Web Scrapping code in R

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Text Analytics Platforms Part 1

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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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k means clustering

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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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Different types of sampling methods

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1. Simple Random Sampling : A simple random sample (SRS) of size  n  is produced by a scheme which ensures that each subgroup of the population of size  n  has an equal probability of being chosen as the sample 2. Stratified Random Sampling : Divide the population into "strata". There can be any number of these. Then choose a simple random sample from each stratum. Combine those into the overall sample. That is a stratified random sample. (Example: Mosque A has 600 women and 400 women as members. One way to get a stratified random sample of size 30 is to take a SRS of 18 women from the 600 women and another SRS of 12 men from the 400 men.) 3. Multi-Stage Sampling : Sometimes the population is too large and scattered for it to be practical to make a list of the entire population from which to draw a SRS. For instance, when the a polling organization samples US voters, they do not do a SRS. Since voter lists are compiled by counties, they might first do a sam...
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