https://github.com/misaghmomenib/social-engagement-analysis
In This Repository, Using a Csv File Related to Social Network Interactions I Made a General Analysis That You Can Have at Your Disposal
https://github.com/misaghmomenib/social-engagement-analysis
data-analysis data-visualization git open-source pandas-python python
Last synced: 7 months ago
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In This Repository, Using a Csv File Related to Social Network Interactions I Made a General Analysis That You Can Have at Your Disposal
- Host: GitHub
- URL: https://github.com/misaghmomenib/social-engagement-analysis
- Owner: MisaghMomeniB
- License: gpl-3.0
- Created: 2025-02-19T12:29:58.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-19T18:50:39.000Z (over 1 year ago)
- Last Synced: 2025-02-28T05:50:10.654Z (over 1 year ago)
- Topics: data-analysis, data-visualization, git, open-source, pandas-python, python
- Language: Python
- Homepage:
- Size: 188 KB
- Stars: 7
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 📊 Social Engagement Analysis
A data-driven exploration project analyzing **social media engagement patterns** using Python & SQL. Designed to reveal what types of content, interactions, and metrics truly resonate with audiences.
---
## 📋 Table of Contents
1. [Overview](#overview)
2. [Features](#features)
3. [Data & Approach](#data--approach)
4. [Getting Started](#getting-started)
5. [Usage Examples](#usage-examples)
6. [Tech Stack](#tech-stack)
7. [Insights & Metrics](#insights--metrics)
8. [Contributing](#contributing)
9. [License](#license)
---
## 💡 Overview
This repository focuses on **measuring and interpreting social media engagement**, leveraging aggregated data on reactions, comments, shares, and post types. It combines:
- **Data cleaning & exploration**
- **Clustering & segmentation**
- **Visual dashboards**
- **Custom SQL metrics and aggregations**
Aims to help developers, marketers, and data analysts understand what drives meaningful engagement. :contentReference[oaicite:1]{index=1}
---
## ✅ Features
- 📦 **Data Preprocessing**: Load `'engagement_data.csv'`, clean missing values, encode features
- 🔍 **Exploratory Analysis**: Plot distributions, correlation matrices, and summary stats
- 🧠 **Clustering**: K-means to segment posts based on reaction-comment-share profiles :contentReference[oaicite:2]{index=2}
- 🎯 **SQL-based Metrics**: Calculate key engagement KPIs like engagement rate, share ratio, reaction breakdowns
- 📈 **Visual Reports**: Charts and tables to highlight insights across clusters and content types
---
## 🗂️ Data & Approach
1. **Data Source**: Replace or extend `data/engagement_data.csv` with your platform’s export. Example columns: likes, loves, wows, hahas, comments, shares, status_type (link/photo/status/video)
2. **Cleaning**: Use Pandas to fill NAs, normalize columns, encode `"status_type"` as dummy variables
3. **EDA**: Visualize distributions using Matplotlib/Seaborn, examine feature correlations
4. **Clustering**: Apply K-means, determine optimal `k` using elbow/silhouette methods
5. **SQL Metrics**: Write `.sql` scripts to aggregate by date, type, cluster — e.g., `engagement_rate = total_interactions / followers`
6. **Visualization**: Output static charts via notebook and export as `.png` for reporting
---
## ⚙️ Getting Started
**Clone & setup**:
```bash
git clone https://github.com/MisaghMomeniB/Social-Engagement-Analysis.git
cd Social-Engagement-Analysis
pip install -r requirements.txt
````
**Run notebook**:
```bash
jupyter notebook Social_Engagement_Analysis.ipynb
```
**Use SQL scripts**:
```bash
sqlite3 engagement.db < sql/metrics.sql
```
*(Or adapt to PostgreSQL/MySQL as needed.)*
---
## 🛠️ Usage Examples
### 🔧 Python - K-means clustering
```python
df = pd.read_csv('data/engagement_data.csv')
features = ['likes','comments','shares']
kmeans = KMeans(n_clusters=3); df['cluster'] = kmeans.fit_predict(df[features])
sns.scatterplot(data=df, x='likes', y='comments', hue='cluster')
```
### 🧮 SQL - Engagement Rate
```sql
SELECT post_type,
SUM(likes + comments + shares)::float / SUM(followers) AS eng_rate
FROM posts
GROUP BY post_type;
```
### 📊 Visual Example
* Reaction & share distribution per cluster
* Post type vs. average engagement plot
* Cluster profiles output to `reports/cluster_profiles.csv`
---
## 🔧 Tech Stack
* **Python 3.10+**
* Pandas, NumPy
* Scikit-learn
* Matplotlib, Seaborn
* **Jupyter Notebook** for exploratory analysis
* **SQLite / PostgreSQL** for SQL aggregations
* **Git** & **GitHub** for version control
---
## 📈 Insights & Metrics
* Profiled content by cluster – e.g., “Cluster A has 2x more shares than others”
* Compared engagement rates across post types
* Identified top content features correlated with high engagement
Metrics used include:
* Engagement Rate = `(likes + comments + shares) / followers`
* Reaction Ratios per post
* Cluster-based behavior segmentation
---
## 🤝 Contributing
Contributions welcome!
1. Fork this repo
2. Create a feature branch (`feature/...`)
3. Commit changes with clear messages
4. Open a pull request for review
---
## 📄 License
This project is licensed under the **MIT License** — see the `LICENSE` file for details.