual data entry is a very tedious and time-consuming process and will result in tons of human error. Using a web scraping tool such as ParseHub ensures timely and accurate data extraction, which can be used for any artificial intelligence algorithm.
ParseHub can extract data from any website and industry, such as:
- Ecommerce
- Real Estate
- Finance
- Tourism
- Social Media
- and many more…
Using ParseHub to extract data from these industries, your enterprise business can create an accurate and well-modelled machine learning algorithm; resulting in meaningful and precise predictions. Since machine learning is using an automated process to learn from its own data and algorithm, it only makes sense to automate the data extraction process as well.
Using ParseHub, you will save a lot of time and money when web scraping, as you won’t need to code and can extract data easily to send to your data scientists. Being a visual web scraper, you can directly see where the data is being extracted and can make sure the data is accurate for your artificial intelligence.
Concluding Thoughts
Machine learning is an impressive branch of artificial intelligence, which is being utilized by most enterprise corporations. Since artificial intelligence can learn like a human, if not better, it is a great asset to any business that deals with data, predictions or analysis. Using machine learning combined with a web scraper such as ParseHub, your business can make more accurate business decisions and increase profits. Using ParseHub is a fail-safe way to reduce human error in data extraction and increase productivity via automation. If you are using machine learning, you definitely would want to have accurate and up-to-date data, which is easily done with ParseHub’s API and scrape scheduling.
We hope you enjoyed this blog post about web scraping and enterprise machine learning!
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