 |
This presentation delves into the critical task of spam email detection and classification in the digital age, focusing on the adaptation of machine learning algorithms such as Decision Tree, KNN, and XGBoost. We will explore the preprocessing and feature extraction techniques employed, including tokenization, stemming, and stopword removal, and discuss the dataset preparation from Kaggle, split into training and testing sets. The simulation results will highlight the performance of each model, with XGBoost demonstrating the highest accuracy and robustness. We will also compare the precision and recall metrics of the models, emphasizing the strengths and weaknesses of each. Finally, we will outline future research directions, including the potential application of advanced techniques like deep learning and reinforcement learning to further enhance spam detection and address emerging email threats.
|
IEEE/ICACT20250220 Slide.02
[Big Slide]
[YouTube]