An iMessages application that detects if a copied text is ham or spam.
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Updated
May 5, 2018 - Swift
An iMessages application that detects if a copied text is ham or spam.
The main goal here is to apply some Supervised Machine Learning classifiers on the Spambase dataset from the UCI Machine Learning Repository and perform Significance analysis afterwards.
Spam Classification System using GNU Octave & MATLAB with using Support vector machine.
Use Naive bayes to predict if the email/message is spam
SpamGuardian: Your shield against spam! 🛡️ Detect spam messages with our ML model. Paste, click, and stay safe. Deployed on Streamlit, powered by Python libraries. Easy-to-use, robust, and ready for local use. 🚀💬 #SpamDetection #AI #MachineLearning
This repo is for the LinkedIn Learning course Recurrent Neural Networks
A SPAM classifier developed in Python, utilizing Machine Learning and Deep Learning algorithms like SVM, KNN, Logistic Regression, Neural Networks, Naive Bayes, and LSTM.
Spam Classifier using Naive Bayes and Decision Tree method
Visualizing spam base stations and text messages in Beijing
An SMS Spam Classifier using Naive Bayes Classifier
Created a classifier that can distinguish spam emails from ham (non-spam) emails
A dataset analysis about spam messages
ML model to classify email in two category in spam or ham.
The notebook covers basic data cleaning, exploration, and visualization techniques to understand the characteristics of spam emails.
LSTM models for text classification on character embeddings.
In this repository, I uploaded all the projects/tasks in Data science Internship at Bharat Intern.
This repository contains machine learning projects in the Python programming language.
Spam detector
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