book

Apache Spark for Data Science Cookbook

Name: Apache Spark for Data Science Cookbook
Author: Padma Priya Chitturi
ISBN: 9781785880100

by Padma Priya Chitturi

December 2016

Beginner to intermediate

392 pages

8h 13m

English

Packt Publishing

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Apache Spark for Data Science Cookbook
Apache Spark for Data Science Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Why subscribe?
Customer Feedback
Preface
What this book covers
What you need for this book
Who this book is for

Sections
Getting readyHow to do it…How it works…There's more…See also
Conventions
Reader feedback
Customer support
Downloading the example codeErrataPiracyQuestions
1. Big Data Analytics with Spark
Introduction
Initializing SparkContext
Getting readyHow to do it…How it works…There's more…See also
Working with Spark's Python and Scala shells
How to do it…How it works…There's more…See also
Building standalone applications
Getting readyHow to do it…How it works…There's more…See also
Working with the Spark programming model
How to do it…How it works…There's more…See also
Working with pair RDDs
Getting readyHow to do it…How it works…There's more…See also
Persisting RDDs
Getting readyHow to do it…How it works…There's more…See also
Loading and saving data
Getting readyHow to do it…How it works…There's more…See also
Creating broadcast variables and accumulators
Getting readyHow to do it…How it works…There's more…See also
Submitting applications to a cluster
Getting readyHow to do it…How it works…There's more…See also
Working with DataFrames
Getting readyHow to do it…How it works…There's more…See also
Working with Spark Streaming
Getting readyHow to do it…How it works…There's more…See also
2. Tricky Statistics with Spark
IntroductionWorking with Pandas
Variable identification
Getting readyHow to do it…How it works…There's more…See also
Sampling data
Getting readyHow to do it…How it works…There's more…See also
Summary and descriptive statistics
Getting readyHow to do it…How it works…There's more…See also
Generating frequency tables
Getting readyHow to do it…How it works…There's more…See also
Installing Pandas on Linux
Getting readyHow to do it…How it works…There's more…See also
Installing Pandas from source
Getting readyHow to do it…How it works…There's more…See also
Using IPython with PySpark
Getting readyHow to do it…How it work…There's more…See also
Creating Pandas DataFrames over Spark
Getting readyHow to do it…How it works…There's more…See also
Splitting, slicing, sorting, filtering, and grouping DataFrames over Spark
Getting readyHow to do it…How it works…There's more…See also
Implementing co-variance and correlation using Pandas
Getting readyHow to do it…How it works…There's more…See also
Concatenating and merging operations over DataFrames
Getting readyHow to do it…How it works…There's more…See also
Complex operations over DataFrames
Getting readyHow to do it…How it works…There's more…See also
Sparkling Pandas
Getting readyHow to do it…How it works…There's more…See also
3. Data Analysis with Spark
Introduction
Univariate analysis
Getting readyHow to do it…How it works…There's more…See also
Bivariate analysis
Getting readyHow to do it…How it works…There's more…See also
Missing value treatment
Getting readyHow to do it…How it works…There's more…See also
Outlier detection
Getting readyHow to do it…How it works…There's more…See also
Use case - analyzing the MovieLens dataset
Getting readyHow to do it…How it works…There's more…See also
Use case - analyzing the Uber dataset
Getting readyHow to do it…How it works…There's more…See also
4. Clustering, Classification, and Regression
Introduction
Supervised learning
Unsupervised learning
Applying regression analysis for sales data
Variable identification
Getting readyHow to do it…How it works…There's more…See also
Data exploration
Getting readyHow to do it…How it works…There's more…See also
Feature engineering
Getting readyHow to do it…How it works…There's more…See also
Applying linear regression
Getting readyHow to do it…How it works…There's more…See also
Applying logistic regression on bank marketing data
Variable identification
Getting readyHow to do it…How it works…There's more…See also
Data exploration
Getting readyHow to do it…How it works…There's more…See also
Feature engineering
Getting readyHow to do it…How it works…There's more…See also
Applying logistic regression
Getting readyHow to do it…How it works…There's more…See also
Real-time intrusion detection using streaming k-means
Variable identification
Getting readyHow to do it…How it works…There's more…See also
Simulating real-time data
Getting readyHow to do it…How it works…There's more…See also
Applying streaming k-means
Getting readyHow to do it…How it works…There's more…See also
5. Working with Spark MLlib
Introduction
Working with Spark ML pipelines
Implementing Naive Bayes' classification
Getting readyHow to do it…How it works…There's more…See also
Implementing decision trees
Getting readyHow to do it…How it works…There's more…See also
Building a recommendation system
Getting readyHow to do it…How it works…There's more…See also
Implementing logistic regression using Spark ML pipelines
Getting readyHow to do it…How it works…There's more…See also
6. NLP with Spark
Introduction
Installing NLTK on Linux
Getting readyHow to do it…How it works…There's more…See also
Installing Anaconda on Linux
Getting readyHow to do it…How it works…There's more…See also
Anaconda for cluster management
Getting readyHow to do it…How it works…There's more…See also
POS tagging with PySpark on an Anaconda cluster
Getting readyHow to do it…How it works…There's more…See also
NER with IPython over Spark
Getting readyHow to do it…How it works…There's more…See also
Implementing openNLP - chunker over Spark
Getting readyHow to do it…How it works…There's more…See also
Implementing openNLP - sentence detector over Spark
Getting readyHow to do it…How it works…There's more…See also
Implementing stanford NLP - lemmatization over Spark
Getting readyHow to do it…How it works…There's more…See also
Implementing sentiment analysis using stanford NLP over Spark
Getting readyHow to do it…How it works…There's more…See also
7. Working with Sparkling Water - H2O
Introduction
Features
Working with H2O on Spark
Getting readyHow to do it…How it works…There's more…See also
Implementing k-means using H2O over Spark
Getting readyHow to do it…How it works…There's more…See also
Implementing spam detection with Sparkling Water
Getting readyHow to do it…How it works…There's more…See also
Deep learning with airlines and weather data
Getting readyHow to do it…How it works…There's more…See also
Implementing a crime detection application
Getting readyHow to do it…How it works…There's more…See also
Running SVM with H2O over Spark
Getting readyHow to do it…How it works…There's more…See also
8. Data Visualization with Spark
Introduction
Visualization using Zeppelin
Getting readyHow to do it…
Installing Zeppelin
Customizing Zeppelin's server and websocket port
Visualizing data on HDFS - parameterizing inputs
Running custom functions
Adding external dependencies to Zeppelin
Pointing to an external Spark Cluster
How to do it…How it works…There's more…See also
Creating scatter plots with Bokeh-Scala
Getting readyHow to do it…How it works…There's more…See also
Creating a time series MultiPlot with Bokeh-Scala
Getting readyHow to do it…How it work…There's more…See also
Creating plots with the lightning visualization server
Getting readyHow to do it…How it works…There's more…See also
Visualize machine learning models with Databricks notebook
Getting readyHow to do it…How it works…There's more…See also
9. Deep Learning on Spark
Introduction
Installing CaffeOnSpark
Getting readyHow to do it…How it works…There's more…See also
Working with CaffeOnSpark
Getting readyHow to do it…How it works…There's more…See also
Running a feed-forward neural network with DeepLearning 4j over Spark
Getting readyHow to do it…How it works…There's more…See also
Running an RBM with DeepLearning4j over Spark
Getting readyHow to do it…How it works…There's more…See also
Running a CNN for learning MNIST with DeepLearning4j over Spark
Getting readyHow to do it…How it works…There's more…See also
Installing TensorFlow
Getting readyHow to do it…How it works…There's more…See also
Working with Spark TensorFlow
Getting readyHow to do it…How it works…There's more…See also
10. Working with SparkR
Introduction
Installing R
Getting ready…How to do it…How it works…There's more…See also
Interactive analysis with the SparkR shell
Getting readyHow to do it…How it works…There's more…See also
Creating a SparkR standalone application from RStudio
Getting readyHow to do it…How it works…There's more…See also
Creating SparkR DataFrames
Getting readyHow to do it…How it works…There's more…See also
SparkR DataFrame operations
Getting readyHow to do it…How it works…There's more…See also
Applying user-defined functions in SparkR
Getting readyHow to do it…How it works…There's more…See also
Running SQL queries from SparkR and caching DataFrames
Getting readyHow to do it…How it works…There's more…See also
Machine learning with SparkR
Getting readyHow to do it…How it works…There's more…See also

Content preview from Apache Spark for Data Science Cookbook

Implementing spam detection with Sparkling Water

In this recipe, we'll look at how to implement a spam detector by extracting data, transforming and tokenizing messages, building Spark's Tf-IDF model, and expanding messages to feature vectors. We'll also create and evaluate H2O's deep learning model. Lastly, we will use the models to detect spam messages.

Getting ready

To step through this recipe, you will need a running Spark Cluster in any one of the following modes: Local, standalone, YARN, Mesos. Include the Spark MLlib package in the build.sbt file so that it downloads the related libraries and the API can be used. Install Hadoop (optionally), Scala, and Java. Also, install Sparkling Water as discussed in the preceding recipe.

How to do it… ...

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