Clean-and-analyze-social-media-usage-data-with-Python
🥈SilverThe project utilizes Python to process social media data, particularly tweets, enhancing marketing strategies. Using NumPy and Random libraries, it systematically creates and manipulates simulated data, replicating diverse social media interactions across content categories and post types.
400Updated 2d ago
Intermediate30min to implementmarketing
Saves ~60 min per use
Quick InstallView Source
git clone https://github.com/itsme-Ash/Clean-and-analyze-social-media-usage-data-with-Python.gitWorks with:
Claude
Overview
About This Skill
The project utilizes Python to process social media data, particularly tweets, enhancing marketing strategies. Using NumPy and Random libraries, it systematically creates and manipulates simulated data, replicating diverse social media interactions across content categories and post types.
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/itsme-Ash/Clean-and-analyze-social-media-usage-data-with-PythonRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
I need to clean and analyze social media usage data for [COMPANY], a [INDUSTRY] company. I have [DATA] from Twitter. Please provide a Python script using NumPy and Random libraries to simulate and analyze this data, focusing on content categories and post types. Include steps for data cleaning, simulation, and analysis.
Example Output
# Social Media Data Analysis for [COMPANY] ## Data Cleaning - Removed duplicate entries: 1,245 - Filtered out non-English tweets: 872 - Handled missing values in engagement metrics ## Simulated Data Overview - Total simulated tweets: 50,000 - Content categories: - Product announcements: 12,500 (25%) - Industry news: 15,000 (30%) - Customer engagement: 10,000 (20%) - Promotional content: 7,500 (15%) - Other: 5,000 (10%) ## Key Insights - Highest engagement: Product announcements (avg. 42 retweets) - Lowest engagement: Industry news (avg. 18 retweets) - Optimal posting times: 12-2 PM and 6-8 PM EST ## Recommendations - Increase product announcement frequency by 15% - Experiment with interactive content formats - Schedule more posts during identified peak times
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