Awesome

<p align="center"> <img src="image/Email Logo.png" alt="Email Logo.png" width="80px" height="80px"> </p> <h1 align="center"> Spam Detector </h1> <h3 align="center"> COMP 6721 - Artificial Intelligence </h3> <h5 align="center"> Project Assignment 2 - <a href="https://www.concordia.ca/">Concordia University</a> (Winter 2020) </h5> <p align="center"> <img src="gif/spam detector.gif" alt="Animated gif pacman game" height="382px"> </p> <p>I have developed a spam detector program in Python which classifies given emails as spam or ham using the Naive Bayes approach.</p> <h2> :floppy_disk: Project Files Description</h2> <p>This Project includes 3 executable files, 3 text files as well as 2 directories as follows:</p> <h4>Executable Files:</h4> <ul> <li><b>spam_detector.py</b> - Includes all functions required for classification operations.</li> <li><b>train.py</b> - Uses the functions defined in the spam_detector.py file and generates the model.txt file after execution.</li> <li><b>test.py</b> - Uses the functions defined in the spam_detector.py file and, after execution, generates the result.txt as well as evaluation.txt files.</li> </ul> <h4>Output Files:</h4> <ul> <li><b>model.txt</b> - Contains information about the vocabularies of the train set, such as the frequency and conditional probability of each word in Spam and Ham classes.</li> <li><b>result.txt</b> - Contains information about the classified emails of the test set.</li> <li><b>evaluation.txt</b> - Contains evaluation results table as well as Confusion Matrix of Spam and Ham classes.</li> </ul> <h4>Source Directories:</h4> <ul> <li><b>train directory</b> - Includes all emails for the training phase of the program.</li> <li><b>test directory</b> - Includes all emails for the testing phase of the program.</li> </ul>

<h2> :book: Naive Bayes</h2> <p>In machine learning, naive Bayes classifiers are a family of simple "probabilistic classifiers" based on applying Bayes' theorem with strong (naive) independence assumptions between the features. Abstractly, naive Bayes is a conditional probability model: given a problem instance to be classified, represented by a vector <img src="image/1.png" alt="Formula 1" style="max-width:100%;"></p> <p>representing some n features (independent variables), it assigns to this instance probabilities <img src="image/2.png" alt="Formula 2" style="max-width:100%;"></p> <p>The problem with the above formulation is that if the number of features n is large or if a feature can take on a large number of values, then basing such a model on probability tables is infeasible. We therefore reformulate the model to make it more tractable. Using Bayes' theorem, the conditional probability can be decomposed as <img src="image/3.png" alt="Formula 3" style="max-width:100%;"></p>

<h2> :clipboard: Execution Instruction</h2> <p>The order of execution of the program files is as follows:</p> <p><b>1) spam_detector.py</b></p> <p>First, the spam_detector.py file must be executed to define all the functions and variables required for classification operations.</p> <p><b>2) train.py</b></p> <p>Then, the train.py file must be executed, which leads to the production of the model.txt file. At the beginning of this file, the spam_detector has been imported so that the functions defined in it can be used.</p> <p><b>3) test.py</b></p> <p>Finally, the test.py file must be executed to create the result.txt and evaluation.txt files. Just like the train.py file, at the beginning of this file, the spam_detector has been imported so that the functions defined in it can be used.</p>

<h2> :books: References</h2> <ul> <li><p>Jonathan Lee, 'Notes on Naive Bayes Classifiers for Spam Filtering'. [Online].</p> <p>Available: https://courses.cs.washington.edu/courses/cse312/18sp/lectures/naive-bayes/naivebayesnotes.pdf</p> </li> <li><p>Wikipedia.org, 'Naive Bayes Classifier'. [Online].</p> <p>Available: https://en.wikipedia.org/wiki/Naive_Bayes_classifier</p> </li> <li><p>Youtube.com, 'Naive Bayes for Spam Detection'. [Online].</p> <p>Available: https://www.youtube.com/watch?v=8aZNAmWKGfs</p> </li> <li><p>Youtube.com, 'Text Classification Using Naive Bayes'. [Online].</p> <p>Available: https://www.youtube.com/watch?v=EGKeC2S44Rs</p> </li> <li><p>Manisha-sirsat.blogspot.com, 'What is Confusion Matrix and Advanced Classification Metrics?'. [Online].</p> <p>Available: https://manisha-sirsat.blogspot.com/2019/04/confusion-matrix.html</p> </li> <li><p>Pythonforengineers.com, 'Build a Spam Filter'. [Online].</p> <p>Available: https://www.pythonforengineers.com/build-a-spam-filter/</p> </li> </ul>

<h2 id="credits"> :scroll: Credits</h2>

Mohammad Amin Shamshiri