Published Paper Details:

Spam, often known as unsolicited email, has grown to be a major worry for every email user. Nowadays, it is quite challenging to filter spam emails since they are made, created, or written in such a unique way that anti-spam filters cannot recognize them. In order to predict or categorize emails as spam, this paper compares and reviews the performance metrics of a few categories of supervised machine learning techniques, including SVM (Support Vector Machine), Random Forest, Decision Tree, CNN, (Convolutional Neural Network), KNN(K Nearest Neighbor), MLP(Multi-Layer Perceptron), Adaboost (Adaptive Boosting), and Nave Bayes algorithm. The goal of this study is to analyze the specifics or content of the emails, discover a limited dataset, and create a classification model that can predict or categorize whether spam is present in an email. Transformers' Bidirectional Encoder Representations) has been optimized to perform the duty of separating spam emails from legitimate emails (HAM). To put the text's context into perspective, BERT uses attention layers. Results are contrasted with a baseline DNN (deep neural network) model that consists of two stacked Dense layers and a BiLSTM (bidirectional Long Short Term Memory) layer. Results are also contrasted with a group of traditional classifiers, including k-NN (k-nearest neighbours) and NB (Naive Bayes). The model is tested for robustness and persistence using two open-source data sets, one of which is utilized to train the model