Email spam detection identifies and filters out unwanted emails. It analyzes features like sender info, subject lines, and content to differentiate spam from legitimate messages. Techniques include rule-based filters, Bayesian filtering, and machine learning.

The "Advanced Spam and Phishing Detection Model" project focuses on creating a machine learning solution to identify and mitigate spam and phishing threats. Utilizing natural language processing techniques, the model is trained to detect suspicious content and …

SMS Spam Detection is a machine learning model that takes an SMS as input and predicts whether the message is a spam or not spam message. The model is built using Python and deployed on the web using Streamlit.

machine learning classifiers and concluded that convolutional neural network outperforms the classical machine learning methods by a small margin but take more time for classification [8].

... main idea of this detection system is to process the collected SMSs and apply a machine learning method to classify them and identify those that are considered to be spam or phishing...

The project uses an interactive machine learning pipeline built using Python modules like Streamlit and Google APIs to solve the ongoing problem of spam in communication technologies, such as email and SMS.

A precise and reliable spam detection technique is required for mobile Short Message Service (SMS) communication to successfully combat this issue. In our study, we suggest utilising machine...

This repository contains the code for building a spam detection system for SMS messages using deep learning techniques in TensorFlow2. Three different architectures, namely Dense Network, LSTM, and Bi-LSTM, have been used to build the spam detection model.

... objective of this model is to classify short messages through two main phases: the conversion of textual messages into dense numerical representations and the use of an ensemble model that...