Certainly one of the largest 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, reminiscent of dimensionality reduction, model optimization, and have selection — these are algorithm-based solutions. An alternative choice to handle this challenge is to make use of a distinct programming language in certain cases. In today’s article, I won’t give attention to algorithm-based methods for improving code efficiency. As an alternative, I’ll discuss practical techniques which can be each convenient and straightforward to master.
For example, 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 may download the unique dataset Online Retail data from the UCI Machine Learning Repository. This dataset incorporates all of the transactional data occurring between a particular period for a UK-based and registered non-store online retail. The goal is to coach a model to predict whether the shopper would make a repurchase and the next python code is used to attain the target.
