The project explores multiple machine learning approaches including traditional ML models (Logistic Regression, SVM, Naive Bayes) and ensemble methods (Random Forest, XGBoost, Voting Classifier).

Abstract: In the design of conventional Bayes network classifiers (e.g. Naive Bayes Classifier, Tree Augment Naive Bayes classifier), the network classifier structures are always fixed. Such network ...

Implement Neural Network in Python from Scratch ! In this video, we will implement MultClass Classification with Softmax by making a Neural Network in Python from Scratch. We will not use any build in ...

Abstract: This study applies Bayesian learning techniques, specifically Variational Inference (VI) and Monte Carlo Dropout (MC Dropout) to Automatic Modulation Classification (AMC). Both methods are ...

ABSTRACT: Since transformer-based language models were introduced in 2017, they have been shown to be extraordinarily effective across a variety of NLP tasks including but not limited to language ...

A Web Based Spam Classifier built with python (flask) and classification is implemented using naive bayes classifier due to its best accuracy.