Introduction to Machine Learning Models Using IBM SPSS Modeler (V18.2)

Course Overview

This course provides an introduction to supervised models, unsupervised models, and association models. This is an application-oriented course and examples include predicting whether customers cancel their subscription, predicting property values, segment customers based on usage, and market basket analysis.

Target Audience

• Data scientists
• Business analysts
• Clients who want to learn about machine learning models

Course Objectives

Please refer to course overview

Course Content

Introduction to machine learning models • Taxonomy of machine learning models • Identify measurement levels • Taxonomy of supervised models • Build and apply models in IBM SPSS Modeler Supervised models: Decision trees - CHAID • CHAID basics for categorical targets • Include categorical and continuous predictors • CHAID basics for continuous targets • Treatment of missing values Supervised models: Decision trees - C&R Tree • C&R Tree basics for categorical targets • Include categorical and continuous predictors • C&R Tree basics for continuous targets • Treatment of missing values Evaluation measures for supervised models • Evaluation measures for categorical targets • Evaluation measures for continuous targets Supervised models: Statistical models for continuous targets - Linear regression • Linear regression basics • Include categorical predictors • Treatment of missing values Supervised models: Statistical models for categorical targets - Logistic regression • Logistic regression basics • Include categorical predictors • Treatment of missing values Supervised models: Black box models - Neural networks • Neural network basics • Include categorical and continuous predictors • Treatment of missing values Supervised models: Black box models - Ensemble models • Ensemble models basics • Improve accuracy and generalizability by boosting and bagging • Ensemble the best models Unsupervised models: K-Means and Kohonen • K-Means basics • Include categorical inputs in K-Means • Treatment of missing values in K-Means • Kohonen networks basics • Treatment of missing values in Kohonen Unsupervised models: TwoStep and Anomaly detection • TwoStep basics • TwoStep assumptions • Find the best segmentation model automatically • Anomaly detection basics • Treatment of missing values Association models: Apriori • Apriori basics • Evaluation measures • Treatment of missing values Association models: Sequence detection • Sequence detection basics • Treatment of missing values Preparing data for modeling • Examine the quality of the data • Select important predictors • Balance the data

Course Prerequisites

• Knowledge of your business requirements