Book Image

Python Data Mining Quick Start Guide

By : Nathan Greeneltch
Book Image

Python Data Mining Quick Start Guide

By: Nathan Greeneltch

Overview of this book

Data mining is a necessary and predictable response to the dawn of the information age. It is typically defined as the pattern and/ or trend discovery phase in the data mining pipeline, and Python is a popular tool for performing these tasks as it offers a wide variety of tools for data mining. This book will serve as a quick introduction to the concept of data mining and putting it to practical use with the help of popular Python packages and libraries. You will get a hands-on demonstration of working with different real-world datasets and extracting useful insights from them using popular Python libraries such as NumPy, pandas, scikit-learn, and matplotlib. You will then learn the different stages of data mining such as data loading, cleaning, analysis, and visualization. You will also get a full conceptual description of popular data transformation, clustering, and classification techniques. By the end of this book, you will be able to build an efficient data mining pipeline using Python without any hassle.
Table of Contents (9 chapters)

What will and will not be covered in this book

A quick and dirty description of data mining I hear in the field can be paraphrased as: "Descriptive and predictive analytics with a focus on previously hidden relationships or trends". As such, this book will cover these topics and skip the predictive analytics that focus on automation of obvious prediction, along with the entire field of prescriptive analytics entirely. This text is meant to be a quick start guide, so even the relevant fields of study will only be skimmed over and summarized. Please see the Recommended reading for further explanation section for inquiring minds that want to delve deeper into some of the subjects covered in this book.

Preprocessing and data transformation are typically considered to be outside of the data mining category. One of the goals of this book is to provide full working data mining examples, and basic preprocessing is required to do this right. So, this book will cover those topics, before delving in to the more traditional mining strategies.

Throughout this book, I will throw in tips I've learned along my career journey around how to apply data mining to solve real-world problems. I will denote them in a special tip box like this one.

Recommended readings for further explanation

These books are good for more in-depth discussions and as an introduction to important and relevant topics. I recommend that you start with these if you want to become an expert:

  • Data mining in practice:

Data Mining: Practical Machine Learning Tools and Techniques, 4th Edition by Ian H. Witten (author), Eibe Frank (author), Mark A. Hall (author), Christopher J. Pal

  • Data mining advanced discussion and mathematical foundation:

Data Mining and Analysis: Fundamental Concepts and Algorithms, 1st Edition by Mohammed J. Zaki (author), Wagner Meira Jr (author)

  • Computer science taught with Python:

Python Programming: An Introduction to Computer Science, 3rd Edition by John Zelle (author)

  • Python machine learning and analytics:

Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition Paperback—September 20, 2017 by Sebastian Raschka (author), Vahid Mirjalili (author)

Advanced Machine Learning with Python Paperback—July 28, 2016 by John Hearty