Location:Main Road, Bangalore

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What you’ll learn
Perform advanced linear regression using predictor selection techniques
Perform any type of nonlinear regression analysis
Make predictions using the k nearest neighbor (KNN) technique
Use binary (CART) trees for prediction (both regression and classification trees)
Use non-binary (CHAID) trees for prediction (both regression and classification trees)
Build and train a multilayer perceptron (MLP)
Build and train a radial basis funcion (RBF) neural network
Perform a two-way cluster analysis
Run a survival analysis using the Kaplan-Meier method
Run a survival analysis using the Cox regression
Validate the predictive techniques (KNN, trees, neural networks) using the validation set approach and the cross-validation
Save a predictive analysis model and use it for predictions on future new data

“Getting Started
Introduction 

“Advanced Linear Regression Techniques
Introduction to Stepwise Regression 
Our Practical Example 
Executing the Stepwise Regression Method 
Interpreting the Results of the Stepwise Method 
Executing the Forward Selection Regression 
Interpreting the Results of the Forward Selection Method 
Executing the Backward Selection Regression 
Interpreting the Results of the Backward Selection Method 
Comparing Nested Models Using the Remove Method 
Executing the Regression Analysis with the Remove Method 
Interpreting the Results of the Remove Method 

“Nonlinear Regression Analysis
Types of Nonlinear Functions 
An Important Classification of the Nonlinear Relationships 
Performing a Nonlinear Regression With an Exponential Relationship 
Performing a Nonlinear Regression With a Logistic Relationship 

“K Nearest Neighbor in SPSS
Introduction to K Nearest Neighbor (KNN) 
Selecting the Optimal Number of Neighbors 
Our Practical Example 
Performing the KNN technique 
Interpreting the results of the KNN analysis 
Finding the Optimal Number of Neighbors with Cross-Validation 
Interpreting the Cross-Validation Results 
Using the KNN Model for Future Predictions 

“Introduction to Decision Trees
What Are Decision Trees? 
Binary Trees (CART) 
Non-Binary Trees (CHAID) 
Advantages and Disadvantages of Decision Trees 

“Growing Binary Trees (CART) in SPSS
Growing a Binary Regression Tree (CART) 
Computing the R Squared 
Growing a CART Regression Tree with Cross-Validation 
Interpreting the Cross-Validation Results for a Regression Tree 
Growing a CART Classification Tree in SPSS 
Interpreting the CART Classification Tree 
Growing a CART Classification Tree with Cross-Validation 
Interpreting the Cross-Validation Results for a Classification Tree 
Using Binary Trees for Future Predictions 

“Growing Non-Binary Trees (CHAID) in SPSS
Building a CHAID Regression Tree 
Interpreting a CHAID Regression Tree 
Growing a CHAID Regression Tree with Cross-Validation 
Building a CHAID Classification Tree 
Interpreting a CHAID Classification Tree 
Growing a CHAID Classification Tree with Cross-Validation 
Using Non-Binary Trees for Future Predictions 

“Introduction to Neural Networks
The Architecture of an Artificial Neural Network 
What Happens Inside of a Neuron? 
Activation Functions 
Neural Network Learning Process 

“Training a Multilayer Perceptron (MLP) in SPSS
Building a Multilayer Perceptron 
Interpreting the Multilayer Perceptron 
Interpreting the ROC Curve 
Using the Multilayer Perceptron for Future Predictions 

“Training a Radial Basis Function (RBF) Neural Network in SPSS
Building an RBF Neural Network 
Interpreting the RBF Network 
Using the RBF Network for Future Predictions 

Two-Step Cluster Analysis
What is Two-Step Clustering? 
Executing the Two-Step Cluster Analysis 
Examining the Evaluation Variables 
Using Your Clustering Model for Future Predictions 

Survival Analysis
What Is the Survival Analysis? 
Introduction to the Kaplan-Meier Method 
Introduction to the Cox Regression 
Our Practical Example 
Executing the Kaplan-Meier Procedure 
Executing the Cox Regression 
Interpreting the Cox Regression 

Practical Exercises

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