This LogisticRegression model is built using basic machine learning techniques to classify spam emails. It is a Binary Classifier that only predicts whether an email is HAM or SPAM. The dataset is taken from an open source public database which stores email data for training and research purposes.

The data is loaded and parsed using python library functions. Custom functions and Transformers are written in the code to transform the loaded data into lowercase plain\text format and all numbers, punctuations or URLs are either removed or replaced with alternate words or same words with their suffixes removed. The transformed data is then used to create vectors that store the counts of all the words present in the emails. An ordered list of most common words present in a spam email is built and each email is checked against this list to see how many of these words are present in the current instance. If the count is high then the email is marked as SPAM and if not then HAM.

• Pandas
• Numpy
• urlextract
• nltk
• matplotlib
• scikit-learn

Branch: Main

• datasets\housing - contains the original data in the easy_ham and spam sub-folders.
• Spam_classifier.ipynb - contains all the code used to transform the data and build the model.
• spam_email.pkl - This is the model itself after being trained; saved using the joblib function.