I have a working TensorFlow 2.x script that trains on tabular numerical data, yet the validation accuracy is still below target. I only need a light round of tuning aimed purely at improving accuracy. What I expect: • Review my current code and dataset preprocessing. • Suggest and implement straightforward tweaks such as better feature scaling, adjusted learning rate, batch size, or simple regularization. • Return the updated Python script with clear comments explaining each change. • Provide a short summary (no more than one page) showing the before-and-after accuracy on my existing validation split. No large-scale model redesign, performance profiling, or deep debugging is required; the focus is squarely on accuracy gains I can verify quickly. If you are comfortable squeezing extra performance out of tabular models in TensorFlow, I would love your help.