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Spam or Ham 📧

Spam or Ham is a program that detects whether an email is considered spam or "ham". The dataset for this project was provided from UCI Machine Learning SMS Spam Collection Dataset. This repository contains implementations of two different machine learning models a naive Bayes classifier and a neural network with the purpose of determining which model would be a better fit for the task. The neural network is a simple perceptron model with a binary step activation function. From my implementations, I determined the naive Bayes classifier generalizes to the data set a lot better than the simple neural network because in the sample_results.txt we can observe that when the neural network trains for 100 iterations it is able to achieve 100% accuracy on the training data but doesn't perform as well on the test data in comparison to the naive Bayes model.

Getting Started

These instructions will give you a copy of the neural network up and running on your local machine for development and testing purposes.

Prerequisites

To run this application locally on your computer, you'll need Git and python3 installed on your computer.

Installing

Then run the following command in the command line and go to the desired directory to store this project:

Clone this repository:

git clone https://github.com/JonathanCen/Spam-or-Ham.git

Navigate to the cloned repository:

cd Spam-or-Ham

To run the naive Bayes text classifier:

python3 Naive_Bayes_Text_Classifier.py

To run the neural network text classifier:

python3 Perceptron_Text_Classifier.py

Quick notes about the results:

  • Test_without_stopwords: running the test documents without the stop words through the perceptron that was trained with stop words
  • Test_without_stopwords_on_new_perceptron: running the test documents without the stop words through a newly trained perceptron on documents without stop words
  • Iterations/Learning Rates I choose:
    • Iterations = [5, 10, 15, 20, 100]
    • Learning Rates = [0.01, 0.05, 0.1, 0.5]

Additional Notes:

Training:

  • I initialized the weights to small random numbers ranging from 0.0 to 1.0.
  • Bias: I hard set the bias with a weight of 5 (did not update this value as I trained).
  • Features: The features are the bag of words of that one document I am currently training on (not the entire training set).
  • When I train my perception, I oscillate between training Ham & Spam documents (training: ham, spam, ham, spam, ...), and when I run out of spam docs, I randomly select spam documents within the training to continue training the model. I did this because there are unequal amount of documents, and I found that the weights of my perceptron were heavily dominated by the ham documents, so when I tested my model, it would correctly predict ham documents 100% of the time but for spam documents, it will have an accuracy of 0%, so I found this as a great solution to solve the problem of the inadequate spam documents.
  • Bias: I hard set the bias feature to 1 with a weight of 5.
  • The activation function I used:
    • if feature_vector * weight_vector > 0, then document is spam (1) else document is ham (-1)

Testing:

  • Bias: Set to the same value as the training
  • Features: Same as the training (bag of words of the current document I am viewing)

Contributing

All issues and feature requests are welcome. Feel free to check the issues page if you want to contribute.

Authors

License

Copyright © 2022 Jonathan Cen.
This project is MIT licensed.