📊 Data Wrangling & Analysis – AI-Related Layoffs Project

📌 Project Overview

This project investigates the economic and emotional impact of AI-related job losses by integrating structured corporate layoff data with unstructured social media discussions from Reddit.

The objective was to collect, clean, merge, and prepare these datasets for further analysis — demonstrating data wrangling, cleaning, and integration skills essential for real-world data science and analytics roles.

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🎯 Key Highlights

• ✅ Multi-source data integration: Combined public layoff records with Reddit discussion threads for richer context.
• ✅ Comprehensive cleaning: Removed duplicates, standardized formats, handled missing values, and filtered for AI-related layoffs.
• ✅ Exported clean dataset for future sentiment analysis, trend forecasting, and reporting.
• ✅ Reproducible workflow in Jupyter Notebook with clear documentation and reusable code.

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📂 Datasets

1. Layoffs Dataset (layoffs.csv)

   • Source: Layoffs.fyi
   • Fields: company, date, industry, total_laid_off, percentage_laid_off, country, etc.

2. Reddit Layoffs Dataset (reddit_layoffs_ai_2023_2025.csv)

   • Source: Reddit (r/layoffs, r/cscareerquestions)
   • Fields: post_title, post_text, date_posted, subreddit.

3. Final Combined Dataset (combined_cleaned_dataset.csv)

   • Merged layoffs and Reddit sentiment data for AI-related layoffs.

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🛠 Tools & Technologies

• Python – Data cleaning & wrangling
• Pandas & NumPy – Data manipulation & transformation
• Jupyter Notebook – Workflow documentation & execution
• Regex – Pattern matching for text filtering
• CSV I/O – Data import/export
• HTML Export – Project report sharing

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🧹 Data Wrangling Process

1. Data Import – Loaded raw CSV files into Pandas DataFrames.
2. Exploration & Profiling – Checked dataset shapes, types, and missing values.
3. Cleaning Steps:
   • Removed duplicate records.
   • Standardized date formats.
   • Converted text fields to lowercase for consistency.
   • Filtered posts mentioning AI-related job losses.
4. Merging – Joined datasets on relevant fields to provide both quantitative (layoff numbers) and qualitative (employee sentiment) insights.
5. Export – Saved cleaned, combined dataset for analysis.

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📊 Potential Analyses & Applications

• Sentiment Analysis – Measure emotional tone in Reddit posts related to AI layoffs.
• Trend Analysis – Identify peaks in layoffs correlated with AI adoption events.
• Industry Impact Reports – Provide insight into sectors most affected by AI-driven workforce changes.

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🚀 How to Reproduce

1. Clone the repository:

   git clone https://github.com/YOUR-USERNAME/data-wrangling-project.git
   cd data-wrangling-project

2. Install dependencies:

   pip install pandas numpy

3. Open and run the Jupyter Notebook:

   jupyter notebook Data_Wrangling_Project_Starter.ipynb

4. View the HTML report for a non-technical presentation:

   • Open Data_Wrangling_Project_Starter.html in your browser.

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📈 Project Impact

This project shows the ability to:

• Work with real-world messy data from multiple sources.
• Apply ETL (Extract, Transform, Load) best practices.
• Prepare datasets for advanced analytics, such as machine learning and business intelligence.
• Communicate findings to both technical and non-technical audiences.

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📜 License

This project is for educational purposes. Data sources belong to their respective owners.

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💼 Ideal for: Data Analyst, Data Scientist, Business Intelligence, and Cloud Data roles.