naive-bayes
Naive Bayes is a quick and dirty way to classify data but is simple to implement. This is a nice way to brute force a result by calculating simple statistics assuming the data is normally distrubuted and an assumption that there is independence in the data, which is almost never true in reality.
Spam vs Ham
I used a dataset from the UCI machine learning repository to perform a classic example of classifying email into spam vs ham which Naive Bayes can perform relatively well on.
Notes
The implementation source code came from this tutorial and credit goes to Dr. Jason Brownlee who wrote it. I adjusted the implementation to fit my needs for the dataset that I wished to clean and classify. The original use case was predicting diabetes.