You have no items in your shopping cart.
ABSTRACT
Machine learning has become a crucial tool in various fields, and selecting the appropriate algorithm for a specific task is a vital decision. This project aims to investigate and compare the performance of selected machine learning models on various datasets. The models under consideration include Logistic Regression, Naive bayes classifier, Random Forest and Support Vector Machine. The evaluation metrics used to assess their performance include accuracy, precision, recall, and F1-score. The results show that Support vector outperformed other models in predicting fake news. This study serves as a guide for practitioners and researchers in choosing the most suitable algorithm for their specific problem domain.
