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More Data Mining with R

 
More Data Mining with R
h264, yuv420p, 1280x720 |ENGLISH, 44100 Hz, 2 Channels | 10h 34mn | 6 GB

How to perform market basket analysis, analyze social networks, mine Twitter data, text, and series data.


What you'll learn

Understand the conceptual foundations of association analysis and perform market basket analyses.

Be able to create visualizations of social (and other) networks using the iGraph package.

Understand how to examine and mine social network data to understand all of the implicit relationships.

Mine text data to create word association visualizations, term documents with word frequency counts and associations, and create word clouds.

Learn how to process text and string data, including the use of 'regular expressions'.

Extract prototypical information about cycles from series data.

Requirements

Students will need to install the no-cost R console software and the no-cost RStudio IDE suite (instructions are provided).

Description

More Data Mining with R presents a comprehensive overview of a myriad of contemporary data mining techniques. More Data Mining with R is the logical follow-on course to the preceding Udemy course Data Mining with R: Go from Bner to Advanced although it is not necessary to take these courses in sequential order. Both courses examine and explain a number of data mining methods and techniques, using concrete data mining modeling examples, extended case studies, and real data sets. Whereas the preceding Data Mining with R: Go from Bner to Advanced course focuses on: (1) linear, logistic and local polynomial regression; (2) decision, classification and regression trees (CART); (3) random forests; and (4) cluster analysis techniques, this course, More Data Mining with R presents detailed instruction and plentiful "hands-on" examples аbout: (1) association analysis (or market basket analysis) and creating, mining and interpreting association rules using several case examples; (2) network analysis, including the versatile iGraph visualization capabilities, as well as social network data mining analysis cases (marriage and power; friendship links); (3) text mining using Twitter data and word clouds; (4) text and string manipulation, including the use of 'regular expressions'; (5) series data mining and analysis, including an extended case study forecasting house price indices in Canberra, Australia.



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