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.

This repo contains code for EMAIL/SMS SPAM classification.

A SMS classifier to predict whether a message is spam or ham.

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.

A Streamlit-based SMS spam detection app powered by deep learning. Real-time predictions, interactive visualizations, and Docker/Streamlit Cloud deployments included.

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.

Contains my custom implementation of various machine learning models and analysis.

"Spam SMS Classifier using TF-IDF and Naive Bayes. Detects spam messages with high accuracy.

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