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Using Z-score method, we can find out how many standard deviations any particular value is away from the mean. The formula for Z-score is given as:

If the Z-score of a data point is more than 3 (because it cover 99.7% of area), it indicates that the data value is quite different from the other values and so is considered as an outlier. Now let's use this method to detect outliers using Python.

First, let's import Numpy as

`np`

`import numpy as <<your code goes here>>`

Now let's define an array of datapoints as

`x`

as follows`<<your code goes here>> = [5, 5, 5, -99, 5, 5, 5, 5, 5, 5, 88, 5, 5, 5]`

Define a function

`calculate_zscore`

to find the outlier(s)`def <<your code goes here>>(data): mean = np.mean(data) std = np.std(data) threshold = 2 outliers = [] for i in data: z = (i-mean)/std if abs(z) > threshold: outliers.append(i) print('outlier in dataset is', outliers)`

Finally, let's call the function with our

`x`

set of datapoints to display the outliers`calculate_zscore(<<your code goes here>>)`

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