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Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (v18)

Learn analytical models to predict a categorical field using IBM SPSS Modeler.

GK# 6013 Vendor# 0E0U7G

$360 - $895 USD

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Course Overview

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In this course, you will learn about analytical models used for predicting a categorical field (churn, fraud, response to a mailing, pass/fail exams, machine break-down, and so forth). You will be introduced to decision trees such as CHAID and C&R Tree, traditional statistical models such as Logistic Regression, and machine learning models such as Neural Networks. You will also learn about important options in dialog boxes, how to interpret the results, and explain the major differences between the models.

Schedule

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What You'll Learn

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  • Introduction to predictive modeling for categorical targets
  • Building decision trees interactively with CHAID
  • Building decision trees interactively with C&R Tree and Quest
  • Building decision trees directly
  • Using traditional statistical models
  • Using machine learning models

Outline

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Viewing outline for:

Virtual Classroom Live Outline

1. Introduction to Data Mining

  • List two applications of data mining
  • Explain the stages of the CRISP-DM process model
  • Describe successful data-mining projects and the reasons why projects fail
  • Describe the skills needed for data mining

2. Working with IBM SPSS Modeler

  • Describe the MODELER user-interface
  • Work with nodes
  • Run a stream or a part of a stream
  • Open and save a stream
  • Use the online Help

3. Creating a Data-Mining Project

  • Explain the basic framework of a data-mining project
  • Build a model
  • Deploy a model

4. Collecting Initial Data

  • Explain the concepts "data structure", "unit of analysis", "field storage" and "field measurement level"
  • Import Microsoft Excel files
  • Import IBM SPSS Statistics files
  • Import text files
  • Import from databases
  • Export data to various formats

5. Understanding the Data

  • Audit the data
  • Explain how to check for invalid values
  • Take action for invalid values
  • Explain how to define blanks

6. Setting the Unit of Analysis

  • Set the unit of analysis by removing duplicate records
  • Set the unit of analysis by aggregating records
  • Set the unit of analysis by expanding a categorical field into a series of flag fields

7. Integrating Data

  • Integrate data by appending records from multiple datasets
  • Integrate data by merging fields from multiple datasets
  • Sample records

8. Deriving and Reclassifying Fields

  • Use the Control Language for Expression Manipulation (CLEM)
  • Derive new fields
  • Reclassify field values

9. Identifying Relationships

  • Examine the relationship between two categorical fields
  • Examine the relationship between a categorical field and a continuous field
  • Examine the relationship between two continuous fields

10. Introduction to Modeling

  • List three modeling objectives
  • Use a classification model
  • Use a segmentation model

Prerequisites

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Experience using IBM SPSS Modeler, including familiarity with the IBM SPSS Modeler environment, creating streams, importing data (Var. File node), basic data preparation (Type node, Derive node, Select node), reporting (Table node, Data Audit node), and creation of models.

Who Should Attend

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  • Data modelers
  • Data analysts
  • Data scientists

Follow-On Courses

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Training Exclusives

This course comes with the following benefits: 

  • Digital Courseware
  • 90 Days Bonus Access to IBM Hands-on Labs
  • 12 Months of Indexed Virtual Class Recordings
Learn More
Course Delivery

This course is available in the following formats:

Virtual Classroom Live

Experience expert-led online training from the convenience of your home, office or anywhere with an internet connection.

Duration: 1 day

Classroom Live

Receive face-to-face instruction at one of our training center locations.

Duration: 1 day

Self-Paced

On-demand content enables you to train on your own schedule.



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