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Navigating Data Simplification with PCA

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Overview

Grasp the essentials of dimensionality reduction and lay the groundwork for your journey by understanding and implementing Principal Component Analysis (PCA) using R. This launchpad course provides a comprehensive introduction into why, how and when to use PCA for feature extraction and enhancing computational efficiency in high-dimensional data sets.

Syllabus

Unit 1: Principal Component Analysis

Principal Component Analysis on Weight Data
Reducing Dataset Dimensions with PCA
PCA Transformation for Space Data

Unit 2: Understanding PCA Foundations

Visualizing Eigenvectors of Standardized Height Data
Calculate Eigenvectors for Greatest Variance in Data
Calculating Eigenvectors and Eigenvalues in R
Standardizing Cosmic Height Data

Unit 3: Principal Component Analysis in R

PCA Implementation and Visualization Task
Modify PCA to Use Two Principal Components
Principal Component Analysis on Updated Dataset
Performing PCA on a Standardized Dataset

Unit 4: Interpreting PCA Results

Dimensionality Reduction and Classification with PCA and Logistic Regression
Applying PCA and Logistic Regression to Predict Overweight Status
Integrating PCA with Logistic Regression for Classification

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