Practical techniques to accelerate heavy workloads with GPU optimization in Python
One of the biggest challenges that data scientists face is the lengthy runtime of Python code when handling extremely large datasets or highly complex machine learning/deep learning models. Many methods have proven effective for improving code efficiency, such as dimensionality reduction, model optimization, and feature selection — these are algorithm-based solutions. Another option to address this challenge is to use a different programming language in certain cases. In today’s article, I won’t focus on algorithm-based methods for improving code efficiency. Instead, I’ll discuss practical techniques that are both convenient and easy to master.
To illustrate, I’ll use the Online Retail dataset, a publicly available dataset under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can download the original dataset Online Retail data from the UCI Machine Learning Repository. This dataset contains all the transactional data occurring between a specific period for a UK-based and registered non-store online retail. The target is to train a model to predict whether the customer would make a repurchase and the following python code is used to achieve the objective.
