Instagram Influencer Data Analysis & Visualization

In this project, I performed an exploratory data analysis on Instagram influencer data to understand how follower counts, post frequency, content categories, and countries affect overall engagement rates.

🎯 Project Goals

• Clean and preprocess raw influencer data
• Explore follower tiers (Nano, Micro, Macro, Mega) and engagement patterns
• Visualize category-level and country-level trends
• Export the cleaned dataset and generate insightful charts

🚀 Quickstart

1. Install dependencies:

   pip install -r requirements.txt

2. Open and run the analysis notebook:

   jupyter notebook influencer_analysis_final .ipynb

📁 Repository Structure

instagram-influencer-analysis/
 ├── LICENSE                        # MIT License
 ├── README.md                      # This file
 ├── requirements.txt               # Dependencies
 ├── influencer_analysis.ipynb      # Initial analysis notebook
 ├── influencer_analysis_final .ipynb # Final analysis and visualization notebook
 └── influencers_raw.sample.csv     # Sample raw influencer dataset

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📄 License

This project is open-source under the MIT License. See LICENSE for details.