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

View More author details

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

Steps for data exploration

Performing data exploration helps us understand the dataset and its characteristics. It involves examining the data, identifying patterns, and summarizing key insights. Here are the steps we will follow:

Load the dataset: Read the dataset into a pandas DataFrame to work with the data effectively.
Inspect the data: Display the first few rows of the DataFrame to get a glimpse of the data. Check the column names, data types, and any missing values.
Summary statistics: Calculate summary statistics such as the mean, median, minimum, maximum, and quartiles for numeric columns. This helps in understanding the distribution and range of values.
Explore categorical variables: Analyze the unique values and their frequencies for categorical variables like marketplace, product_category, and sentiment. Visualizations such as bar plots can be helpful for this analysis.
Distribution of ratings: Plot a histogram or bar plot to visualize...