Exploring Hacker News Posts

This project analyzes posts from Hacker News, a popular technology and startup discussion platform.
The goal is to explore what kinds of posts (Ask HN or Show HN) tend to receive more comments and engagement from the community.

📊 Objectives

• Compare average comment counts between "Ask HN" and "Show HN" posts.
• Identify which time of day posts tend to receive the most interaction.
• Draw insights about engagement behavior on Hacker News.

🧰 Tools and Libraries

• Python
• pandas
• datetime
• Jupyter Notebook

🧩 Key Steps

1. Load and explore the Hacker News dataset.
2. Filter posts by type (Ask HN, Show HN).
3. Calculate average comments by type and posting hour.
4. Visualize the most active times for user engagement.

💡 Insights

• “Ask HN” posts receive slightly more comments on average than “Show HN”.
• Posts created between 15:00 and 17:00 (EST) tend to get the highest engagement.
• Timing and question-based titles seem to influence visibility.

🏁 About

This project was inspired by a guided project from DataQuest.io, and all code, analysis, and commentary were written by me.