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Distributed machine learning with Pyspark : migrating effortlessly from Pandas and Scikit-Learn / Abdelaziz Testas.

Testas, Abdelaziz, author.
2023
Q325.5 .T47 2023
Available Online
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Title
Distributed machine learning with Pyspark : migrating effortlessly from Pandas and Scikit-Learn / Abdelaziz Testas.
Author
Testas, Abdelaziz, author.
ISBN
9781484297513 electronic book
1484297512 electronic book
1484297504
9781484297506
DOI
https://doi.org/10.1007/978-1-4842-9751-3
Published
Berkeley, CA : Apress L. P., [2023]
Language
English
Description
1 online resource (500 p.)
Item Number
10.1007/978-1-4842-9751-3 doi
Call Number
Q325.5 .T47 2023
Dewey Decimal Classification
006.3/1
Summary
Migrate from pandas and scikit-learn to PySpark to handle vast amounts of data and achieve faster data processing time. This book will show you how to make this transition by adapting your skills and leveraging the similarities in syntax, functionality, and interoperability between these tools. Distributed Machine Learning with PySpark offers a roadmap to data scientists considering transitioning from small data libraries (pandas/scikit-learn) to big data processing and machine learning with PySpark. You will learn to translate Python code from pandas/scikit-learn to PySpark to preprocess large volumes of data and build, train, test, and evaluate popular machine learning algorithms such as linear and logistic regression, decision trees, random forests, support vector machines, Nave Bayes, and neural networks. After completing this book, you will understand the foundational concepts of data preparation and machine learning and will have the skills necessary to apply these methods using PySpark, the industry standard for building scalable ML data pipelines. You will: Master the fundamentals of supervised learning, unsupervised learning, NLP, and recommender systems Understand the differences between PySpark, scikit-learn, and pandas Perform linear regression, logistic regression, and decision tree regression with pandas, scikit-learn, and PySpark Distinguish between the pipelines of PySpark and scikit-learn.
Note
Naive Bayes with Scikit-Learn
Bibliography, etc. Note
Includes bibliographical references and index.
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Access limited to authorized users.
Source of Description
Description based on online resource; title from digital title page (viewed on December 12, 2023).
Available in Other Form
Distributed Machine Learning with Pyspark
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Intro
Table of Contents
About the Author
About the Technical Reviewer
Acknowledgments
Introduction
Chapter 1: An Easy Transition
PySpark and Pandas Integration
Similarity in Syntax
Loading Data
Selecting Columns
Aggregating Data
Filtering Data
Joining Data
Saving Data
Modeling Steps
Pipelines
Summary
Chapter 2: Selecting Algorithms
The Dataset
Selecting Algorithms with Cross-Validation
Scikit-Learn
PySpark
Bringing It All Together
Scikit-Learn
PySpark
Summary

Chapter 3: Multiple Linear Regression with Pandas, Scikit-Learn, and PySpark
The Dataset
Multiple Linear Regression
Multiple Linear Regression with Scikit-Learn
Multiple Linear Regression with PySpark
Summary
Chapter 4: Decision Tree Regression with Pandas, Scikit-Learn, and PySpark
The Dataset
Decision Tree Regression
Decision Tree Regression with Scikit-Learn
The Modeling Steps
Decision Tree Regression with PySpark
The Modeling Steps
Bringing It All Together
Scikit-Learn
PySpark
Summary

Chapter 5: Random Forest Regression with Pandas, Scikit-Learn, and PySpark
The Dataset
Random Forest Regression
Random Forest with Scikit-Learn
Random Forest with PySpark
Bringing It All Together
Scikit-Learn
PySpark
Summary
Chapter 6: Gradient-Boosted Tree Regression with Pandas, Scikit-Learn, and PySpark
The Dataset
Gradient-Boosted Tree (GBT) Regression
GBT with Scikit-Learn
GBT with PySpark
Bringing It All Together
Scikit-Learn
PySpark
Summary
Chapter 7: Logistic Regression with Pandas, Scikit-Learn, and PySpark
The Dataset

Logistic Regression
Logistic Regression with Scikit-Learn
Logistic Regression with PySpark
Putting It All Together
Scikit-Learn
PySpark
Summary
Chapter 8: Decision Tree Classification with Pandas, Scikit-Learn, and PySpark
The Dataset
Decision Tree Classification
Scikit-Learn and PySpark Similarities
Decision Tree Classification with Scikit-Learn
Decision Tree Classification with PySpark
Bringing It All Together
Scikit-Learn
PySpark
Summary
Chapter 9: Random Forest Classification with Scikit- Learn and PySpark
Random Forest Classification

Scikit-Learn and PySpark Similarities for Random Forests
Random Forests with Scikit-Learn
Random Forests with PySpark
Bringing It All Together
Scikit-Learn
PySpark
Summary
Chapter 10: Support Vector Machine Classification with Pandas, Scikit-Learn, and PySpark
The Dataset
Support Vector Machine Classification
Linear SVM with Scikit-Learn
Linear SVM with PySpark
Bringing It All Together
Scikit-Learn
PySpark
Summary
Chapter 11: Naive Bayes Classification with Pandas, Scikit-Learn, and PySpark
The Dataset
Naive Bayes Classification

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