You're reading from AI-Assisted Programming for Web and Machine Learning Improve your development workflow with ChatGPT and GitHub Copilot

Product type Paperback

Published in Aug 2024

Publisher Packt

ISBN-13 9781835086056

Length 602 pages

Edition 1st Edition

Languages

Python

Tools

ChatGPT

Concepts

Artificial Intelligence

Authors (5):

Marina Fernandez

Ajit Jaokar

Anjali Jain

Christoffer Noring

Ayşe Mutlu

+1 more

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Table of Contents (25) Chapters

Preface

1. It’s a New World, One with AI Assistants, and You’re Invited FREE CHAPTER

2. Prompt Strategy

3. Tools of the Trade: Introducing Our AI Assistants

4. Build the Appearance of Our App with HTML and Copilot

5. Style the App with CSS and Copilot

6. Add Behavior with JavaScript

7. Support Multiple Viewports Using Responsive Web Layouts

8. Build a Backend with Web APIs

9. Augment Web Apps with AI Services

10. Maintaining Existing Codebases

11. Data Exploration with ChatGPT

12. Building a Classification Model with ChatGPT

13. Building a Regression Model for Customer Spend with ChatGPT

14. Building an MLP Model for Fashion-MNIST with ChatGPT

15. Building a CNN Model for CIFAR-10 with ChatGPT

16. Unsupervised Learning: Clustering and PCA

17. Machine Learning with Copilot

18. Regression with Copilot Chat

19. Regression with Copilot Suggestions

20. Increasing Efficiency with GitHub Copilot

21. Agents in Software Development

22. Conclusion

23. Other Books You May Enjoy

24. Index

Prompting strategy

To utilize ChatGPT effectively for data exploration of the Amazon product review dataset, we need to establish clear prompting strategies tailored to generate Python code and data insights. Here’s how we can approach this.

Strategy 1: Task-Actions-Guidelines (TAG) prompt strategy

1.1 – Task: The specific goal is to explore the Amazon product review dataset thoroughly using various statistical and visualization techniques.

1.2 – Actions: The key steps in exploring this dataset include:

Data Loading: Load the dataset into a pandas DataFrame.
Data Inspection: Check for missing data, understand data types, and inspect the first few entries.
Statistical Summaries: Calculate summary statistics for numerical data.
Categorical Analysis: Analyze categorical variables using counts and visualizations.
Rating Distribution: Create histograms or bar charts to visualize the distribution of star ratings.