Python Data Mining Quick Start Guide, published by Packt
This is the code repository for Python Data Mining Quick Start Guide, published by Packt.
A beginner's guide to extracting valuable insights from your data
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 pipeline and Python is a popular tool to perform these tasks as it offers a wide variety of tools for data mining.
This book covers the following exciting 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
If you feel this book is for you, get your copy today!
All of the code is organized into folders. For example, Chapter02.
The code will look like the following:
sns.pairplot(df,hue='species',diag_kind='hist',
palette='bright',markers=['o','x','v']
Following is what you need for this book: 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.
With the following software and hardware list you can run all code files present in the book (Chapter 1-7).
| Chapter | Software required | OS required |
|---|---|---|
| 1-7 | Pandas 0.23.4 | Windows, Mac OS X, and Linux |
| seaborn 0.9.0 | ||
| scikit-learn 0.20.2 | ||
| sqlite 3.25.3 | ||
| numpy 1.16.2 | ||
| matplotlib 2.2.3 | ||
| pickle 3.7 |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
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.
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