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Machine Learning for Data Mining
Machine Learning for Data Mining

Machine Learning for Data Mining: Improve your data mining capabilities with advanced predictive modeling

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Machine Learning for Data Mining

Getting Started with Machine Learning

In the last chapter, we saw what machine learning predictive models are and formed a basic understanding of how they work. In this chapter, we will demonstrate the working of neural net models and move on to another type of model, the (Support Vector Machines)
SVMs model.

The following are the topics that will be covered in this chapter:

  • Demonstrating a neural network
  • Support Vector Machines
  • Demonstrating SVMs

Demonstrating a neural network

Let's jump to a hands-on example of neural networks. The software that we are using is the SPSS Modeler, provided by IBM. But feel free to use any data-mining software package.

Running a neural network model

In order to run our first neural network, we will have to bring in the data that we will be using, if you are using IBM SPSS Modeler you can follow these steps:

  1. Get the data using the Var. File node, and bring it up to the canvas:
  1. Attach the dataset to the source node:

Click on the triple dot box on the right side of file box and navigate to your data; we are using Electronics_Data here:

Click Open.

  1. Go on to the Types tab to check whether the data was read correctly:

Click...

Support Vector Machines

Support Vector Machines (SVMs) models were built to predict categorical and continuous outcomes and are especially good when you have many predictors. They were developed for difficult predicting situations where linear models were unable to separate the categories of the outcome field. They too work like black boxes, hiding their complex work in predicting results. Let's get an insight into how SVMs work.

Working with Support Vector Machines

Suppose, for example, there is a kind of data that cannot be separated using a single line as shown in this diagram:

Consider these shapes to be different types of data. As you can see, we won't be able to separate a cluster of data by just drawing a...

Demonstrating SVMs

In this section, we will run an SVM model and see how it works.

First of all, get your dataset just the way you did for neural networks, partition the dataset into a training and testing dataset, and create a scenario such as this:

Let's see how to run SVMs:

  1. Go to the Modeling palette and connect the partition node to SVM:
  1. Go to the Expert tab and select the Expert option in Mode. Remember, whenever you run an SVM model, you must always run it in Expert mode because this is a model that requires constant changes on the default values based on the status of your model. The Expert mode will enable us to change the values easily when required:

Let's discuss these options in detail:

    • You can tick the Append all probabilities box when you have categorical outcomes. But, for now, let's keep it on default.
    • The stopping criteria can also be changed...

Summary

In this chapter, we saw how to work with neural network models. Then we moved on to cover SVM models and demonstrated how SVM works. We have seen how to work with different types of kernel transformations.

In the next chapter, we will look at machine learning models in more detail.

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Key benefits

  • Learn how to apply machine learning techniques in the field of data science
  • Understand when to use different data mining techniques, how to set up different analyses, and how to interpret the results
  • A step-by-step approach to improving model development and performance

Description

Machine learning (ML) combined with data mining can give you amazing results in your data mining work by empowering you with several ways to look at data. This book will help you improve your data mining techniques by using smart modeling techniques. This book will teach you how to implement ML algorithms and techniques in your data mining work. It will enable you to pair the best algorithms with the right tools and processes. You will learn how to identify patterns and make predictions with minimal human intervention. You will build different types of ML models, such as the neural network, the Support Vector Machines (SVMs), and the Decision tree. You will see how all of these models works and what kind of data in the dataset they are suited for. You will learn how to combine the results of different models in order to improve accuracy. Topics such as removing noise and handling errors will give you an added edge in model building and optimization. By the end of this book, you will be able to build predictive models and extract information of interest from the dataset

Who is this book for?

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.

What you will learn

  • 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

Product Details

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Publication date : Apr 30, 2019
Length: 252 pages
Edition : 1st
Language : English
ISBN-13 : 9781838821555
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Product Details

Publication date : Apr 30, 2019
Length: 252 pages
Edition : 1st
Language : English
ISBN-13 : 9781838821555
Category :
Languages :

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Table of Contents

6 Chapters
Introducing Machine Learning Predictive Models Chevron down icon Chevron up icon
Getting Started with Machine Learning Chevron down icon Chevron up icon
Understanding Models Chevron down icon Chevron up icon
Improving Individual Models Chevron down icon Chevron up icon
Advanced Ways of Improving Models Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Full star icon 5
(2 Ratings)
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4 star 0%
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1 star 0%
Amazon Customer Nov 02, 2019
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Great book
Amazon Verified review Amazon
Em Dec 10, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
A few years ago I bought the book, IBM SPSS Modeler Essentials, by the same author, and I found it to be extremely useful. This book introduces machine learning and then covers the ins and outs of several models. However it is chapters 4 and 5 that really take analyzing data to a whole other level.
Amazon Verified review Amazon
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