Social Media Performance Analysis

(The Level Supermind Hackathon 2025 Assignment)

This repository contains a Jupyter Notebook that analyzes social media performance data. The goal of this project is to extract meaningful insights and trends from the dataset, helping to understand key patterns and improve social media strategies.

Installation

To run the notebook locally, follow these steps:

1. Clone the repository:

   git clone https://github.com/your-username/social-media-performance-analysis.git
   cd social-media-performance-analysis

2. Set up a Python environment:

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`

3. Install the required dependencies:

   pip install -r requirements.txt

4. Launch Jupyter Notebook:

   jupyter notebook

5. Open Social_media_performance_analysis.ipynb in your browser.

Usage

1. Ensure your dataset is in the correct format and location (details specified in the notebook).
2. Run the notebook cell by cell to:
   • Load and preprocess the data.
   • Analyze trends and generate insights.
   • Visualize key findings.

Example Insights

• Posts by females receive 46% more likes compared to posts by males.
• Engagement rates are higher during weekends compared to weekdays.
• Visual content (images and videos) outperforms text-only posts in terms of shares.

Dependencies

• Python 3.7+
• Jupyter Notebook
• pandas
• matplotlib
• seaborn
• numpy

Refer to requirements.txt for the complete list of dependencies.