Noise
In data science, noise refers to the random variation or errors in a dataset that can obscure or distort the underlying signal or pattern. Noise can arise from a variety of sources, including measurement errors, data entry mistakes, or natural variability in the data. It can also be introduced during data collection, transmission, or processing. Noise can have a significant impact on the accuracy and reliability of data analysis, as it can lead to incorrect conclusions or predictions. Therefore, it is important to identify and remove or reduce noise in a dataset before performing any analysis or modeling. This can be done using various techniques, such as filtering, smoothing, or outlier detection.
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