Applied Data Science Using Pyspark: Learn the End-To-End Predictive

Description: Discover the capabilities of PySpark and its application in the realm of data science. 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. Applied Data Science Using PySpark is divided unto six sections which walk you through the book. In section 1, you start with the basics of PySpark focusing on data manipulation. We make you comfortable with the language and then build upon it to introduce you to the mathematical functions available off the shelf. In section 2, you will dive into the art of variable selection where we demonstrate various selection techniques available in PySpark. In section 3, we take you on a journey through machine learning algorithms, implementations, and fine-tuning techniques. We will also talk about different validation metrics and how to use them for picking the best models. Sections 4 and 5 go through machine learning pipelines and various methods available to operationalize the model and serve it through Docker/an API. In the final section, you will cover reusable objects for easy experimentation and learn some tricks that can help you optimize your programs and machine learning pipelines. By the end of this book, you will have seen the flexibility and advantages of PySpark in data science applications. This book is recommended to those who want to unleash the power of parallel computing by simultaneously working with big datasets. What You Will Learn Build an end-to-end predictive model Implement multiple variable selection techniques Operationalize models Master multiple algorithms and implementations Who This Book is For Data scientists and machine learning and deep learning engineers who want to learn and use PySpark for real-time analysis of streaming data. Chapter 1: Setting up the Pyspark Environment Chapter Goal: Introduce readers to the PySpark environment, walk them through steps to setup the environment and execute some basic operations Number of pages: 20 Subtopics: 1. Setting up your environment & data2. Basic operations Chapter 2: Basic Statistics and Visualizations Chapter Goal: Introduce readers to predictive model building framework and help them acclimate with basic data operations Number of pages: 30 Subtopics: 1. Basic Statistics 2. data manipulations/feature engineering 3. Data visualizations4. Model building framework Chapter 3: Variable Selection Chapter Goal: Illustrate the different variable selection techniques to identify the top variables in a dataset and how they can be implemented using PySpark pipelines Number of pages: 40 Subtopics:1. Principal Component Analysis 2. Weight of Evidence & Information Value 3. Chi square selector 4. Singular Value Decomposition 5. Voting based approach Chapter 4: Introduction to different supervised machine algorithms, implementations & Fine-tuning techniques Chapter Goal: Explain and demonstrate supervised machine learning techniques and help the readers to understand the challenges, nuances of model fitting with multiple evaluation metrics Number of pages: 40 Subtopics: 1. Supervised: · Linear regression · Logistic regression · Decision Trees · Random Forests · Gradient Boosting · Neural Nets · Support Vector Machine · One Vs Rest Classifier · Naive Bayes 2. Model hyperparameter tuning:· L1 & L2 regularization · Elastic net Chapter 5: Model Validation and selecting the best model Chapter Goal: Illustrate the different techniques used to validate models, demonstrate which technique should be used for a particular model selection task and finally pick the best model out of the candidate models Number of pages: 30 Subtopics: 1. Model Validation Statistics: · ROC· Accuracy · Precision · Recall · F1 Score · Misclassification · KS · Decile · Lift & Gain · R square · Adjusted R square · Mean squared error Chapter 6: Unsupervised and recommendation algorithms Chapter Goal: The readers explore a different set of algorithms - Unsupervised and recommendation algorithms and the use case of when to apply them Number of pages: 30 Subtopics: 1. Unsupervised:· K-Means · Latent Dirichlet Allocation 2. Collaborative filtering using Alternating least squares Chapter 7: End to end modeling pipelines Chapter Goal: Exemplify building the automated model framework and introduce reader to a end to end model building pipeline including experimentation and model tracking Number of pages: 40 Subtopics: 1. ML Flow Chapter 8: Productionalizing a machine learning model Chapter Goal: Demonstrate multiple model deployment techniques that can fit and serve variety of real-world use cases Number of pages: 60 Subtopics: 1. Model Deployment using hdfs object 2. Model Deployment using Docker 3. Creating a simple Flask API Chapter 9: Experimentations Chapter Goal: The purpose of this chapter is to introduce hypothesis testing and use cases, optimizations for experiment-based data science applications Number of pages: 40 Subtopics: 1. Hypothesis testing 2. Sampling techniques Chapter 10: Other Tips: Optional Chapter Goal: This bonus chapter is optional and will offer reader some handy tips and tricks of the trade Number of pages: 20 Subtopics: 1. Tips on when to switch between python and PySpark 2. Graph networks

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