SIFT Analytics offers a variety of training paths to help you find the appropriate courses for your job's role or need.

Predictive Modeling for Categorical Targets Using IBM SPSS Modeler

1 Day | 9:30am to 4:30pm

600 North Bridge Road, #12-05 Parkview Square, Singapore 188778

PRICE: S$700+

17 June 2020

Course Code: 0A0U7

Predictive Modeling for Categorical Targets Using IBM SPSS Modeler (V18) is an intermediate level course that provides an overview of how to use IBM SPSS Modeler to predict the category to which a customer belongs. Students will be exposed to rule induction models such as CHAID and C and R Tree. They will also be introduced to traditional statistical models and machine learning models. Business use case examples include: predict whether a customer switches to another provider/brand and whether a customer responds to a particular advertising campaign. Although this course focuses on classifying customers (including students, patients, employees, and so forth), the techniques can also be applied to business questions such as predicting breakdown of machine parts.

Target Audience:

This intermediate course is for IBM SPSS Modeler Analysts who have completed the Introduction to IBM SPSS Modeler and Data Mining course who want to become familiar with the modeling techniques used to classify customers in IBM SPSS Modeler. This includes data analysts and analytics business users.


You should have:
-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.
-Taken the course Introduction to IBM SPSS Modeler and Data Mining (V18)

Course Outline:

1. Introduction to Classifying Customers
2. Building Your Tree Interactively with CHAID
3. Building Your Tree Interactively with C&R Tree and Quest
4. Building Your Tree Directly
5. Using Traditional Statistical Modeling
6. Using Machine Learning Models

    Contact Person Information

    Participant's Particulars

    Interested to find out how analytics can help you achieve your goals?
    Contact us today!