It is understood that data is the fuel for any data science related project. Since web is becoming the biggest repository of data that has ever been, it makes sense to consider web scraping for fueling data science use cases. In fact, aggregating web data has many applications in the data science arena. Here are some of the use cases.
Real time analytics: Many data science projects require real time or near real time data for analytics. This can be facilitated by crawling websites using a low latency crawl. Low latency crawls work by extracting data at a very high frequency that matches with update speed of target site. This gives near real time data for analytics.
Predictive modeling: Predictive modeling is all about analyzing data and using probability to predict outcomes for future scenarios. Every model includes a number of predictors, which are variables that can influence the future results. The data required for making relevant predictors can be acquired from different websites by using web scraping. A statistical model is formulated once the processing is done.
Natural language processing: Natural language processing is used to equip machines with the ability to interpret and process natural languages used by humans like English as opposed to a computer language like Java or Python. As it’s difficult to determine a definite meaning for words or even sentences in natural languages, natural language processing is a vast and complicated field. Since the data available on the web is of diverse nature, it happens to be highly useful in NLP. Web data can be extracted to form a large text corpora which can be used in Natural language processing. Forums, blogs and websites with customer reviews are great sources for Natural language processing.
Training machine learning models:Machine learning is all about equipping machines to learn on their own by providing them training data. Training data could differ according to individual cases. However, data from the web is ideal for training machine learning models for a wide range of use cases. With training data sets, machine learning models can be taught to do correlational tasks like classification, clustering, attribution etc. Since the performance of a machine learning model will depend on the quality of training data, it is important to crawl only high quality sources.
Provided with the training data, machine learning programs learn to do correlational tasks like classification, clustering, attribution etc. Here, the efficiency and power of the machine learning program will hugely depend on the quality of training data.