Tableau • PostgreSQL • Data Analysis Project
This project presents an end-to-end data analysis of Top Instagram Influencers using Tableau for Visualization and PostgreSQL for SQL-based Analysis.
The goal is to understand influencer reach, engagement, growth patterns, and regional influence using real-world social media metrics.
The dataset ranks influencers based on follower count and includes engagement and performance indicators to support marketing, brand strategy, and influencer selection decisions.
├── README.md
├── Top Instagram Influencers Analysis Project Report.pdf
├── Tableau Analysis/
│ ├── Engagement_Growth_&_Influence_Analysis.png
│ ├── Influencer_Performance_&_Reach_Overview.png
│ └── Top Instagram Influencers Analysis.twb
├── SQL Analysis/
│ ├── advanced_analysis.sql
│ ├── data_import.sql
│ ├── eda_queries.sql
│ └── schema.sql
├── notebook/
│ └── Instagram_data_cleaning.ipynb
└── data/
├── instagram_influencers_cleaned.csv
└── instagram_influencers_raw.csv
The dataset contains top Instagram influencers ranked by followers, with the following attributes:
- rank – Influencer rank based on follower count
- channel_info – Instagram username
- influence_score – Overall influence metric
- posts – Total posts published
- followers – Total followers
- avg_likes – Average likes per post
- engagement_rate_60_days – Engagement rate over last 60 days
- new_post_avg_like – Average likes on recent posts
- total_likes – Total likes across all posts
- country – Country of origin
Data preprocessing was performed using Python (Pandas):
- Converted values like
3.3k,475.8m,57.4binto numeric format - Removed
%symbol from engagement rate - Fixed data types for SQL & Tableau compatibility
- Saved a clean, analysis-ready dataset
📂 Notebook: notebook/Instagram_data_cleaning.ipynb
📌 These dashboards summarize influencer performance, engagement, growth trends, and country-level insights.
Key Insights:
- Overall reach and follower distribution
- Top influencers by influence score
- Country-wise influencer presence
- Influence score distribution
Key Insights:
- Engagement rate vs follower count
- Growth trends in new post likes
- Like-to-follower ratio comparison
- Country-level engagement patterns
📂 Tableau Workbook:
Tableau Analysis/Top Instagram Influencers Analysis.twb
SQL was used for structured data exploration and advanced insights.
- Schema creation (
schema.sql) - CSV import (
data_import.sql)
- Follower distribution analysis
- Top influencers by influence score
- Engagement rate comparison
- High-engagement, low-follower influencers
- Growth potential using new post performance
- Country-wise average influence score
📂 SQL Scripts:
schema.sqldata_import.sqleda_queries.sqladvanced_analysis.sql
- High follower count does not always guarantee high engagement
- Several mid-tier influencers show strong growth potential
- Engagement rates vary significantly across countries
- Emerging influencers can outperform celebrities in engagement efficiency
- Influence score is a stronger metric than followers alone
- Tableau – Data visualization & dashboards
- PostgreSQL – SQL querying & analysis
- Python (Pandas) – Data cleaning & preprocessing
- Jupyter Notebook – Data preparation workflow
- Review raw and cleaned datasets in the
data/folder - Explore SQL analysis using PostgreSQL scripts
- Open Tableau workbook (
.twb) to interact with dashboards - Refer to dashboard images for quick insights
- Influencer marketing strategy
- Brand collaboration decisions
- Social media performance benchmarking
- Data analytics portfolio project
👤 Harsh Belekar
📍 Data Analyst | Python Developer | SQL | Power BI | Excel | Data Visualization
📬 LinkedIn | 🔗GitHub
⭐ If you found this project helpful, feel free to star the repo and connect with me for collaboration!