The study begins by identifying key deficiencies in traditional spam filtering systems. Classic rule-based methods and machine learning classifiers such as Naïve Bayes, Support Vector Machines (SVM), ...

We use the Multinomial Naïve Bayes model, a probabilistic algorithm ideal for text classification, to fit our training vectors to the values of the target variable. The Multinomial Naïve Bayes ...

We use a Naive Bayes classifier -- a fast, interpretable model that estimates the probability a piece of text belongs to each possible class. A key part of the pipeline is an elegant for loop that ...

Six models—SVM, KNN, CatBoost, Naive Bayes, CNN, and LSTM—were evaluated, with CatBoost excelling in both binary classification (99.85% accuracy) and multiclass classification (99.82%), outperforming ...

In the machine learning (ML) classification phase, we utilized various algorithms such as Support Vector Machine (SVM), Multinomial Naïve Bayes (MNB), Bernoulli's Naïve Bayes (BNB), Decision Tree (DT) ...

Goal is to have each model predict whether an email is spam (1) or not spam (0) From there an analysis will be given Goals for future: Fully implement the SVM customly Make improvements on Naive Bayes ...

Therefore, in this work, we discuss the theory behind machine learning techniques and the tasks they perform such as classification ... machine learning algorithms like Naive Bayes, random forest, ...