EEE407 Machine Learning in Business Intelligence

The course focuses on how Machine Learning (ML) can help enhance business strategy and decision-making.

Objectives of the Course:

  • Understand the fundamentals of Business Intelligence and its components.
  • Explore the role of Machine Learning in BI.
  • Analyze case studies of successful BI implementations with ML.
  • Apply ML techniques to solve real-world BI problems.
Grading Policy
  • Project: 30%
  • Midterm Exam: 30%
  • Final Exam: 40%

Week 1: Introduction to Business Intelligence and Machine Learning

  • Overview of Business Intelligence
  • Role of Machine Learning in BI
  • Case Studies: Successful BI implementations with ML

Week 2-4: Data Preprocessing and Exploration for BI

  • Data cleaning and preparation
  • Exploratory data analysis (EDA) with Python
  • Visualization Techniques for Business Data using Google Data Studio

Week 5-6: Supervised Learning for Business Analytics

  • Regression analysis for sales forecasting and financial modeling
  • Classification techniques for customer segmentation and churn prediction

Week 7-8: Unsupervised Learning in BI

  • Clustering for market segmentation
  • Association rules for market basket analysis

Week 9-10: Advanced Machine Learning Techniques

  • Introduction to neural networks and deep learning in BI
  • Reinforcement learning for dynamic pricing strategies
  • Natural Language Processing (NLP) for customer feedback analysis

Week 11: Integrating ML Models into Business Processes

  • Deployment strategies for ML models
  • Ethics and privacy considerations in BI

Week 12-14: Project Presentations

  • Group project presentations