This project provides a detailed exploratory data analysis (EDA) on Instagram data consisting of user interactions, photos, tags, comments, and follow relationships. The goal is to derive insights about user behavior, popular tags, posting habits, and network dynamics.
Instagram Data Analysis.ipynb: Jupyter notebook with full analysis and visualizations.photos.csv: Metadata of uploaded photos.comments.csv: User comments on photos.follows.csv: Follower-following relationships among users.tags.csv: List of available tags.photo_tags.csv: Mapping between photos and their tags.userslikes
- 📅 Time-based posting trends.
- 🧑🤝🧑 Top users based on posts, likes, and followers.
- 🏷️ Most used tags and their associated engagement.
- 💬 Comment frequency and top commenters.
- 📷 Tag-photo relationships and their impact.
- 🔁 Network exploration of follow relationships.
- Python 3
- Pandas
- Matplotlib
- Seaborn
- NetworkX (optional for graph analysis)
- Jupyter Notebook
- A few users dominate the platform in terms of content and interactions.
- Tags have a strong correlation with engagement metrics.
- Posting activity varies significantly by time and user.
- The follow network displays a power-law distribution typical of social media.