Analyze and visualize Instagram follower data for OpenAI’s official Instagram account using Python.
This project helps identify users with the most posts, followers and following, along with category-based insights.
This project focuses on analyzing pre-collected Instagram follower data for OpenAI.
The dataset is provided as a text file and is parsed into structured dictionaries for easy analysis.
The main goals are to:
- Structure raw text data into JSON format
- Identify top-performing users (most posts, followers, following)
- Count and analyze unique categories
- Clean and refine missing data
- Python – Core language for data processing
- Jupyter Notebook – Interactive analysis & visualization
- JSON – Storing structured and parsed data
- Data Structures (lists, dictionaries) – For organizing the dataset
- Uses pre-collected Instagram follower data
- No web scraping involved
- Converts raw text-based data into structured dictionaries
- Saves processed output as JSON for reuse
Identifies the user with:
- 🏆 Most Posts
- 🏆 Most Followers
- 🏆 Most Following
- Extracts categories from the dataset
- Counts total unique categories
- Replaces missing/blank categories with "Unknown"
- Contributions, issues and suggestions are welcome!
- Feel free to open a pull request to improve the project.
- Dataset Source: The Instagram follower data used in this project was originally collected by CWH (Code With Harry).
- All credit for the dataset goes to them—this project only focuses on parsing, analyzing and visualizing that data.