Deep learning models, while highly effective, are often perceived as black boxes. To enhance interpretability, we employed techniques such as Grad-CAM (Gradient-weighted Class Activation Mapping) to visualize which regions of input feature vectors were most crucial for model predictions. This allowed us to find key features driving the neural network's decisions.