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Applied data science using PySpark = learn the end-to-end predictive model-building cycle /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied data science using PySpark/ by Ramcharan Kakarla ... [et al.].
Reminder of title:	learn the end-to-end predictive model-building cycle /
other author:	Kakarla, Ramcharan.
Published:	Berkeley, CA :Apress : : 2024.,
Description:	xviii, 449 p. :ill., digital ;24 cm.
[NT 15003449]:	Chapter 1: Setting up the Pyspark Environment -- Chapter 2: PySpark Basics -- Chapter 3: Variable Selection -- Chapter 4: Variable Selection -- Chapter 5: Supervised Learning Algorithms -- Chapter 6: Model Evaluation -- Chapter 7: Unsupervised Learning and Recommendation Algorithms -- Chapter 8: Machine Learning Flow and Automated Pipelines -- Chapter 9: Deploying machine learning models -- Chapter 10: Experimentation -- Chapter 11: Modeling Frameworks.
Contained By:	Springer Nature eBook
Subject:	Big data. -
Online resource:	https://doi.org/10.1007/979-8-8688-0820-3
ISBN:	9798868808203

Applied data science using PySpark = learn the end-to-end predictive model-building cycle /
Applied data science using PySparklearn the end-to-end predictive model-building cycle /[electronic resource] :by Ramcharan Kakarla ... [et al.]. - Second edition. - Berkeley, CA :Apress :2024. - xviii, 449 p. :ill., digital ;24 cm.

Chapter 1: Setting up the Pyspark Environment -- Chapter 2: PySpark Basics -- Chapter 3: Variable Selection -- Chapter 4: Variable Selection -- Chapter 5: Supervised Learning Algorithms -- Chapter 6: Model Evaluation -- Chapter 7: Unsupervised Learning and Recommendation Algorithms -- Chapter 8: Machine Learning Flow and Automated Pipelines -- Chapter 9: Deploying machine learning models -- Chapter 10: Experimentation -- Chapter 11: Modeling Frameworks.

This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade. In Chapters 1, 2 & 3, we will get started with setting up the environment, and the basics of PySpark focusing on data manipulations. In Chapter 4, we will dive into the art of Variable Selection where we demonstrate various selection techniques available in PySpark. In Chapters 5, 6 & 7, we take you on the journey of machine learning algorithms, implementations and fine-tuning techniques. Chapters 8 and 9 will walk you through machine learning pipelines, and various methods available to operationalize the model and serve it through docker/API. Chapter 10 will demonstrate how you can unlock the power of predictive models when used in coherence to create a meaningful impact on your business. Chapter 11 will introduce you to some of the most used and powerful modelling frameworks to unlock real value from data. In this new edition, you will learn predictive modelling frameworks that can quantify customer lifetime values and estimate the return of your predictive modelling investments. This edition also contains methods to measure engagement and identify actionable populations for churn treatments effectively. In addition, a dedicated chapter for experimentation design including steps to efficiently design, conduct, test and measure the results of your models is added. All the codes will be refreshed as needed to reflect the latest stable version of Spark. You will: Learn the overview of end to end predictive model building Understand Multiple variable selection techniques & implementations Work with Operationalizing models Perform Data science experimentations & tips.

ISBN: 9798868808203

Standard No.: 10.1007/979-8-8688-0820-3doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45

Dewey Class. No.: 005.7

Applied data science using PySpark = learn the end-to-end predictive model-building cycle /
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245 0 0 $a Applied data science using PySpark $h [electronic resource] : $b learn the end-to-end predictive model-building cycle / $c by Ramcharan Kakarla ... [et al.].
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300 $a xviii, 449 p. : $b ill., digital ; $c 24 cm.
505 0 # $a Chapter 1: Setting up the Pyspark Environment -- Chapter 2: PySpark Basics -- Chapter 3: Variable Selection -- Chapter 4: Variable Selection -- Chapter 5: Supervised Learning Algorithms -- Chapter 6: Model Evaluation -- Chapter 7: Unsupervised Learning and Recommendation Algorithms -- Chapter 8: Machine Learning Flow and Automated Pipelines -- Chapter 9: Deploying machine learning models -- Chapter 10: Experimentation -- Chapter 11: Modeling Frameworks.
520 # $a This comprehensive guide with hand-picked examples of daily use cases will walk you through the end-to-end predictive model-building cycle with the latest techniques and tricks of the trade. In Chapters 1, 2 & 3, we will get started with setting up the environment, and the basics of PySpark focusing on data manipulations. In Chapter 4, we will dive into the art of Variable Selection where we demonstrate various selection techniques available in PySpark. In Chapters 5, 6 & 7, we take you on the journey of machine learning algorithms, implementations and fine-tuning techniques. Chapters 8 and 9 will walk you through machine learning pipelines, and various methods available to operationalize the model and serve it through docker/API. Chapter 10 will demonstrate how you can unlock the power of predictive models when used in coherence to create a meaningful impact on your business. Chapter 11 will introduce you to some of the most used and powerful modelling frameworks to unlock real value from data. In this new edition, you will learn predictive modelling frameworks that can quantify customer lifetime values and estimate the return of your predictive modelling investments. This edition also contains methods to measure engagement and identify actionable populations for churn treatments effectively. In addition, a dedicated chapter for experimentation design including steps to efficiently design, conduct, test and measure the results of your models is added. All the codes will be refreshed as needed to reflect the latest stable version of Spark. You will: Learn the overview of end to end predictive model building Understand Multiple variable selection techniques & implementations Work with Operationalizing models Perform Data science experimentations & tips.
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