Improve engineered features for even better accuracy

[Philipp-Sc]

The current engineered 'hard-coded' features are very basic, while they provide useful information there is room for improvement.

src/build/feature_engineering/mod.rs

Instead of hard-coded conditions, create / augment with Bag Of Words vector that is derived from the training dataset.

E.g then using a frequency encoding of common words that often occur within spam but not in ham and vice versa. Resulting in two vectors that together contain the most important/common words for/against a spam classification.

[Philipp-Sc]

Update:

Instead of adding a whole count vector trained on the vocabulary to the engineered 'hard-coded' features:

• Fit a Naive Bayes model and add the prediction to the engineered features.

Benefits:

• Naive Bayes works well with words / text data
• hard-coded feature vector size remains manageable