NorthPeak technicians describe each incident in a short note. To predict the incident category from these notes, a model needs numbers, not sentences. The simplest recipe counts words: one column per word, one row per note. TF-IDF then lowers the weight of words such as the that appear in every note. This lesson builds that table on 108 training descriptions from incidents.csv and shows its limits.
The simplest way to turn text into numbers is to count words. Build a vocabulary: one column per distinct token. Then one row per text, with the count of each token. This table is a bag-of-words. The word order is lost, like words shaken in a bag.
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