Machine Learning Algorithm Based Ad Click Prediction and Marketing Competitive Analysis

Abstract

In this paper, by systematically evaluating the performance of multiple machine learning models in the task of advertisement click prediction, it is found that the XGBoost algorithm exhibits the best prediction potential by virtue of its integrated learning advantages. In order to further improve the model performance, the Sparrow Search Algorithm (SSA) is innovatively introduced to intelligently search for the key hyperparameters of XGBoost, and the SSA-XGBoost fusion model is constructed. The experimental results show that the optimized model achieves significant breakthroughs in classification performance: the accuracy rate reaches 0.87, which is 18.1% higher than that of the basic XGBoost; the recall rate is synchronously increased to 0.87, while the precision rate achieves a leapfrog growth to reach the excellent level of 0.887, which is 21.3% higher than that of the unoptimized model (0.731). These performance improvements have special value in the dimension of false alarm rate reduction - when the model accuracy rate is increased by 21.3%, it means that about 50,000 invalid placements can be reduced in a million-volume ad exposure scenario, and this accuracy improvement not only verifies the effectiveness of the sparrow search algorithm in parameter optimization, but also highlights the practical business value brought by the algorithm improvement. This improvement in accuracy not only verifies the effectiveness of the algorithm in terms of parameter optimization, but also highlights the practical commercial value of the algorithm improvement. From the perspective of feature engineering, SSA successfully solves the efficiency bottleneck of traditional grid search in high-dimensional parameter space through the strategy of combining global search and local optimization, so that key hyperparameters such as the tree structure parameters and learning rate of XGBoost reach a more optimal configuration, which effectively mitigates the risk of overfitting while maintaining the model's stronger generalization ability (19.2% improvement in F1-score) (34% reduction in cross-validation variance). The intelligent prediction model constructed in this study is of great practical significance to the field of digital marketing: through high-precision click prediction, advertisers can accurately identify potential user groups, and reduce the cost of ineffective advertisement exposure while improving the conversion efficiency. This data-driven decision support can not only optimize the advertising budget allocation strategy, but also promote the programmatic advertising delivery system to evolve in the direction of intelligence, and provide technical support for enterprises to build core advantages in digital marketing competition.