<![CDATA[This research focuses on optimizing the design parameters of Gradient Boosted Trees (GBT) to predict customer interest in subscribing to term loans. The study highlights the importance of tuning parameters such as the number of trees, tree depth, and learning rate to enhance the predictive accuracy of GBT. Through this optimization, the model aims to provide more precise insights into customer behavior, aiding financial institutions in making informed decisions and improving operational efficiency. The research compares GBT with other algorithms like Decision Trees and Random Forests, utilizing metrics such as accuracy, precision, recall, and AUC. The results indicate that GBT, with optimal parameter settings, outperforms the other models in predicting customer interest. The study concludes that GBT is an effective tool for market segmentation and can significantly contribute to more accurate predictions in financial services, ultimately helping companies develop better-targeted marketing strategies.]]>
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