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Python Data Mining Quick Start Guide

Python Data Mining Quick Start Guide

By : Greeneltch
5 (10)
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Python Data Mining Quick Start Guide

Python Data Mining Quick Start Guide

5 (10)
By: 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)
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Setting up Python environments for data mining

A computing setup conducive to advanced data mining requires a comfortable development environment and working libraries for data management, analytics, plotting, and deployment. The popular bundled Python distribution from Anaconda is a perfect fit for the job. It is targeted at scientists and engineers, and includes all the required packages to get started. Conda itself is a package manager for maintaining working Python environments and, of course, is included in the bundle. The package manager will allow you to install/remove combinations of libraries into segregated Python environments, all the while reconciling any version dependencies between the distinct libraries.

It includes an integrated development environment called The Scientific Python Development Environment (Spyder) and a ready-to-use implementation of Jupyter Notebook interface. Both of these development environments use the interactive Python console called IPython. IPython gives you a live console for scripting. You can run a single line of code, check results, then run another line of code in same console in an interactive fashion. A few trial-and-error sessions with IPython will demonstrate very clearly why these Python tools are so beloved by practitioners working in a rapid prototyping environment.

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