sms-classification

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.

AI-powered SMS spam detection platform built with Scikit-Learn, NLP, and Streamlit.

classify the sms in different categories.

📧 ML project focused on email spam classification, demonstrating data preprocessing, model training, and evaluation using Python and scikit-learn.

Machine learning-based SMS spam classifier using NLP and text classification.

An interactive Spam Detector web app built with Streamlit and NLTK. Classifies SMS messages as "Spam" or "Ham" in real-time using an NLP pipeline and a pre-trained model.

Traditional NLP spam classification workflow using SMS text preprocessing, Bag of Words, TF-IDF, Naive Bayes, Logistic Regression, SVM, and ROC-AUC analysis.

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

SMS Spam Detection using Machine Learning | CountVectorizer | Naive Bayes | 98.55% Accuracy

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

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.

A machine learning project that classifies emails and SMS messages as spam or not spam (ham) using Natural Language Processing (NLP) techniques and a Multinomial Naive Bayes classifier

In this repository, I uploaded all the projects/tasks in Data science Internship at Bharat Intern.

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

Browser based SMS spam detection application using a trained scikit-learn machine learning model and Pyodide.

Internship portfolio of Christopher Pineda, BS Computer Science (AUF) student. Features A.S.E.A., an mBERT SMS scam detector for code-switched Tagalog–English, plus UI/UX, Power BI, networking, and database work. Frosted-glass, responsive, single-file site.

End-to-end SMS spam classifier built with Python, sklearn, and Flask – features a web interface for predictions.

An NLP and Machine Learning-based Spam Detection model that classifies SMS messages as Spam or Ham using text preprocessing and feature extraction.