Java Data Science Cookbook

Java Data Science Cookbook

By : Rushdi Shams

Buy this Book

Java Data Science Cookbook

By: Rushdi Shams

Buy this Book

Overview of this book

If you are looking to build data science models that are good for production, Java has come to the rescue. With the aid of strong libraries such as MLlib, Weka, DL4j, and more, you can efficiently perform all the data science tasks you need to. This unique book provides modern recipes to solve your common and not-so-common data science-related problems. We start with recipes to help you obtain, clean, index, and search data. Then you will learn a variety of techniques to analyze, learn from, and retrieve information from data. You will also understand how to handle big data, learn deeply from data, and visualize data. Finally, you will work through unique recipes that solve your problems while taking data science to production, writing distributed data science applications, and much more - things that will come in handy at work.

Java Data Science Cookbook

Credits

About the Author

About the Reviewer

www.PacktPub.com

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Preface

Free Chapter

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

Indexing and Searching Data

Introduction

Indexing data with Apache Lucene

Searching indexed data with Apache Lucene

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

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

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

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

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

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)

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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Retrieving lemma, part-of-speech, and recognizing named entities from tokens using Stanford CoreNLP

Now that we know how to extract tokens or words from a given text, we will see how we can get different types of information from the tokens such as their lemmas, part-of-speech, and whether the token is a named entity.

The process of lemmatization group inflected forms of a word together so that they can be analyzed as a single text unit. This is similar to the process of stemming with a big difference that stemming does not consider context during its grouping. Therefore, lemmatization is particularly more useful for text data analysis than stemming but requires more computation power.

Part-of-speech tags of the tokens in an article or document are widely used as features for many machine-learning models that can be useful for data scientists.

Named entities, on the other hand, are very important for news article data analysis and have very high impact on researches related to business corporations...

Java Data Science Cookbook

By : Rushdi Shams

Java Data Science Cookbook

By: Rushdi Shams

Overview of this book

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Retrieving lemma, part-of-speech, and recognizing named entities from tokens using Stanford CoreNLP