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Dimensionality Reduction in R
Rating: 2.9 out of 5(13 ratings)
31 students

Dimensionality Reduction in R

Developed by Data Scientist/IITian
Created byArpan Gupta
Last updated 2/2019
English
English [Auto],

What you'll learn

  • Understand Principal Component Analysis (PCA)
  • k-means Clustering After PCA
  • How to find groups in large data sets
  • Scaling,normalization ,etc
  • Dimensionality Reduction Techniques

Course content

3 sections • 5 lectures • 1h 2m total length

  • Introduction to PCA(Principal Component Analysis)2:29

    Explore principal component analysis (PCA) to reduce high-dimensional data in R, understand variance, standard deviation, and covariance, and learn how to compute and apply PCA.

  • Intuition of PCA5:16

    Understand the intuition of principal component analysis (PCA) for dimensionality reduction by rotating data axes to maximize variance along the first component.

  • What is Scaling and Variance8:27

    Learn scaling and variance for dimensionality reduction in R, using principal component analysis to transform four predictor variables into uncorrelated principal components that maximize explained variance with standardized data.

Requirements

  • Some understanding and interest in the R programming language

Description

Dimensionality Reduction is a category of unsupervised machine learning techniques which  is used to reduce the number of features or variables of columns  in a dataset.

Lot of variables often enhances the noise signal in the data which is bad for modelling but Dimensionality Reduction techniques can help in this.

One of the Dimensionality Reduction Technique is Principal component Analysis which creates a new feature set which are uncorrelated or orthogonal .The newly created features are called Principal components.First principal component explains the most of the variance in the data and then the next principal component explains the remaining.

Principal Component analysis is helpful for any dataset which has many variables or variables which are anonymous.

Principal component analysis can help in explaining the structure of the dataset or creating the groups in the data or doing the predictive analytics .  

In this course we will discuss the following items:

  • What is Principal component Analysis 
  • When PCA is useful
  • Data Visualization using  PCA in  2  & 3 Dimensions both
  • What is principal component?
  • What is the variance in the data in different dimensions?
  • Properties of Principal Components
    1. Summarize PCA concepts
    2. Understand significance of first few PC's 
  • Conduct PCA using R: 
    1. Scaling/Normalization/Transformation
    2. Correlation Matrix
    3. Scree plot
  •    Conduct k-means Clustering after Principal Component Analysis
  • Visualizing the clusters in Reduced space


Who this course is for:

  • Interest in reducing large data sets
  • Interest in Clustering in large data sets

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