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Explore the role and importance of data normalization You might come across certain matches that have missing data on shot outcomes, or any other metric. Correcting these issues ensures your analysis is based on clean, reliable data.
Data engineers can prepare the data by removing duplicates, dealing with outliers, standardizing data types and precision between data sets, and joining data sets together. Using this cleaneddata, our machine learning engineers can develop models to be trained and used to predict metrics such as sales.
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