This paper experiments with well known pruning approaches, iterative and one-shot, and presents a new approach to lottery ticket pruning applied to tabular neural networks based on iterative pruning. Our contribution is a standard model for comparison in terms of speed and performance for tabular datasets that often do not get optimized through research. We show leading results in several tabular datasets that can compete with ensemble approaches. We tested on a wide range of datasets with a general improvement over the original (already leading) model in 6 of 8 datasets tested in terms of F1/RMSE. This includes a total reduction of over 85% of nodes with the additional ability to prune over 98% of nodes with minimal affect to accuracy. The new iterative approach we present will first optimize for lottery ticket quality by selecting an optimal architecture size and weights, then apply the iterative pruning strategy. The new iterative approach shows minimal degradation in accuracy compared to the original iterative approach, but it is capable of pruning models much smaller due to optimal weight pre-selection. Training and inference time improved over 50% and 10%, respectively, and up to 90% and 35%, respectively, for large datasets.