BUS 6131 - Predictive Analytics

3 lecture hours 0 lab hours 3 credits

• Evaluate, propose, and apply appropriate predictive analytics techniques for a given decision-making scenario
• Illustrate the benefits of business analytics for individuals, stakeholders, and organizations
• Develop and deliver clear and persuasive messages based on the outcomes of analytics projects
• Build decision models using supervised and unsupervised learning algorithms
• Implement predictive models using supervised learning methods of linear, polynomial and logistic regression, support vector machines, decision trees and forests
• Implement predictive models using unsupervised learning methods of clustering, k-means analysis, hierarchical clustering, and DBSCAN

• Python language environments
  • Installation, configuration, and maintenance of Python environments using shared environments (Rosie, Google CoLab, etc.) and single-user environments (Anaconda) with Jupyter Notebooks
• Python language review
  • Expressions, operators (arithmetic, logical and relational), data types (None, integers, floating point numeric and object), compound expressions leading to statements
  • Flow of control in Python (sequential, branching (if/elif/else), iteration (for loops, while loops, ranges, else parts to loops, iteration over collections), functions and lambda expressions). Points of syntax - importance of colons and tab stops
  • Collections in Python (lists, tuples, dictionaries, sets) indexing and slicing, iteration over collections using comprehensions, iterable objects and generators
  • Classes and object-oriented programming in Python (instance variables, methods, operator overloading, inheritance, polymorphism)
  • Numeric Python (NumPy), Scientific Python (SciPy) and Pandas Libraries
• Machine learning basics
  • Linear regression fundamentals, simple and multivariate linear models
  • Closed form equation solution for linear regression on small data sets
  • Problems related to underfitting and overfitting
  • Python Libraries for machine learning and scikit-learn functions
• Multivariate regression models
  • Polynomial regression and curve fitting techniques

  • Model evaluation in regression models

  • Train and test, training accuracy and out-of-sample accuracy, and K-fold cross-validation concept

  • Regression evaluation metrics

  • Logistic regression for binary decision-making
• Support Vector Machines (SVMs)

  • Classification with SVM (Support Vector Machines)

  • Data transformation

  • Using SVM to find the hyperplane

  • Advantages and disadvantages of SVM

  • SVM applications

• Decision trees
  • Decision tree classifiers

  • Atribute selection in tree building

  • Entropy measure

  • Ensemble learning with random forests

• Unsupervised learning
  • Clustering, k-nearest neighbors, k-means clustering

  • Similarity/dissimilarity metrics

  • Hierarchical clustering

  • Agglomerative clustering applications

  • Density Based Spatial Application with Noise (DBSCAN)