Comparative Analysis of Ensemble Learning Techniques for Purchase Prediction in Digital Promotion through Social Network Advertising

This study conducts a comprehensive comparative analysis of ensemble learning techniques for predicting user purchases in social network advertising. The ensemble methods evaluated include Random Forest, Gradient Boosting Machines (GBM), AdaBoost, and Bagging. The dataset, consisting of 7,000 records of user interactions with social network advertisements, was preprocessed to handle missing values, encode categorical variables, and standardize numerical features. Performance metrics such as accuracy, precision, recall, F1 score, and ROC AUC score were used to evaluate each model. The Random Forest model achieved an accuracy of 0.875, precision of 0.821, recall of 0.821, F1 score of 0.821, and ROC AUC score of 0.948. The GBM model also performed well, with an accuracy of 0.875, precision of 0.846, recall of 0.786, F1 score of 0.815, and ROC AUC score of 0.948. The AdaBoost model showed the highest performance, with an accuracy of 0.9, precision of 0.917, recall of 0.786, F1 score of 0.846, and ROC AUC score of 0.969. The Bagging model achieved an accuracy of 0.875, precision of 0.821, recall of 0.821, F1 score of 0.821, and ROC AUC score of 0.939. Feature importance analysis revealed that Age and Estimated Salary were the most significant predictors across all models. Hyperparameter tuning was crucial in optimizing each model's performance, ensuring they were neither too simple nor too complex. The study's findings underscore the effectiveness of ensemble learning techniques in social network advertising and provide valuable insights for marketers. Future research could explore larger and more diverse datasets, other ensemble methods, and the computational efficiency of these models. This research contributes to predictive analytics in marketing, enhancing the accuracy and effectiveness of advertising strategies.