/Email-Spam_Detector_AutoEncoder

This Machine Learning (ML) Python program aims to detect spam emails using an autoencoder-based learning approach. It first imports necessary libraries for data handling, evaluation metrics, preprocessing, and neural network modeling.

Primary LanguageJupyter NotebookMIT LicenseMIT

Email Spam/Non-Spam Binary Classification with Autoencoder-based learning

Authors: Leo Martinez III - LinkedIn

Contact: leo.martinez@students.tamuk.edu

Created: Spring 2024

Two spam/non-spam datasets trained on the same machine learning model

This project consists of the same autoencoder-based model used for binary classification on two different dataset (both written in Python).

The models are split into two different folders

The first model is in folder ML_model1 and ML_model2, and each contains their respective dataset, results, and program files. There are also visual images that have been generated to better visualize results that can be found in the results folder.

Heatmap for Dataset1 Heatmap for Dataset2

Dependencies

  • Scikit Learn
  • Tensorflow
  • Keras

License

This project is licensed under the MIT License.

Note:

  • Program was created in Jupyter Notebook (.ipynb) using Python and was also converted into a traditional (.py) file suited for Spyder Anaconda (Python 3.8).

Here is a brief explanation of the items:

  • src: folder that contains the source code for python and Jupyter Notebook files
  • data: folder that contains the dataset for the respective models
  • results: folder that contains the results for the respective models
  • README.md: contains most basic information about the project
  • LICENSE: Contains license information in regards to the Github repository