Market Basket Analysis & Linear Discriminant Analysis with R

Explore powerful data analysis techniques with this comprehensive course covering Association Rules (Market Basket Analysis) and Linear Discriminant Analysis (LDA).

Association Rules (Market Basket Analysis - MBA)

Uncover hidden patterns in customer behavior using Market Basket Analysis (MBA). This section delves into:

• Understanding MBA: Learn what Market Basket Analysis is and how it reveals relationships between items frequently purchased together.
• Practical Applications: Discover diverse applications of association rules across various industries.
• Rule Representation: Understand the structure and interpretation of association rules.
• Rule Strength: Analyze the significance of rules using:
  • Support: Measure of how frequently an itemset appears in a dataset.
  • Confidence: Probability of purchasing item B given item A.
  • Lift: Measures the strength of the association between two items.
• Basic Algorithm: Learn the fundamental algorithm for deriving association rules, including a discussion of breadth-first and depth-first approaches.
• Hands-on Demo: See the algorithm in action with a practical demonstration.
• R Implementation: Two practical examples using R to apply Market Basket Analysis.
• Assignment: Reinforce your understanding with a challenging assignment.

Linear Discriminant Analysis (LDA)

This part explores Linear Discriminant Analysis (LDA), a powerful classification technique:

• The Need for Classification Models: Understand the importance of classification in data analysis.
• Purpose of LDA: Learn the core objective of Linear Discriminant Analysis.
• LDA Use Cases: Discover real-world applications of classification models.
• Formal Definition: Grasp the mathematical foundation of Linear Discriminant Analysis.
• Analytics Applicability: Explore the applicability of LDA in various analytical scenarios.
• Two Key Uses of LDA:
  • Variable Selection: Identify the most relevant variables for classification.
  • Classification: Build predictive models for categorizing data.
• Understanding LDA Components: Master the three crucial components of LDA:
  • Euclidean Distance: A fundamental measure of distance used in LDA.
  • Mahalanobis Distance: An enhanced distance measure considering correlations between variables.
  • Linear Discriminant Function: The core function used for classification.
• Posterior Probability & Bayes' Theorem: Understand the role of posterior probability and Bayes' theorem in LDA.
• R Implementation: Practical demonstration of LDA using R, including the jackknife approach.
• In-depth Output Analysis: Deep dive into interpreting LDA output.
• Visualization: Visualize LDA operations and results.
• LDA vs. PCA: Compare and contrast LDA with Principal Component Analysis (PCA).
• Multi-Class LDA: Learn how to apply LDA to datasets with more than two classes, including data visualization and model development.
• Model Validation: Validate your LDA models using train and test datasets.
• Industry Applications: Explore real-world applications of classification algorithms.
• Handling Special Cases: Learn strategies for addressing special considerations in LDA.