Data Cleaning Project: Company Layoffs Dataset Overview: This project focuses on cleaning and standardizing a dataset of company layoffs, consisting of 2,362 rows, covering data up until the year 2023. The objective was to transform raw, unstructured data into a clean and reliable format suitable for detailed analysis.
Key Steps:
Removed Duplicates: Identified and eliminated redundant records to ensure accuracy. Standardized Data: Unified inconsistent industry names, cleaned company names, and corrected country names. Handled Nulls and Blanks: Addressed missing values and removed unnecessary rows. Data Type Corrections: Converted date fields from text to date format for precise analysis. Results: The cleaned dataset now offers a comprehensive view of company layoffs over the years, facilitating detailed exploratory data analysis and insightful trend identification. Key patterns related to industry impacts, company-specific layoffs, and funding stages can now be accurately examined.