Threat Detection Of Spam And URL Using Machine Learning And NLP

Authors: Gaurav Kadam, Soham Patel, Piyush Bande

Abstract: Spam Messages are very irritating and an upscaled issue in online communication devices and system , it leads to security issue and fraudness in todays world. This paper presents a simple and normal solution that uses Natural language processing techniques to overcome the security issues and help to maintain security. The system developed in this paper represents the combination of NLP and ML that uses advanced text preprocessing, TF-IDF feature extraction, and classification using models or classifiers such as naive bias Logistic Regression, Support Vector Machine (SVM), Random Forest, and Long Short-Term Memory (LSTM) networks. The system predefined outcomes showcases that two model get higher accuracy then others which are Logistic Regression and LSTM and very rigid for outliers. Our Spam detection also forms a user Interface with the help of Streamlit which is further explained in the given paper.