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)

Summary

This chapter covered the background and thought process that goes into designing a clustering algorithm for data mining work. It then introduced common clustering methods in the field and illustrated a comparison between all of them with toy datasets. After reading this chapter, you should know the difference between algorithms that cluster based on means separation, density, and connectivity. You should also be able to see a plot of incoming data and have some intuition on whether clustering fits your mining project. In addition, you should have a good idea of what method to try first.

The next chapter will cover common prediction and classification strategies, as well as introducing the concepts of loss functions, gradient descent, and cross validation.