Predictive Analytics 2 with R – Neural Nets and Regression
As a continuation of Predictive Analytics 1, this course introduces to the basic concepts in predictive analytics, with a focus on R, to visualize and explore predictive modeling.
Overview
In this course you will continue work from Predictive Analytics 1, and be introduced to additional techniques in predictive analytics, also called predictive modeling, the most prevalent form of data mining. The course includes hands-on work with XLMiner, a data-mining add-in for Excel.
Note: We also offer Predictive Analytics 2 using R and Python.
- Introductory, Intermediate
- 4 Weeks
- Expert Instructor
- Tuiton-Back Guarantee
- 100% Online
- TA Support
Learning Outcomes
Upon completing this course students will be able to distinguish between profiling and prediction tasks for linear and logistic regression. They will be able to specify and interpret linear and logistics regression models, use various analytical tools for prediction and classification, and preprocess text for text mining.
- Distinguishing between profiling (explanation) tasks and prediction tasks for linear and logistic regression
- Specifying and interpreting linear regression models to predict continuous outcomes
- Specifying and interpreting logistic regression models for classification
- Using discriminant analysis for classification
- Using neural nets for prediction and classification
- Preprocessing text for text mining, and using a predictive model with the resulting matrix
Who Should Take This Course
Marketing and IT managers, financial analysts and risk managers, accountants, data analysts, data scientists, forecasters. This course is especially useful if you want to understand what predictive modeling might do for your organization, undertake pilots with minimum setup costs, manage predictive modeling projects, or work with consultants or technical experts involved with ongoing predictive modeling deployments.
Our Instructors
Course Syllabus
Week 1
Linear and Logistic Regression
- Review Predictive Analytics 1
- Linear regression for descriptive modeling
- Fitting the model Assessing the fit
- Inference
- Linear regression for predictive modeling
- Choosing predictor variables
- Generating predictions
- Assessing predictive performance
- Logistic regression for descriptive modeling
- Odds and logit
- Fitting the model
- Interpreting output
- Logistic regression for classification
- Choosing predictor variables
- Generating classifications and probabilities
- Assessing classification performance
Week 2
Discriminant Analysis and Neural Nets
- Discriminant analysis for classification
- Statistical (Mahalanobis) distance
- Linear classification functions
- Generating classifications
- Rare cases and asymmetric costsIntegrating class ratios and misclassification costs
- Integrating class ratios and misclassification costs
- Neural network structure
- Input layer
- Hidden layer
- Output layer
- Back propagation and iterative learning
Week 3
Text Mining
- Representing text in a table
- Term-document matrix
- Bag of words
- Preprocessing of text
- Tokenization
- Text reduction
- Term Frequency – Inverse Document Frequency (TF-IDF)
- Tokenization
- Fitting a predictive model
Week 4
Additional Topics: Looking Ahead
- Multiclass classification
- Network analytics
- Project
Prerequisites
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Predictive Analytics 2 with R – Neural Nets and Regression
Additional Information
Homework
In addition to assigned readings, this course also has supplemental video lectures, and an end of course data modeling project.
Course Text
The recommended text for this course is Data Mining for Business Analytics: Concepts, Techniques, and Applications in R, by Shmueli, Patel, Yahav, Bruce and Lichtendahl. This same text is also used in the follow on courses: “Predictive Analytics 1 – Machine Learning Tools – with R” and “Predictive Analytics 3 – Dimension Reduction, Clustering and Association Rules – with R”.
Software
This is a hands-on course, and participants will apply data mining algorithms to real data. The course will use R, a free software environment for statistical computing and graphics. It compiles and runs on a wide variety of UNIX platforms, Windows and MacOS.
Supplemental Information
Literacy, Accessibility, and Dyslexia
At Statistics.com, we aim to provide a learning environment suitable for everyone. To help you get the most out of your learning experience, we have researched and tested several assistance tools. For students with dyslexia, colorblindness, or reading difficulties, we recommend the following web browser add-ons and extensions:
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- Other native accessibility features instructions
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Register For This Course
Predictive Analytics 2 with R – Neural Nets and Regression