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)

Types of data sources and loading into pandas

This part of the chapter will show you how to load data in the computer memory. This is, of course, essential to all the downstream work and analysis that you plan to do.

Databases

A relational database is one of the most common ways that enterprises can store data. So, loading from and interacting with databases is essential for most fieldwork. The Python library that we will use is sqlite3 and is included in Anaconda's package. Let's begin by connecting to the database, which is stored in a .db file, and included with the book materials. After we connect to the database, we will create a cursor object that we will use to traverse the object during a query. Next, we...