A Performance Study of the Naive Bayes Classifier in Advertisement Analysis

Modern business strategies would not be complete without advertising campaigns. Moreover, advertising greatly influences an organization's ability to succeed and enhance its visibility. This study explores a data-driven strategy that predicts the effectiveness of ads by utilizing machine learning algorithms. It highlights the importance of using data to inform decisions in the advertising sector and provide opportunities for more investigation and real-world application in businesses, we will require less training data when using a Naive Bayes classifier. The current study provides crucial details on algorithmic design to address several specific problems in advertising CTR prediction. The model will forecast metrics like click-through rates (CTR) or conversion rates by analysing historical data and relevant features. The goal is to perform Naive Bayes prediction in the data set which provides businesses with actionable insights to optimize their ad campaigns and improve marketing ROI.