
In the realm of data science, the quest for efficiency often drives professionals to seek out innovative ways to accelerate the runtime of Python code, especially when handling extensive datasets or intricate machine learning models. Rather than solely focusing on algorithm-based optimizations like dimensionality reduction, model fine-tuning, and feature selection, there exist practical, user-friendly methodologies that deliver significant performance improvements.