This is the code repository for Machine Learning for Data Mining, published by Packt.
Improve your data mining capabilities with advanced predictive modeling
Most data mining opportunities involve machine learning and often come with greater financial rewards. This book will help you bring the power of machine learning techniques into your data mining work. By the end of the book, you will be able to create accurate predictive models for data mining.
This book covers the following exciting features:
- Hone your model-building skills and create the most accurate models
- Understand how predictive machine learning models work
- Prepare your data to acquire the best possible results
- Combine models in order to suit the requirements of different types of data
- Analyze single and multiple models and understand their combined results
- Derive worthwhile insights from your data using histograms and graphs
If you feel this book is for you, get your copy today!
Following is what you need for this book: If you are a data scientist, data analyst, and data mining professional and are keen to achieve a 30% higher salary by adding machine learning to your skillset, then this is the ideal book for you. You will learn to apply machine learning techniques to various data mining challenges. No prior knowledge of machine learning is assumed.
With the following software and hardware list you can run all code files present in the book (Chapter 1-5).
| Chapter | Software required | OS required |
|---|---|---|
| All | IBM SPSS Software | Windows, Mac OS X, and Linux (Any) |
We also provide a PDF file that has color images of the screenshots/diagrams used in this book. Click here to download it.
Jesus Salcedo has a PhD in psychometrics from Fordham University. He is an independent statistical consultant and has been using SPSS products for over 20 years. He is a former SPSS Curriculum Team Lead and Senior Education Specialist who has written numerous SPSS training courses and trained thousands of users.
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If you have already purchased a print or Kindle version of this book, you can get a DRM-free PDF version at no cost.
Simply click on the link to claim your free PDF.
https://packt.link/free-ebook/9781838828974