Python Data Mining Quick Start Guide
Packt Publishing Limited (Verlag)
978-1-78980-026-5 (ISBN)
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 DescriptionData 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 forPython 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.
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.
Table of Contents
Data Mining and Getting Started with Python Tools
Basic Terminology and Our End-to-End Example
Collecting, Exploring, and Visualizing Data
Cleaning and Readying Data for Analysis
Grouping and Clustering Data
Prediction with Regression and Classification
Advanced Topics - Building a Data Processing Pipeline and Deploying It
| Erscheinungsdatum | 30.04.2019 |
|---|---|
| Verlagsort | Birmingham |
| Sprache | englisch |
| Maße | 75 x 93 mm |
| Themenwelt | Informatik ► Datenbanken ► Data Warehouse / Data Mining |
| Mathematik / Informatik ► Informatik ► Theorie / Studium | |
| ISBN-10 | 1-78980-026-9 / 1789800269 |
| ISBN-13 | 978-1-78980-026-5 / 9781789800265 |
| Zustand | Neuware |
| Haben Sie eine Frage zum Produkt? |
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