CTR Prediction and Campaign Simulation System using Machine Learning

Abstract: Digital advertising plays an important role in modern marketing. Companies use online advertisements to promote their products and services. One of the most important performance metrics in digital advertising is Click Through Rate (CTR). CTR measures how many users click on an advertisement compared to how many users view it. Accurate CTR prediction helps businesses improve advertisement performance and increase profit.

Manual prediction of advertisement performance is difficult and inaccurate. Machine learning provides an intelligent solution to predict CTR using historical data. Machine learning models analyze advertisement features such as user behavior, advertisement type, device type, and campaign details. Based on these features, the system predicts the probability of user clicks.

This project presents a CTR Prediction and Campaign Simulation System using machine learning. The system uses Logistic Regression to predict CTR. The system also provides campaign simulation to help businesses test advertisement performance before launching campaigns. The system improves decision making, reduces risk, and increases marketing efficiency.

Keywords: CTR Prediction, Machine Learning, Logistic Regression, Digital Marketing, Campaign Simulation