Layoff Data Cleaning Project

📌 Project Overview

This project focuses on cleaning and preprocessing layoff data to ensure accuracy, consistency, and usability for further analysis. The dataset contains records of company layoffs, including details like company name, location, industry, total layoffs, and funding. The data was cleaned using SQL to remove duplicates, standardize formats, and fix inconsistencies.

🗂 Dataset

The dataset includes the following key columns:

• company – Name of the company
• location – Company location
• industry – Industry sector
• total_laid_off – Number of employees laid off
• percentage_laid_off – Percentage of workforce laid off
• date – Layoff date
• stage – Growth stage of the company
• country – Country of the company
• funds_raised_millions – Funds raised by the company

🔍 Data Cleaning Steps

1️⃣ Removing Duplicates

• Created a staging table layoff_stage for processing.
• Used ROW_NUMBER() to identify duplicate records based on company, location, industry, layoffs, and funding.
• Deleted duplicate records to retain only the first occurrence.

2️⃣ Trimming and Standardizing Text Data

• Removed leading/trailing spaces from company names.
• Standardized industry names (e.g., converting crypto% to crypto).
• Fixed country names (e.g., removing trailing dots from United States.).

3️⃣ Date Format Standardization

• Converted date from text format to a proper DATE type using STR_TO_DATE().
• Altered the column type in layoff_stage3 to store date values correctly.

4️⃣ Handling Missing Values

• Checked for NULL values in industry and total_laid_off.
• Removed records where both total_laid_off and percentage_laid_off were NULL (considered as unusable data).

5️⃣ Schema Optimization

• Dropped unnecessary columns like row_num after removing duplicates.
• Ensured all transformations were applied to layoff_stage3, which serves as the final cleaned dataset.

🛠 Tools Used

• SQL (MySQL)
• MySQL Workbench

📊 Final Output

After performing all cleaning operations, the dataset is now:
✅ Free from duplicates
✅ Standardized in formatting
✅ Ready for further analysis

🚀 How to Use

1. Clone this repository or copy the SQL scripts.
2. Run the SQL queries in MySQL Workbench or any SQL client.
3. Use the cleaned layoff_stage3 table for analysis.

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💡 Future Improvements

🔹 Further validation checks for incorrect entries.
🔹 Automating the cleaning process using stored procedures.
🔹 Exploring data visualization for deeper insights.

📩 Questions? Suggestions? Feel free to reach out! 😊