Principal Component Analysis (PCA) Implementation in Machine Learning
source link: https://www.geeksforgeeks.org/videos/principal-component-analysis-pca-implementation-in-machine-learning/
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Principal Component Analysis (PCA) Implementation in Machine Learning
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- 11/08/2022
In this video, we will see how to implement PCA in python.
The primary purpose of a PCA (Principal Component Analysis) is to reduce the number of dimensions in a variety of artificial intelligence applications, such as computer vision and image compression.
In situations when the data has large dimensions, it can also be used to uncover hidden patterns. Principal Component Analysis is used in a variety of disciplines, including finance, data mining, psychology, and others.
Following steps to achieve the objective:
1. Import all the libraries
2. Loading Data
3: Normalize the input features into standard scaler form
4: Generate the covariance matrix to get the sense of how variables are correlated
5: Compute the eigen value and eigen vector of the covariance matrix
Implementing PCA in Python with scikit-learn
https://www.geeksforgeeks.org/implementing-pca-in-python-with-scikit-learn/
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