Data science using Python and R
โ Scribed by Larose, Chantal D.; Larose, Daniel T
- Publisher
- John Wiley & Sons
- Year
- 2019
- Tongue
- English
- Leaves
- 247
- Series
- Wiley series on methods and applications in data mining
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Table of Contents
Content: Chapter 1 Introduction to data science --
Chaper 2 The basics of python and R --
Chapter 3 Dath preparation --
Chapter 4 Exploratory data analysis --
Chapter 5 Preparing to model the data --
Chapter 6 Decision trees --
Chapter 7 Model evaluation --
Chapter 8 naive Bsyes classification --
Chapter 9 Neural networks --
Chapter 10 Clustering --
Chapter 11 Regression modeling --
Chapter 12 Dimension reduction --
Chapter 13 Generalized linear models --
Chapter 14 Association rules.
โฆ Subjects
Data mining.;Python (Computer program language);R (Computer program language);Big data.;Data structures (Computer science)
๐ SIMILAR VOLUMES
Over 85 recipes to help you complete real-world data science projects in R and Python About This Book - Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data - Get beyond the theory and implement real-world projects in data science using R and
<p><b>Over 85 recipes to help you complete real-world data science projects in R and Python</b></p><h2>About This Book</h2><ul><li>Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data</li><li>Get beyond the theory and implement real-world proj
<p><b>Over 85 recipes to help you complete real-world data science projects in R and Python</b></p><h2>About This Book</h2><ul><li>Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data</li><li>Get beyond the theory and implement real-world proj
<p><b>Over 85 recipes to help you complete real-world data science projects in R and Python</b></p><h2>About This Book</h2><ul><li>Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data</li><li>Get beyond the theory and implement real-world proj
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