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Outlier Removal in the dataset using Z-score method

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Outlier Removal

A python package for implementation of outlier removal in the dataset using Z-score method.

The Z-score is the signed number of standard deviations by which the value of an observation or data point is above the mean value of what is being observed or measured. The intuition behind Z-score is to describe any data point by finding their relationship with the Standard Deviation and Mean of the group of data points. Z-score is finding the distribution of data where mean is 0 and standard deviation is 1 i.e. normal distribution. While calculating the Z-score we re-scale and center the data and look for data points which are too far from zero. These data points which are way too far from zero will be treated as the outliers. In most of the cases a threshold of 3 or -3 is used i.e if the Z-score value is greater than or less than 3 or -3 respectively, that data point will be identified as outliers.

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