Layoffs Data Cleaning (SQL)

📊 Overview

This project focuses on cleaning and preparing a real-world layoffs dataset using SQL.

The goal was to transform raw, inconsistent data into a structured and analysis-ready format.

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🎯 Objectives

• Identify and remove duplicate records
• Handle missing and null values
• Standardize inconsistent data entries
• Convert data types for accurate analysis

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🛠️ Tools & Technologies

• Microsoft SQL Server (SSMS)
• SQL (T-SQL)

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📁 Dataset

• Layoffs dataset containing company, industry, location, and workforce reduction data

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🧹 Data Cleaning Process

1. Removing Duplicates

• Identified duplicate rows using SQL queries
• Removed duplicates to ensure data accuracy

2. Handling Missing Values

• Addressed NULL values in key columns
• Applied appropriate transformations where needed

3. Standardizing Data

• Cleaned inconsistent entries (e.g., country names, industry labels)
• Ensured uniform formatting across dataset

4. Data Type Conversion

• Converted columns into appropriate data types (dates, integers, etc.)

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📊 Result

• Cleaned dataset ready for analysis
• Improved data consistency and reliability
• Reduced errors caused by duplicates and missing values

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🧠 What I Learned

• Real-world data cleaning techniques in SQL
• Handling inconsistent and incomplete datasets
• Writing structured and readable SQL queries
• Preparing data for downstream analysis

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