By Chiu (David Chiu) Yu-Wei
- Apply R to simplify predictive modeling with brief and easy code
- Use desktop studying to unravel difficulties starting from small to important data
- Build a coaching and checking out dataset from the churn dataset, utilising various class methods
The R language is a strong open resource practical programming language. At its middle, R is a statistical programming language that gives outstanding instruments to investigate information and create high-level graphics.
This publication covers the fundamentals of R by means of developing a trouble-free programming surroundings and acting info ETL in R. information exploration examples are only if display how robust info visualization and desktop studying is in studying hidden relationships. you are going to then dive into vital computing device studying themes, together with facts class, regression, clustering, organization rule mining, and size reduction.
What you'll learn
- Create and check up on the transaction dataset, acting organization research with the Apriori algorithm
- Visualize styles and institutions utilizing a number of graphs and locate common itemsets utilizing the Eclat algorithm
- Compare changes among each one regression technique to detect how they clear up problems
- Predict attainable churn clients with the type approach
- Implement the clustering technique to section purchaser data
- Compress pictures with the size relief method
- Incorporate R and Hadoop to resolve laptop studying difficulties on vast Data
About the Author
Yu-Wei, Chiu (David Chiu) is the founding father of Largit information. He has formerly labored for pattern Micro as a software program engineer, with the accountability of establishing giant info structures for company intelligence and client courting administration platforms. as well as being a start-up entrepreneur and information scientist, he makes a speciality of utilizing Spark and Hadoop to procedure substantial info and observe information mining concepts for information analysis.
Table of Contents
- Practical desktop studying with R
- Data Exploration with RMS Titanic
- R and Statistics
- Understanding Regression Analysis
- Classification (I) Tree, Lazy, and Probabilistic
- Classification (II) Neural community and SVM
- Model Evaluation
- Ensemble Learning
- Association research and series Minin
- Dimension Reduction
- Big info research (R and Hadoop)
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