Naive Bayesian, SVM, Random Forest Classifier, and Deeplearing (LSTM) on top of Keras and wod2vec TF-IDF were used respectively in SMS classification

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

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.

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

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

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