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Dimensionality Reduction Techniques in Data Science

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  Introduction Machine learning involves a lot of computations and resources, not to mention the manual effort that goes along with it, to analyze data using a list of variables. The dimensionality reduction approaches are instrumental in this situation.  A high-dimensional dataset may be converted into a lower-dimensional dataset using the dimensionality reduction approach without sacrificing any of the critical characteristics of the original data. These dimensionality reduction methods essentially fall under data pre-processing, which is done before model training. What is Dimensionality Reduction Technique in Data Science? Consider developing a model that can forecast the weather for the following day using the current climatic circumstances. Millions of such environmental characteristics are too difficult to examine, including sunshine, humidity, cold, temperature, and many more that might influence the current conditions . Therefore, by identifying the features with a hi...