why We use Dimensionality Reduction Technique?

Human Being are Can’t visualize the High Dimensional data so we want to reduce in to low dimension.In real world data analysis tasks we analyze complex data i.e. multi dimensional data. We plot the data and find various patterns in it or use it to train some machine learning models. One way to think about dimensions is that suppose you have an data point _x _, if we consider this data point as a physical object then dimensions are merely a basis of view, like where is the data located when it is observed from horizontal axis or vertical axis.

As the dimensions of data increases, the difficulty to visualize it and perform computations on it also increases. So, how to reduce the dimensions of a data-

* Remove the redundant dimensions

* Only keep the most important dimensions

what are the Techniques in the Dimensionality Reduction in Machine Learning?

In this article We use the Fundamental Techniques Like PCA and t-SNE .

_Principal Component Analysis(PCA): _In Machine Learning PCA is the Unsupervised Learning Technique .

First try to understand some terms

_Variance : _It is a measure of the variability or it simply measures how spread the data set is. Mathematically, it is the average squared deviation from the mean score. We use the following formula to compute variance var(x).

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