04452nam a22005535i 4500 979-8-8688-0820-3 DE-He213 20251030114558.0 cr nn 008mamaa 241201s2024 xxu| s |||| 0|eng d 9798868808203 979-8-8688-0820-3 10.1007/979-8-8688-0820-3 doi QA76.7-.73 UMX bicssc COM051010 bisacsh UMX thema Kakarla, Ramcharan. author. aut http://id.loc.gov/vocabulary/relators/aut 88412 Applied Data Science Using PySpark [electronic resource] : Learn the End-to-End Predictive Model-Building Cycle / by Ramcharan Kakarla, Sundar Krishnan, Balaji Dhamodharan, Venkata Gunnu. 2nd ed. 2024. Berkeley, CA : Apress : Imprint: Apress, 2024. XVIII, 449 p. 203 illus. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda 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. Programming languages (Electronic computers). 66206 Big data. 57216 Machine learning. 55059 Programming Language. 66208 Big Data. 57216 Machine Learning. 55059 Krishnan, Sundar. author. aut http://id.loc.gov/vocabulary/relators/aut 88413 Dhamodharan, Balaji. author. aut http://id.loc.gov/vocabulary/relators/aut 88414 Gunnu, Venkata. author. aut http://id.loc.gov/vocabulary/relators/aut 88415 SpringerLink (Online service) Springer Nature eBook Printed edition: 9798868808197 Printed edition: 9798868808210 Printed edition: 9798868813757 https://doi.org/10.1007/979-8-8688-0820-3 ZDB-2-CWD ZDB-2-SXPC 65407 65407