Python Idle
Machines can't read words — they need numbers. TF-IDF (Term Frequency-Inverse Document Frequency) is one of the most powerful ways to convert text into meaningful numerical features. Words that appear frequently in one document but rarely across all documents get higher scores. Your mission: transform the spam dataset into a TF-IDF matrix and discover which words are the strongest signals for spam.
~15 minscenario500 rows
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Goals: 3 tests
should create a TF-IDF matrix
should have more than 100 features
should identify top features as a list
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