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)

Cleaning input data

Real data is dirty and its integrity must be ensured before useful insights can be harvested. Missing or corrupt values can contribute to spurious conclusions or completely uncovered insights. In addition to data integrity, feature scaling, and variable types (that is, continuous or discrete) contribute heavily to the effectiveness of downstream methods. I will explain the reasons for these contributions in the dedicated sections for each topic.

Missing values

Missing values can ruin a data mining job. Sometimes, an entire record or row is empty, and at other times a single cell or value inside a record is missing. The latter situation is much harder to spot and, indeed, these missing cells can be quiet...