Dimensionality Reduction for Big Data using Machine Learning

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Dimension reduction refers to the process of converting a set of data having huge dimensions into data with low dimensions ensuring that it communicate similar information concisely. These techniques are typically used while solving machine learning problems to obtain better features for a classification or regression task.

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use dimensionality reduction on huge data using machine learning with applying some genetic algorithms specially with (Particle Swarm Optimization and Dragonfly Optimization) on two different environments (MATLAB and Python).