One of the biggest challenges data scientists face is the long execution time of Python code when dealing with extremely large data sets or highly complex machine learning/deep learning models. Many methods have been proven to be effective in 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 will not focus on algorithm-based methods to improve code efficiency. Instead, I will discuss practical techniques that are convenient and easy to master.
To illustrate, I will use the Online Retail Dataset, a data set publicly available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. You can download the original data set. Online retail data from the UCI machine learning repository. This dataset contains all transaction data occurring between a specific period for a UK-based registered non-store online retailer. The goal is to train a model to predict whether the customer would make a repurchase and the following Python code is used to achieve the goal.
