Data Mining for Business Analytics: Concepts, Techniques, and Applications with JMP Pro presents an applied and interactive approach to data mining.<br>Featuring hands-on applications with JMP Pro, a statistical package from the SAS Institute, the book uses engaging, real-world example
Data mining for business analytics : concepts, techniques, and applications in JMP Pro
โ Scribed by Bruce, Peter C.; Patel, Nitin Ratilal; Shmueli, Galit; Stephens, Mia L.
- Publisher
- John Wiley & Sons
- Year
- 2017
- Tongue
- English
- Leaves
- 467
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Table of Contents
Overview of the data mining process --
Data visualization --
Dimension reduction --
Evaluating predictive performance --
Multiple linear regression --
K-nearest neighbors (kNN) --
The naive Bayes classifier --
Classification and regression trees --
Logistic regression --
Neural nets --
Discriminant analysis --
Combining methods : ensembles and uplift modeling --
Cluster analysis --
Handling time series --
Regression-based forecasting --
Smoothing methods --
Cases.
โฆ Subjects
Business mathematics -- Computer programs;Business -- Data processing;JMP (Computer file);Data mining;COMPUTERS / General
๐ SIMILAR VOLUMES
<p><span>Data Mining for Business Analytics: Concepts, Techniques, and Applications in Python</span><span> presents an applied approach to data mining concepts and methods, using Python software for illustration</span></p><p><span>Readers will learn how to implement a variety of popular data mining
<p><b><i>Data Mining for Business Analytics: Concepts, Techniques, and Applications in R </i></b><b>presents an applied approach to data mining concepts and methods, using R software for illustration</b></p> <p>Readers will learn how to implement a variety of popular data mining algorithms in R (a f
This book supplies insightful, detailed guidance on fundamental data mining techniques. The book guides readers through the use of Python software for developing predictive models and techniques in order to describe and find patterns in data. The authors use interesting, real-world examples to build
<b><i>Data Mining for Business Analytics: Concepts, Techniques, and Applications in R</i>presents an applied approach to data mining concepts and methods, using R software for illustration</b><br /><br />Readers will learn how to implement a variety of popular data mining algorithms in R (a free and