Implementation of Principal Component Analysis from scratch

Author(s): Navoneel Chakrabarty

Originally published on Towards AI.

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Real-time data may have a vast number of attributes, which often makes essential Exploratory Data Analytics very difficult. Such data are known as highly Multi-Dimensional Data in which each and every attribute is referred to as a dimension. Moving ahead with Multi-Dimensional Data often results in:

Lack of Proper Data Visualization: As data with more than 2 dimensions cannot be plotted on a 2-Dimensional Space, Decision Boundary Visualization is not possible. In such cases, the model’s decision making/pattern recognition logic can’t be correctly interpreted.The trouble with Data Analytics: Data Analytics becomes unnecessarily troublesome with such High-Dimensional Data.Flawed Machine Learning Model Development:… Read the full blog for free on Medium.

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Published via Towards AI