The need for data
Machine learning algorithms require large quantities of high quality data to learn. Data is required to train, test and validate machine learning models before they can be used for prediction. The success of a machine learning project depends heavily on the quality and quantity of data used for training and testing the model.
Public Data-sets for Machine Learning
For learning ML and playing around with various ML algorithms and libraries there are many public data-sets available. But for training-testing models which solves problems unique to your projects, the data required may not be available first-hand in public domain.
Web Scraping for collecting training/testing data
In such cases the required data might be already present online in structured format. Then, the technique of web scraping can be used to extract them to a spreadsheet or database.
For example, if your model learns from thousands of reviews/ratings provided by customers for various products in an eCommerce website or for various hotels/restaurants in sites like TripAdvisor, then this data can be easily fetched using web scraping. Or, if your model learns from real estate data of thousands of properties from various locations, then that too can be extracted by employing web scraping.
Using WebHarvy for easy web scraping
You can either write your own script/code to fetch data from multiple pages of various websites, or more easily, you can use a visual web scraping tool like WebHarvy to get the data which you need with the least effort in a more efficient way. In case you are interested, please follow the link below to know more.
Have any questions ?
Feel free to contact us if you have any questions or need any assistance in fetching data using WebHarvy.