Name: Java Data Science Cookbook
Author: Rushdi Shams
ISBN: 9781787127654

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Java Data Science Cookbook

Rushdi Shams

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Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
Chapter 1. Obtaining and Cleaning Data
Introduction
Retrieving all filenames from hierarchical directories using Java
Retrieving all filenames from hierarchical directories using Apache Commons IO
Reading contents from text files all at once using Java 8
Reading contents from text files all at once using Apache Commons IO
Extracting PDF text using Apache Tika
Cleaning ASCII text files using Regular Expressions
Parsing Comma Separated Value (CSV) Files using Univocity
Parsing Tab Separated Value (TSV) file using Univocity
Parsing XML files using JDOM
Writing JSON files using JSON.simple
Reading JSON files using JSON.simple
Extracting web data from a URL using JSoup
Extracting web data from a website using Selenium Webdriver
Reading table data from a MySQL database
Chapter 2. Indexing and Searching Data
Introduction
Indexing data with Apache Lucene
Searching indexed data with Apache Lucene
Chapter 3. Analyzing Data Statistically
Introduction
Generating descriptive statistics
Generating summary statistics
Generating summary statistics from multiple distributions
Computing frequency distribution
Counting word frequency in a string
Counting word frequency in a string using Java 8
Computing simple regression
Computing ordinary least squares regression
Computing generalized least squares regression
Calculating covariance of two sets of data points
Calculating Pearson s correlation of two sets of data points
Conducting a paired t-test
Conducting a Chi-square test
Conducting the one-way ANOVA test
Conducting a Kolmogorov-Smirnov test
Chapter 4. Learning from Data - Part 1
Introduction
Creating and saving an Attribute-Relation File Format (ARFF) file
Cross-validating a machine learning model
Classifying unseen test data
Classifying unseen test data with a filtered classifier
Generating linear regression models
Generating logistic regression models
Clustering data points using the KMeans algorithm
Clustering data from classes
Learning association rules from data
Selecting features/attributes using the low-level method, the filtering method, and the meta-classifier method
Chapter 5. Learning from Data - Part 2
Introduction
Applying machine learning on data using Java Machine Learning (Java-ML) library
Classifying data points using the Stanford classifier
Classifying data points using Massive Online Analysis (MOA)
Classifying multilabeled data points using Mulan
Chapter 6. Retrieving Information from Text Data
Introduction
Detecting tokens (words) using Java
Detecting sentences using Java
Detecting tokens (words) and sentences using OpenNLP
Retrieving lemma, part-of-speech, and recognizing named entities from tokens using Stanford CoreNLP
Measuring text similarity with Cosine Similarity measure using Java 8
Extracting topics from text documents using Mallet
Classifying text documents using Mallet
Classifying text documents using Weka
Chapter 7. Handling Big Data
Introduction
Training an online logistic regression model using Apache Mahout
Applying an online logistic regression model using Apache Mahout
Solving simple text mining problems with Apache Spark
Clustering using KMeans algorithm with MLib
Creating a linear regression model with MLib
Classifying data points with Random Forest model using MLib
Chapter 8. Learn Deeply from Data
Introduction
Creating a Word2vec neural net using Deep Learning for Java (DL4j)
Creating a Deep Belief neural net using Deep Learning for Java (DL4j)
Creating a deep autoencoder using Deep Learning for Java (DL4j)
Chapter 9. Visualizing Data
Introduction
Plotting a 2D sine graph
Plotting histograms
Plotting a bar chart
Plotting box plots or whisker diagrams
Plotting scatter plots
Plotting donut plots
Plotting area graphs

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Java Data Science Cookbook