SMS Spam and Ham Detection using Multinomial Naive Bayes Algorithm.
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Updated
Jan 3, 2021 - Jupyter Notebook
SMS Spam and Ham Detection using Multinomial Naive Bayes Algorithm.
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An interactive SMS Spam Detection application using Streamlit and machine learning. This app allows users to classify messages as spam or ham and view performance metrics for different models.
A machine learning project using different feature analysis and cross validation and NLP.
SMS Spam Collection Data Set
Simple example for Kaggles SMS Spam Collection Dataset with a simple LSTM.
detects youtube comment spam, text spam, email spam, sms spam in one
CS3244 project - worked on SMS spam classifier using KNN
High-performance SMS spam detection using a scalable Naive Bayes algorithm and Hadoop's MapReduce framework to tackle large-scale spam filtering effectively.
Natural Language Processing using Tensorflow, the model is trained on >5000 SMS text messages to identify spam messages with an validation accuracy of over 98%.
SMS Spam Detection using Machine Learning Approach
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Use the Naive Bayes algorithm to create a model that can classify dataset (https://archive.ics.uci.edu/ml/datasets/SMS+Spam+Collection) SMS messages as spam or not spam
Spam classifier using Bag of Words (BOW) model and Support Vector Machine (SVM) applied with GridSearchCV.
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