In the previous chapter, we did a form of data preprocessing by filtering out stopwords. Some machine learning algorithms have trouble with data that is not distributed as a Gaussian with a mean of 0 and variance of 1. The sklearn.preprocessing module takes care of this issue. We will be demonstrating it in this section. We will preprocess the meteorological data from the Dutch KNMI institute (original data for De Bilt weather station from http://www.knmi.nl/climatology/daily_data/datafiles3/260/etmgeg_260.zip). The data is just one column of the original datafile and contains daily rainfall values. It is stored in the .npy format discussed in Chapter 5, Retrieving, Processing, and Storing Data. We can load the data into a NumPy array. The values are integers that we have to multiply by 0.1 to get the daily precipitation amounts in mm.

The data has the somewhat quirky feature that values below 0.05 mm are quoted as -1. We will set those values equal to 0.025 (0.05 divided by...