• Logistic Regression
• Decision Trees
• Naive Bayes

• Python 3.6.10
• Numpy 1.18.4
• Scikit-learn 0.23.1
• Scipy 1.4.1

1. Run the preprocessing file to extract the data from email, get the clean text after removing stopwords, punctuations and upload to ES after splitting into training and testing.

   python preprocessing.py
   --dirpath
   --labels
   --index
   --seed 4

2. Get the unigrams from the Elastic Search.

   python getUnigrams.py
   --index

3. Build matrix and run the models.

   python spamClassifier.py
   --index
   --labels
   --features
   --cutoff
   --result
   --model <model types are : reg, logit(default), tree, nb(naive bayes)>
   --sparse (use only when u want to create sparse matrix)

Top 10 words after running Logistic Regression on unigrams sparse matrix:

('freebsd', 1.1172201336086656).
('click', 1.1067116575308757).
('antivir', 1.079915881679438).
('penis', 1.0705047080248018).
('opt', 1.0648006849173453).
('girl', 1.0265354745168107).
('adf', 1.0224262305744003).
('website', 0.9591053071640367).
('products', 0.8749953047612927).
('remove', 0.8748610351597089).