Thread by @Sachintukumar

Day 47 of #100dayswithmachinelearning

Topic -- Principle Component Analysis
(PCA) Part 1

PCA statistics is science of analyzing all the dimension & reducing them as much as possible while preserving exact information

You can monitor multi-dimensional data (can visualize in 2D or 3D dimension) over any platform using the Principal Component Method of factor analysis.

Step by step explanation of Principal Component Analysis

STANDARDIZATION
COVARIANCE MATRIX COMPUTATION
FEATURE VECTOR
RECAST THE DATA ALONG THE PRINCIPAL COMPONENTS AXES

Applications of PCA Analysis

- PCA in machine learning is used to visualize multidimensional data.

- In healthcare data to explore the factors that are assumed to be very important in increasing the risk of any chronic disease.

- PCA helps to resize an image.

Disadvantages of PCA

Sometimes, PCA is difficult to interpret. In rare cases, you may feel difficult to identify the most important features even after computing the principal components.

You may face some difficulties in calculating the covariances and covariance matrices.

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