As someone who is currently juggling a full-time data science job with multiple freelance projects, I am usually the first to try tools that can potentially decrease my turnaround time.
When ChatGPT started rolling out the Code Interpreter plugin to subscribers in the past week, I couldn’t wait to incorporate it into my data science projects.
If you haven’t already heard about the tool, Code Interpreter is a plugin that allows you to upload documents and run Python programs within the ChatGPT interface.
Gone are the days when we’d manually copy and paste data into ChatGPT and wait for a response.
With Code Interpreter, you can simply upload your dataset and get the tool to analyze your data, build machine learning models, and generate visualizations in minutes.
In this article, I will show you how Code Interpreter can be used to execute an end-to-end data science project.
In my previous company, I worked as a marketing data scientist.
This meant that I’d use customer data to increase sales — by identifying our most profitable users, predicting churn rates, and building profiles of people who should be targeted in future marketing campaigns.
I even wrote a tutorial on building a customer segmentation model with Python, in which I used a publicly available dataset to identify how valuable each customer was to an e-commerce company.
In this article, we will be performing customer segmentation on the same dataset. This time, however, we will be using ChatGPT Code Interpreter to help us build the model.
We will be using Kaggle’s E-Commerce Dataset for this analysis. This dataset was obtained from the UCI Machine Learning Repository and comprises information on real retail transactions for a UK-based ecommerce company.
This dataset is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.