Python Data Mining Quick Start Guide

Greeneltch, Nathan

ISBN 10: 1789800269 / ISBN 13: 9781789800265

Published by Packt Publishing, 2019

About this Item

Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book is an introduction to data mining and its practical demonstration of working with real-world data sets. With this book, you will be able to extract useful insights using common Python libraries. You will also learn key stages like data loading, cl.

Seller Inventory # 286335628

Bibliographic Details

• Title: Python Data Mining Quick Start Guide
• Publisher: Packt Publishing
• Publication Date: 2019
• Language: English
• ISBN 10: 1789800269
• ISBN 13: 9781789800265
• Binding: Kartoniert / Broschiert
• Condition: New

About this title

Synopsis

Explore the different data mining techniques using the libraries and packages offered by Python

Key Features

• Grasp the basics of data loading, cleaning, analysis, and visualization
• Use the popular Python libraries such as NumPy, pandas, matplotlib, and scikit-learn for data mining
• Your one-stop guide to build efficient data mining pipelines without going into too much theory

Book Description

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.

What you will learn

• Explore the methods for summarizing datasets and visualizing/plotting data
• Collect and format data for analytical work
• Assign data points into groups and visualize clustering patterns
• Learn how to predict continuous and categorical outputs for data
• Clean, filter noise from, and reduce the dimensions of data
• Serialize a data processing model using scikit-learn's pipeline feature
• Deploy the data processing model using Python's pickle module

Who this book is for

Python developers interested in getting started with data mining will love this book. Budding data scientists and data analysts looking to quickly get to grips with practical data mining with Python will also find this book to be useful. Knowledge of Python programming is all you need to get started.

Table of Contents

1. Data Mining and Getting Started with Python Tools
2. Basic Terminology and Our End-to-End Example
3. Collecting, Exploring, and Visualizing Data
4. Cleaning and Readying Data for Analysis
5. Grouping and Clustering Data
6. Prediction with Regression and Classification
7. Advanced Topics - Building a Data Processing Pipeline and Deploying It

About the Author

Nathan Greeneltch, PhD is a ML engineer at Intel Corp and resident data mining and analytics expert in the AI consulting group. He's worked with Python analytics in both the start-up realm and the large-scale manufacturing sector over the course of the last decade. Nathan regularly mentors new hires and engineers fresh to the field of analytics, with impromptu chalk talks and division-wide knowledge-sharing sessions at Intel. In his past life, he was a physical chemist studying surface enhancement of the vibration signals of small molecules; a topic on which he wrote a doctoral thesis while at Northwestern University in Evanston, IL. Nathan hails from the southeastern United States, with family in equal parts from Arkansas and Florida.