370 - Principal Component Analysis (PCA): Mastering Dimensionality Reduction & Visualization
Автор: DigitalSreeni
Загружено: 2025-11-05
Просмотров: 1586
Описание:
Drowning in high-dimensional data? Can't visualize beyond 3D? Algorithms running too slow? PCA is your solution!
What You'll Learn:
The Curse of Dimensionality and why it matters
Complete PCA implementation from mathematical foundations:
Data standardization (why scale matters!)
Covariance matrix calculation
Eigendecomposition step-by-step
Principal component transformation
4 methods for choosing optimal number of components
Advanced 3D visualization and biplot analysis
Case Study: Breast Cancer Classification
Using 569 tumor samples with 30 features - but these principles work for ANY high-dimensional data: customer analytics, image processing, genomics, sensor networks, text analysis, and more.
Complete Python Implementation:
Proper ML pipeline with PCA (avoid data leakage!)
2D decision boundary visualization
K-means clustering vs SVM classification
Quality assessment metrics and reconstruction error
Interactive 3D projections and biplots
Hands-On Mini-Project:
Classify cancer types using only 2 principal components! Compare unsupervised clustering vs supervised learning approaches.
Part of the Statistical Analysis in Python Tutorial Series
Code from the video: https://github.com/bnsreenu/python_fo...
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