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In this method we will use the Inter Quartile Range(IQR) to detect outliers. IQR tells us the variation in the data set. Any value, which is beyond the range of -1.5 x IQR to 1.5 x IQR are treated as outliers

- Q1 represents the 1st quartile/25th percentile of the data.
- Q2 represents the 2nd quartile/median/50th percentile of the data.
- Q3 represents the 3rd quartile/75th percentile of the data.
- (Q1–1.5
*IQR) represent the smallest value in the data set and (Q3+1.5*IQR) represent the largest value in the data set.

First, we will import

`Numpy`

as`np`

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

Next, we will define the datapoints we had used previously

`x = [5, 5, 5, -99, 5, 5, 5, 5, 5, 5, 88, 5, 5, 5]`

Now, we will define a function

`calculate_iqr`

to detect the outliers using the IQR method`def <<your code goes here>>(data): Q1 = np.percentile(data, 25, interpolation = 'midpoint') Q2 = np.percentile(data, 50, interpolation = 'midpoint') Q3 = np.percentile(data, 75, interpolation = 'midpoint') IQR = Q3 - Q1 low_lim = Q1 - 1.5 * IQR up_lim = Q3 + 1.5 * IQR outlier =[] for num in x: if ((num> up_lim) or (num<low_lim)): outlier.append(num) print("Outliers:",outlier)`

Finally, we will call the function using our datapoints

`<<your code goes here>>(x)`

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