Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification
The project leverages Naive Bayes Classifiers, a family of algorithms based on Bayes’ Theorem, which presumes independence between predictive features. This theorem is crucial for calculating the likelihood of a message being spam based on various characteristics of the data.
classify the sms in different categories.
In this repository, I uploaded all the projects/tasks in Data science Internship at Bharat Intern.
End-to-end SMS spam classifier built with Python, sklearn, and Flask – features a web interface for predictions.
SMS Spam Classifier is a machine learning project that classifies SMS messages as spam or ham using the SMS Spam Collection dataset. It employs text preprocessing (TF-IDF) and machine learning algorithms like Logistic Regression, Naive Bayes, and SVM to predict spam messages effectively.
This repo contains code for EMAIL/SMS SPAM classification.
Contains my custom implementation of various machine learning models and analysis.
A SMS classifier to predict whether a message is spam or ham.
"Spam SMS Classifier using TF-IDF and Naive Bayes. Detects spam messages with high accuracy.
A Streamlit-based SMS spam detection app powered by deep learning. Real-time predictions, interactive visualizations, and Docker/Streamlit Cloud deployments included.
This project is a SMS spam classifier which detect whether the SMS is spam or ham using the multinomial Naive Bayes algorithm along the side of BOW/TF-IDF in NLP
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