Many businesses assume that feeding large volumes of data into an ML engine is enough to generate accurate predictions. - bigdata dataanalysis
The truth is it can result in a number of problems, for example, algorithmic bias or limited scalability. The success of machine learning depends heavily on data.
And the sad given is: all data sets are flawed. That is why data preparation is crucial for machine learning. It helps rule out inaccuracies and bias inherent in raw data, so that the resulting ML model generates more reliable and accurate predictions.
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