Web Scrapping code in R

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In today's world, data plays an integral role in driving business decisions. One of the most common and effective ways of analyzing data is through machine learning algorithms. K-Means clustering is an unsupervised machine learning algorithm that has gained popularity for its efficiency in data clustering. What is K-Means Clustering? K-Means clustering is an algorithm used to create clusters based on the data fed to the machine. The algorithm works by randomly assigning a color to a few data points, known as the centroid kids, and grouping the surrounding data points based on the mean distance from each centroid. Applying K-Means Clustering to Customer Segmentation For instance, retailers can use K-Means clustering to decide which customer gets promotional offers. They can create three clusters of customers, namely the loyal, somewhat loyal, and lowest priced shoppers, based on their shopping patterns. Then, they can create strategies to convert somewhat loyal customers into loyal ...
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Text analytics is still largely an immature science, and embraces several different approaches. Natural language processing (NLP) includes dozens of techniques for accomplishing tasks such as language translation, document categorization and tagging, extraction of meaningful terms and so on. Text mining on the other hand is primarily concerned with the extraction of meaningful metrics from unstructured text data so they can be fed into data mining algorithms for pattern discovery. Some suppliers have applied text analytics to very specific business problems, usually centering on customer data and sentiment analysis. This is an evolving field and the next few years should see significant progress. Other suppliers provide NLP based technologies so that documents can be categorized and meaning extracted from them. Text mining platforms are a more recent phenomenon and provide a mechanism to discover patterns which might be used in operational activities. Text is used to generate extra...
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