BUAL 6610/6616-Predictive Modeling II  

Spring 2018
Dr. Pei Xu
Harbert College of Business
Auburn University

Final Project

Unit 1: Course Introduction (Week 1)

1. Syllabus; 2. Introduction to Business Analytics

Useful Links:

 

Unit 2: Accessing and Assaying Prepared Data (Week 2)

1. Slides: PredictiveModelingOverview

2. Lab: Introduction to Python (notebooks)

3. Extra reading: Big Data_New tricks for Econometrics

 

Unit 3: Evaluation of Binary Classifier, ROC (Week 3)

1. Slides: Binary Classifier & ROC <pdf, ipython>

2. Python Lab: Evaluate Model Performance; calculate prediction accuracy, sensitivity, specificity, ROC.

3. Extra Reading: Logistics Regression; ROC

 

Unit 4: Resampling Methods: Cross Validation & Bootstrap  (Week 4)

1. Slides: (1) Bias-Variance Tradeoff <slides>    (2) Cross Validation & Bootstrap <pdf, ppt>

2. Python Lab: Use different Resampling Methods to Split the Smarket dataset; evaluate the model performance.

Holdout Validation; K-fold Validation

3. A quick puzzle -baseball cap.docx

 

Unit 5: Decision Tree  (Week 5)

1. Slides: Introduction to Decision Tree and Random Forest <pdf>

2. Python lab: Decision Tree notebook <Decision Tree.ipynb; pdf> <datafile: Hitters>; Random Forest notebook <ipynb>

3. Extra Reading: O'Reilly 2016 Data Science Salary Survey; Predict Customer Retention

Case study: How a Japanese cucumber farmer is using deep learning

 

Kaggle Competitions

Challenge yourself with real-world machine learning problems: First Kaggle Project.ipynb

 

Unit 6: Support Vector Machine

Slides: 6.Support Vector Machines.pptx

Python notebook: Support Vector Machines.ipynb

 

Unit 7: Text Mining

1. Slides: Text Mining-updated.pptx

2. Case: Facebook & Sales; Facebook & Sales-supplement.pdf; DirectTV

3. Lab: AmazonReview.zip

4. SASTextMiner-Tutorial.pdf

 

Unit 8: Neural Network

Concept, review questions <ppt>

Case study - Classification neural network(FIFA) ; Neural Network 2017

 

Unit 9: Working on Projects

Cases: Target and Twitter cases.docx

Final project presentation

 

Unit 10: Case study

AUFootball Facebook Comments <data, ipython notebook>