Abstract
Reverse Engineering The Hacker News Ranking Algorithm is an open-source Data Science project. An analysis of historical Hacker News data to determine the ranking algorithm. This repository is a companion to the article Reverse Engineering the Hacker News Ranking Algorithm. The article explores the process of figuring out how the Hacker News Ranking Algorithm works using historical data. It is built using Jupyter Notebook. The complete source code is publicly available on GitHub under the GNU General Public License v3.0, making it a useful reference for students building a Data Science mini project or final-year project.
1. Introduction
This repository is a companion to the article Reverse Engineering the Hacker News Ranking Algorithm. The article explores the process of figuring out how the Hacker News Ranking Algorithm works using historical data. You probably want to read that either before or in tandem with the analysis code included in this repository in order to develop an understanding of the notation, derivations, and general context.
2. Objective
An analysis of historical Hacker News data to determine the ranking algorithm
This project demonstrates how Jupyter Notebook 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 with Jupyter Notebook / JupyterLab (or Google Colab)
- pip for dependencies
- Git (to clone the repository)
6. Installation & Setup
git clone https://github.com/sangaline/reverse-engineering-the-hacker-news-ranking-algorithm.git
cd reverse-engineering-the-hacker-news-ranking-algorithmFull 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.
- What is the source of the dataset and how was missing data handled?
- Which exploratory analysis steps revealed the most useful insight?
- Why were these particular charts chosen to present the data?
- Which statistical or ML technique supports the conclusions?
- How could the analysis be automated or refreshed with new data?
9. Source Code & License
This project is developed by sangaline and published on GitHub under the GNU General Public License v3.0. Please follow the license terms and credit the original author when you use or modify this code.
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