However, the broader field of Web information extraction also requires the knowledge of natural language processing techniques such as text pre-processing, information extraction (entity extraction, relationship extraction, coreference resolution), sentiment analysis, text categorization/classification and language models. In this chapter we focus on Web data extraction (Web scraping) - automatically extracting data from websites and storing it in a structured format.
Taking into account all the existing Internet-enabled devices, we can estimate that approximatelly 30 billion devices are connected to the internet ( Deitel, Deitel, and Deitel 2011). Today there are more than 3.7 billion Internet users, which almost 50% of the entire population ( Internet World Stats 2017). 11.4.2 Single imputation with prediction.11.1 The severity of the missing data problem.10.6 Putting it all together with Python.Estimating how performance will generalize.10.2 Commonly used prediction models and paradigms.10.1.1 The process of predictive modelling.8.5.3 Agglomerative hierarchical clustering.8.5.2 Determining the number of clusters.8.4 t-Distributed Stochastic Neighbor Embedding (t-SNE).6.3.4 Docker application example with multiple services.5.2 Descriptive statistics for bivariate distributions.5.1.5 Testing the shape of a distribution.5.1 Descriptive statistics for univariate distributions.4.2.3 Modern Web sites and JS frameworks.4.1 Introduction to Web data extraction.3.5 Data dashboards - tooling and libraries.3.2.1 Preregistration - the future standard.1.3.2 Pure Python distribution installation.1.3.1 Anaconda distribution installation.