Unsupervised Learning (Clustering-based Customer Segmentation)
PCA (Principal Component Analysis)
Learning Outcome
5
Recognize the mandatory pre-processing step (Scaling) and the loss of interpretability.
4
Determine how many components to keep using a Scree Plot.
3
Visualize how PCA finds the axes of maximum variance.
2
Explain the difference between Feature Selection and Feature Extraction (PCA).
1
Understand the "Curse of Dimensionality" and why too many columns destroy models.
Topic Name-Recall(Slide3)
Hook/Story/Analogy(Slide 4)
Transition from Analogy to Technical Concept(Slide 5)
Core Concepts (Slide 6)
Core Concepts (Slide 7)
Core Concepts (.....Slide N-3)
Summary
5
The trade-off for compressing your data is that you completely lose the interpretability of your original features.
4
You must always Scale your data before applying PCA.
3
PC1 captures the maximum variance (the "perfect camera angle").
2
It uses Feature Extraction, blending old columns into new, synthetic Principal Components.
1
PCA is an unsupervised dimensionality reduction technique.
Quiz
Text
A. Facebook
B. Instagram
C. LinkedIn
D. Snapchat
Quiz-Answer
Which platform is mainly used for professional networking and B2B marketing ?
A. Facebook
B. Instagram
C. LinkedIn
D. Snapchat
