As you will have noticed in the previous section, the distributions of our features are very dispersed. Handling the outliers in your model is a very important part of your analysis. It is also very crucial when you look at descriptive statistics. You can be easily confused and misinterpret the distribution because of these extreme values. SciPy has very extensive statistical functions for calculating your descriptive statistics in regards to trimming your data. The main idea of using the trimmed statistics is to remove the outliers (tails) in order to reduce their effect on statistical calculations. Let's see how we can use these functions and how they will affect our feature distribution:
In [58]: np.set_printoptions(suppress= True, linewidth= 125)
samples = dataset.data
CRIM = samples[:,0:1]
minimum = np.round(np.amin(CRIM), decimals...
