<![CDATA[User retention is a critical indicator of success for digital applications, particularly in highly competitive technology-driven markets. This study aims to predict user retention using an Artificial Neural Network (ANN) approach based on user activity data. The dataset utilized is obtained from Kaggle and includes behavioral features such as usage frequency, session duration, and user interactions within the application. The research methodology involves data preprocessing, ANN model development with multiple architectural variations, and evaluation using metrics including Accuracy, Precision, Recall, F1-Score, Confusion Matrix, and ROC-AUC. The results demonstrate that the ANN model achieves an accuracy of approximately 88%, precision of 90%, recall of 97%, and an F1-score of 93%, with an AUC value above 0.90, indicating strong classification performance. However, further analysis reveals signs of overfitting and a relatively high number of false positives due to data imbalance. Additionally, increasing model complexity does not necessarily lead to improved performance. Overall, ANN proves to be an effective approach for predicting user retention, although further optimization is required to achieve a more balanced and robust model for real-world implementation.]]>
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