The thesis highlights utilizing traditional machine learning algorithms (Random Forest, Decision Trees, Gradient Boosting, Logistic Regression, and K-Nearest Neighbours) and deep learning approaches. An ensemble approach is introduced, combining predictions from multiple models to enhance accuracy and reliability. Furthermore, the thesis outlines an assessment plan to evaluate the effectiveness of individual models and the ensemble method. Evaluation metrics such as accuracy, precision, recall, and F1-score are emphasized.
