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.
Linear and Logistic Regression
- Review Predictive Analytics 1
- Linear regression for descriptive modeling
- Fitting the model Assessing the fit
- 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
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
- Representing text in a table
- Term-document matrix
- Bag of words
- Preprocessing of text
- Text reduction
- Term Frequency – Inverse Document Frequency (TF-IDF)
- Fitting a predictive model
Additional Topics: Looking Ahead
- Multiclass classification
- Network analytics
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In addition to assigned readings, this course also has supplemental video lectures, and an end of course data modeling project.
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”.
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.
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:
- Color Enhancer (for colorblindness)
- HelperBird (for colorblindness, dyslexia, and reading difficulties)
- Mobile Dyslexic
- Color Vision Simulation (native accessibility feature)
- Other native accessibility features instructions