HN SO Analysis

Abstract

1. Introduction

Stack Overflow and Hacker News are portals mainly (but not only) read and used by programmers and other people who occupy their (professional or free) time with writing code.

Is there a relationship between popularity of a given technology on Stack Overflow (SO) and Hacker News (HN)? And a few words about causality dgwozdz 3rd JUNE 2018

Stack Overflow lets their users easily Stack Overflow (SO), an established in 2008 portal on which programmers help each other by asking and answering coding questions, lets their users easily find questions related to a certain programming language/framework/library etc. by tags. The questions and replies/comments are evaluated in a form of points so it is usually instantly obvious which answer was rated the highest (and therefore is considered as the best one by the community) or whether a described problem is reproducible, i.e. you can replicate it with a piece of code prepared by a person asking a question.

2. Objective

Is there a relationship between popularity of a given technology on Stack Overflow (SO) and Hacker News (HN)? And a few words about causality

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/dgwozdz/HN_SO_analysis.git
cd HN_SO_analysis

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by dgwozdz and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.