Machine Learning Algorithms
Principal Component Analysis(PCA)
Naïve Bayes Classifier Algorithm
Least Squares and Polynomial Fitting
K Means Clustering Algorithm
Linear Regression
Logistic Regression
Artificial Neural Networks
Decision Trees
Random Forests
Nearest Neighbours
Principal Component Analysis(PCA)
Principal Component Analysis, or PCA for short, is a method for reducing the dimensionality of data.
The PCA method can be described and implemented using the tools of linear algebra.
PCA is an operation applied to a dataset, represented by an n x m matrix A -
Steps to perform Algorithm:
Step1:
Import all numpy library file related to this like:
from numpy import array
from numpy import mean
from numpy import cov
from numpy.linalg import eig
Step2:
Define a matrix like:
A=array([1,2],[3,4],[5,6])
print(A)
Step3:
Calculate the mean of each column
M=mean(A.T,axis=1)
print(M)
Step4:
Center column using given formula
C=A-M
print(C)
Step5:
Calculate covariance matrix of centered matrix
V=cov(C.T)
print(V)
Step6:
Igen decomposition of covariance matrix
value,vectors=eig(V)
print(vectors)
print(values)
Step7:
Then finally we find the Project data
P = vectors.T.dot(C.T)
print(P.T)
Reusable PCA
Using the PCA() class in the scikit-learn library it it can be applied to new data again and again quite easily.
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