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)

Data Mining and Getting Started with Python Tools

In a sense, data mining is a necessary and predictable response to the dawn of the information age. Indeed, every piece of the modern global economy relies more each year on information and an immense in-stream of data. The path from information pool to actionable insights has many steps. Data mining is typically defined as the pattern and/or trend discovery phase in the pipeline.

This book is a quick-start guide for data mining and will include utilitarian descriptions of the most important and widely used methods, including the mainstays among data professionals such as k-means clustering, random forest prediction, and principal component dimensionality reduction. Along the way, I will give you tips I've learned and introduce helpful scripting tools to make your life easier. Not only will I introduce the tools, but I will clearly describe what makes them so helpful and why you should take the time to learn them.

The first half of the book will cover the nuts and bolts of data collection and preparation. The second half will be more conceptual and will introduce the topics of transformation, clustering, and prediction. The conceptual discussions start in the middle of Chapter 4, Cleaning and Readying Data for Analysis, and are written solely as a conversation between myself and the reader. These conversations are ported mostly from the many adhoc training sessions I've done over the years on Intel office marker boards. The last chapter of the book will be on the deployment of these models. This topic is the natural next step for new practitioners and I will provide an introduction and references for when you think you are ready to take the next steps.

The following topics will be covered in this chapter:

  • Descriptive, predictive, and prescriptive analytics
  • What will and will not be covered in this book
  • Setting up Python environments for data mining
  • Installing the Anaconda distribution and Conda package manager
  • Launching the Spyder IDE
  • Launching a Jupyter Notebook
  • Installing a high performance Python distribution
  • Recommended libraries and how to install
Practitioners should be familiar with the previous data selection, preprocessing, and transformation steps as well as the subsequent pattern and trend evaluation. Knowledge of the full process and an understanding of the goals will orient your data mining efforts in space and keep you aligned with the overall goal.