Book Image

Mastering Data Mining with Python - Find patterns hidden in your data

By : Megan Squire
Book Image

Mastering Data Mining with Python - Find patterns hidden in your data

By: Megan Squire

Overview of this book

Data mining is an integral part of the data science pipeline. It is the foundation of any successful data-driven strategy – without it, you'll never be able to uncover truly transformative insights. Since data is vital to just about every modern organization, it is worth taking the next step to unlock even greater value and more meaningful understanding. If you already know the fundamentals of data mining with Python, you are now ready to experiment with more interesting, advanced data analytics techniques using Python's easy-to-use interface and extensive range of libraries. In this book, you'll go deeper into many often overlooked areas of data mining, including association rule mining, entity matching, network mining, sentiment analysis, named entity recognition, text summarization, topic modeling, and anomaly detection. For each data mining technique, we'll review the state-of-the-art and current best practices before comparing a wide variety of strategies for solving each problem. We will then implement example solutions using real-world data from the domain of software engineering, and we will spend time learning how to understand and interpret the results we get. By the end of this book, you will have solid experience implementing some of the most interesting and relevant data mining techniques available today, and you will have achieved a greater fluency in the important field of Python data analytics.
Table of Contents (16 chapters)
Mastering Data Mining with Python – Find patterns hidden in your data
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Gensim for topic modeling


We used the Gensim library already in Chapter 7, Automatic Text Summarization for extracting keywords and summaries of text. Here we will use it for building a topic model of a collection of texts. Just as we did in earlier chapters, we will practice with a few different types of document collections and see how the results vary.

First, we will build a small test program to make sure that Gensim and LDA are installed correctly and able to generate a topic model from a collection of documents. If Gensim is not loaded into your version of Anaconda, simply run conda install gensim in your terminal.

We begin with importing the Gensim libraries and a PrettyPrinter for formatting:

from gensim import corpora
from gensim.models.ldamodel import LdaModel
from gensim.parsing.preprocessing import STOPWORDS
import pprint

We will need some variables to serve as ways of adjusting the model. As we learn how topic modeling works, we will tweak these values to see how the results change...