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Home > Data > Machine Learning > Apache Spark Machine Learning Blueprints
Apache Spark Machine Learning Blueprints
Apache Spark Machine Learning Blueprints

Apache Spark Machine Learning Blueprints: Develop a range of cutting-edge machine learning projects with Apache Spark using this actionable guide

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Apache Spark Machine Learning Blueprints

Chapter 2. Data Preparation for Spark ML

Machine learning professionals and data scientists often spend 70% or 80% of their time preparing data for their machine learning projects. Data preparation can be very hard work, but it is necessary and extremely important as it affects everything to follow. Therefore, in this chapter, we will cover all the necessary data preparation parts for our machine learning, which often runs from data accessing, data cleaning, datasets joining, and then to feature development so as to get our datasets ready to develop ML models on Spark. Specifically, we will discuss the following six data preparation tasks mentioned before and then end our chapter with a discussion of repeatability and automation:

  • Accessing and loading datasets
    • Publicly available datasets for ML
    • Loading datasets into Spark easily
    • Exploring and visualizing data with Spark
  • Data cleaning
    • Dealing with missing cases and incompleteness
    • Data cleaning on Spark
    • Data cleaning made easy
  • Identity matching...

Accessing and loading datasets

In this section, we will review some publicly available datasets and cover methods of loading some of these datasets into Spark. Then, we will review several methods of exploring and visualizing these datasets on Spark.

After this section, we will be able to find some datasets to use, load them into Spark, and then start to explore and visualize this data.

Accessing publicly available datasets

As there is an open source movement to make software free, there is also a very active open data movement that made a lot of datasets freely accessible to every researcher and analyst. At a worldwide scale, most governments make their collected datasets open to the public. For example, on http://www.data.gov/, there are more than 140,000 datasets available to be used freely, which are spread over agriculture, finance, and education.

Besides open data coming from various governmental organizations, many research institutions also collect a lot of very useful datasets and...

Data cleaning

In this section, we will review some methods for data cleaning on Spark with a focus on data incompleteness. Then, we will discuss some of Spark's special features for data cleaning and also some data cleaning solutions made easy with Spark.

After this section, we will be able to clean data and make datasets ready for machine learning.

Dealing with data incompleteness

For machine learning, the more the data the better. However, as is often the case, the more the data, the dirtier it could be—that is, the more the work to clean the data.

There are many issues to deal with data quality control, which can be as simple as data entry errors or data duplications. In principal, the methods of treating them are similar—for example, utilizing data logic for discovery and subject matter knowledge and analytical logic to correct them. For this reason, in this section, we will focus on missing value treatment so as to illustrate our usage of Spark for this topic. Data cleaning...

Identity matching

In this section, we will cover one important data preparation topic, which is about identity matching and related solutions. We will discuss some of Spark's special features for solving identity issues and also some data matching solutions made easy with Spark.

After this section, we will be capable of taking care of some common data identity problems with Apache Spark.

Identity issues

For data preparation, we often need to deal with some data elements that belong to the same person or units, but which do not look similar to them. For example, we may have purchased some data for customer Larry Z. and web activity data for L. Zhang. Is Larry Z a same person as L. Zhang? Are there many identity variations in the data?

Matching entities is a big challenge for machine learning data preparation as these types of entity variation are very common and could be caused by many different reasons, such as duplications, errors, name variants, and intentional aliasing. Sometimes, it...

Dataset reorganizing

In this section, we will cover dataset reorganization techniques. Then, we will discuss some of Spark's special features for data reorganizing and also some of R's special methods for data reorganizing that can be used with the Spark notebook.

After this section, we will be able to reorganize datasets for various machine learning needs.

Dataset reorganizing tasks

Reorganizing datasets sounds easy but could be very challenging and also often very time consuming.

Two common data reorganizing tasks are—firstly, to obtain a subset of the data for modeling and, secondly, to aggregate data to a higher level. For example, we have students' data, but we need to have a dataset at the classroom level. For this, we will need to calculate some attributes for students and then reorganize it into new data.

To work with data reorganizing, data scientists and machine learning professionals often utilize their familiar SQL or R programming tools. Fortunately within...

Dataset joining

In this section, we will cover dataset joining techniques. We will also discuss some of Spark's special features for data joining plus some data joining solutions made easy with Spark.

After this section, we will be able to join data for various machine learning needs.

Dataset joining and its tool – the Spark SQL

In preparing datasets for a machine learning project, we often need to combine data from multiple datasets. For relational tables, the task is to join tables through a primary and foreign key relationship.

Joining two or more datasets together sounds easy, but can be very challenging and time consuming. In SQL, SELECT is the most frequently used command. As an example, the following is a typical SQL code to perform a join:

SELECT column1, column2, …
FROM table1, table2
WHERE table1.joincolumn = table2.joincolumn
AND search_condition(s);

To work with the table joining tasks mentioned before, data scientists and machine learning professionals often utilize...

Accessing and loading datasets

In this section, we will review some publicly available datasets and cover methods of loading some of these datasets into Spark. Then, we will review several methods of exploring and visualizing these datasets on Spark.

After this section, we will be able to find some datasets to use, load them into Spark, and then start to explore and visualize this data.

Accessing publicly available datasets

As there is an open source movement to make software free, there is also a very active open data movement that made a lot of datasets freely accessible to every researcher and analyst. At a worldwide scale, most governments make their collected datasets open to the public. For example, on http://www.data.gov/, there are more than 140,000 datasets available to be used freely, which are spread over agriculture, finance, and education.

Besides open data coming from various governmental organizations, many research institutions also collect a lot of very useful datasets and make...

Data cleaning

In this section, we will review some methods for data cleaning on Spark with a focus on data incompleteness. Then, we will discuss some of Spark's special features for data cleaning and also some data cleaning solutions made easy with Spark.

After this section, we will be able to clean data and make datasets ready for machine learning.

Dealing with data incompleteness

For machine learning, the more the data the better. However, as is often the case, the more the data, the dirtier it could be—that is, the more the work to clean the data.

There are many issues to deal with data quality control, which can be as simple as data entry errors or data duplications. In principal, the methods of treating them are similar—for example, utilizing data logic for discovery and subject matter knowledge and analytical logic to correct them. For this reason, in this section, we will focus on missing value treatment so as to illustrate our usage of Spark for this topic. Data cleaning covers data...

Identity matching

In this section, we will cover one important data preparation topic, which is about identity matching and related solutions. We will discuss some of Spark's special features for solving identity issues and also some data matching solutions made easy with Spark.

After this section, we will be capable of taking care of some common data identity problems with Apache Spark.

Identity issues

For data preparation, we often need to deal with some data elements that belong to the same person or units, but which do not look similar to them. For example, we may have purchased some data for customer Larry Z. and web activity data for L. Zhang. Is Larry Z a same person as L. Zhang? Are there many identity variations in the data?

Matching entities is a big challenge for machine learning data preparation as these types of entity variation are very common and could be caused by many different reasons, such as duplications, errors, name variants, and intentional aliasing. Sometimes, it could...

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Key benefits

  • Customize Apache Spark and R to fit your analytical needs in customer research, fraud detection, risk analytics, and recommendation engine development
  • Develop a set of practical Machine Learning applications that can be implemented in real-life projects
  • A comprehensive, project-based guide to improve and refine your predictive models for practical implementation

Description

There's a reason why Apache Spark has become one of the most popular tools in Machine Learning – its ability to handle huge datasets at an impressive speed means you can be much more responsive to the data at your disposal. This book shows you Spark at its very best, demonstrating how to connect it with R and unlock maximum value not only from the tool but also from your data. Packed with a range of project "blueprints" that demonstrate some of the most interesting challenges that Spark can help you tackle, you'll find out how to use Spark notebooks and access, clean, and join different datasets before putting your knowledge into practice with some real-world projects, in which you will see how Spark Machine Learning can help you with everything from fraud detection to analyzing customer attrition. You'll also find out how to build a recommendation engine using Spark's parallel computing powers.

Who is this book for?

If you are a data scientist, a data analyst, or an R and SPSS user with a good understanding of machine learning concepts, algorithms, and techniques, then this is the book for you. Some basic understanding of Spark and its core elements and application is required.

What you will learn

  • Set up Apache Spark for machine learning and discover its impressive processing power
  • Combine Spark and R to unlock detailed business insights essential for decision making
  • Build machine learning systems with Spark that can detect fraud and analyze financial risks
  • Build predictive models focusing on customer scoring and service ranking
  • Build a recommendation systems using SPSS on Apache
  • Spark
  • Tackle parallel computing and find out how it can support your machine learning projects
  • Turn open data and communication data into actionable insights by making use of various forms of machine learning

Product Details

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Publication date : May 30, 2016
Length: 252 pages
Edition : 1st
Language : English
ISBN-13 : 9781785887789
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Category :
Data
Languages :
R
Concepts :
Machine Learning
Tools :
Apache Spark

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Publication date : May 30, 2016
Length: 252 pages
Edition : 1st
Language : English
ISBN-13 : 9781785887789
Vendor :
Apache
Category :
Data
Languages :
R
Concepts :
Machine Learning
Tools :
Apache Spark

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Table of Contents

12 Chapters
1. Spark for Machine Learning Chevron down icon Chevron up icon
1. Spark for Machine Learning
Spark overview and Spark advantages
Spark computing for machine learning
Machine learning algorithms
MLlib
Spark RDD and dataframes
ML workflows and Spark pipelines
ML workflow examples
Spark notebooks
Summary
2. Data Preparation for Spark ML Chevron down icon Chevron up icon
2. Data Preparation for Spark ML
Accessing and loading datasets
Data cleaning
Identity matching
Dataset reorganizing
Dataset joining
Feature extraction
Repeatability and automation
Summary
3. A Holistic View on Spark Chevron down icon Chevron up icon
3. A Holistic View on Spark
Spark for a holistic view
Methods for a holistic view
Feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
4. Fraud Detection on Spark Chevron down icon Chevron up icon
4. Fraud Detection on Spark
Spark for fraud detection
Methods for fraud detection
Feature preparation
Model estimation
Model evaluation
Results explanation
Deploying fraud detection
Summary
5. Risk Scoring on Spark Chevron down icon Chevron up icon
5. Risk Scoring on Spark
Spark for risk scoring
Methods of risk scoring
Data and feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
6. Churn Prediction on Spark Chevron down icon Chevron up icon
6. Churn Prediction on Spark
Spark for churn prediction
Methods for churn prediction
Feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
7. Recommendations on Spark Chevron down icon Chevron up icon
7. Recommendations on Spark
Apache Spark for a recommendation engine
Methods for recommendation
Data treatment with SPSS
Model estimation
Model evaluation
Recommendation deployment
Summary
8. Learning Analytics on Spark Chevron down icon Chevron up icon
8. Learning Analytics on Spark
Spark for attrition prediction
Methods of attrition prediction
Feature preparation
Model estimation
Model evaluation
Results explanation
Deployment
Summary
9. City Analytics on Spark Chevron down icon Chevron up icon
9. City Analytics on Spark
Spark for service forecasting
Data and feature preparation
Model estimation
Model evaluation
Explanations of the results
Summary
10. Learning Telco Data on Spark Chevron down icon Chevron up icon
10. Learning Telco Data on Spark
Spark for using Telco Data
Methods for learning from Telco Data
Data and feature development
Model estimation
Model evaluation
Results explanation
Model deployment
Summary
11. Modeling Open Data on Spark Chevron down icon Chevron up icon
11. Modeling Open Data on Spark
Spark for learning from open data
Data and feature preparation
Model estimation
Results explanation
Deployment
Summary
Index Chevron down icon Chevron up icon
Index

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Rating distribution
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(1 Ratings)
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Sven Feb 17, 2017
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Vous trouverez beaucoup mieux directement sur la documentation de Spark ou sur google. Ce livre n'apporte aucune plus value, ni dans la forme, ni dans la structure, ni dans le contenu, ni dans les use cases, ni dans son originalité,...
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About the author

Profile icon Alex Liu
Alex Liu
Alex Liu is an expert in research methods and data science. He is currently one of IBM's leading experts in Big Data analytics and also a lead data scientist, where he serves big corporations, develops Big Data analytics IPs, and speaks at industrial conferences such as STRATA, Insights, SMAC, and BigDataCamp. In the past, Alex served as chief or lead data scientist for a few companies, including Yapstone, RS, and TRG. Before this, he was a lead consultant and director at RMA, where he provided data analytics consultation and training to many well-known organizations, including the United Nations, Indymac, AOL, Ingram Micro, GEM, Farmers Insurance, Scripps Networks, Sears, and USAID. At the same time, he taught advanced research methods to PhD candidates at University of Southern California and University of California at Irvine. Before this, he worked as a managing director for CATE/GEC and as a research fellow for the Asia/Pacific Research Center at Stanford University. Alex has a Ph.D. in quantitative sociology and a master's degree of science in statistical computing from Stanford University.
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