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Even with many successful phishing email detectors, phishing emails still cost businesses and individuals millions of dollars per year. Most of these models seem to ignore features like word count, stopword count, and punctuations; they use features like n-grams and part of speech tagging. Previous phishing email research ignores or removes the stopwords, and features relating to punctuation only count as a minor part of the detector. Even with a strong unconventional focus on features like word counts, stopwords, punctuation, and uniqueness factors, an ensemble learning model based on a linear kernel SVM gave a true positive rate of 83% and a true negative rate of 96%. Moreover, these features are robustly detected even in noisy email data. It is much easier to detect our features than correct part-of-speech tags or named entities in emails.