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The goal is to standardize the data, handle missing values, remove duplicates, and perform exploratory queries to uncover insights into global layoff trends.  \n\n## Dataset Source  \nThe dataset was imported into **MySQL Workbench** for cleaning and analysis.  \n- [Download Dataset](https://www.kaggle.com/datasets/swaptr/layoffs-2022)  \n\n## Steps Performed  \n1. **Data Cleaning** – removed duplicates, standardized values, formatted dates, and handled missing entries.  \n2. **Data Transformation** – converted datatypes, trimmed extra spaces, and normalized text fields.  \n3. **Exploratory Data Analysis (EDA)** – identified top companies by layoffs, yearly trends, and rolling totals.  \n\n## Tools Used  \n- MySQL Workbench  \n- SQL (DDL \u0026 DML queries, Window functions, CTEs, Ranking, Aggregations)  \n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnarpat78%2Flayoffs-data-cleaning-and-eda-using-sql","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnarpat78%2Flayoffs-data-cleaning-and-eda-using-sql","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnarpat78%2Flayoffs-data-cleaning-and-eda-using-sql/lists"}