Machine Learning Assignment Help-Top 10 Machine Learning Algorithm | How Become a Data Scientist?

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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