To identify the optimal hyperparameter configuration for the neural network, we conducted a hyperparameter search, a crucial step in fine-tuning neural networks. This process involves adjusting parameters such as learning rate, batch size, and regularization to achieve the best model performance. Through iterative exploration of these parameters, we aim to achieve the optimal generalization, ultimately improving the model's predictive capabilities. In this study, we employed grid search for hyperparameter optimization.