Spam Email Detection

This project focuses on the practice and integration of Deep Learning techniques to classify emails as spam or ham. It includes exploratory data analysis, model selection and tuning, and the creation of a predictive system using training and validation sets. Emphasis is placed on text preprocessing and feature extraction using pre-trained embeddings. The implemented neural network architecture combines an embedding layer using GloVe vector representations, Conv1D convolutional layers, and bidirectional LSTM layers, along with regularization techniques such as Dropout and L2 to improve generalization.

Subsequently, an analysis of the incorrectly classified emails is performed.