Things to know about web scraping

Things to know about web scraping

First things first, it is important to understand what web scraping means and what is its purpose. Web scraping is a computer software technique through which people can extract information and content from various websites. The main purpose is to use that information in a way that the site owner does not have direct control over it. Most people use web scraping in order to turn commercial advantage of their competitors into their own.

There are many scraping tools available on the Internet, but because some people might think that web scraping goes long beyond their duties, many small companies that provide this type of services have appeared on the market. This way, you can turn this challenging and complex process into an easy web scraping one, which, believe it or not, exists for nearly as long as the web. All you have to do is some quick research on the Internet and find the best consultant that is willing to help you with this matter. When it comes to the industries that web scraping is targeting, it is worth mentioning that some of them prevail over others. One good example is digital publishers and directories. They are one of the easiest targets for web scrappers, because most of their intellectual property is available to a large number of people. Industries like travel or real estate are also a good place for scraping, along with ecommerce, which is an obvious target too. Time-limited promotions and even flash sales are the reasons why ecommerce is seen as a candy by web scrapers.

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Data Mining - Techniques and Process of Data Mining

Data Mining - Techniques and Process of Data Mining

Data mining as the name suggest is extracting informative data from a huge source of information. It is like segregating a drop from the ocean. Here a drop is the most important information essential for your business, and the ocean is the huge database built up by you.

Recognized in Business

Businesses have become too creative, by coming up with new patterns and trends and of behavior through data mining techniques or automated statistical analysis. Once the desired information is found from the huge database it could be used for various applications. If you want to get involved into other functions of your business you should take help of professional data mining services available in the industry

Data Collection

Data collection is the first step required towards a constructive data-mining program. Almost all businesses require collecting data. It is the process of finding important data essential for your business, filtering and preparing it for a data mining outsourcing process. For those who are already have experience to track customer data in a database management system, have probably achieved their destination.

Algorithm selection

You may select one or more data mining algorithms to resolve your problem. You already have database. You may experiment using several techniques. Your selection of algorithm depends upon the problem that you are want to resolve, the data collected, as well as the tools you possess.

Regression Technique

The most well-know and the oldest statistical technique utilized for data mining is regression. Using a numerical dataset, it then further develops a mathematical formula applicable to the data. Here taking your new data use it into existing mathematical formula developed by you and you will get a prediction of future behavior. Now knowing the use is not enough. You will have to learn about its limitations associated with it. This technique works best with continuous quantitative data as age, speed or weight. While working on categorical data as gender, name or color, where order is not significant it better to use another suitable technique.

Classification Technique

There is another technique, called classification analysis technique which is suitable for both, categorical data as well as a mix of categorical and numeric data. Compared to regression technique, classification technique can process a broader range of data, and therefore is popular. Here one can easily interpret output. Here you will get a decision tree requiring a series of binary decisions.

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