🧹 SQL Data Cleaning Project

📌 Project Overview

This project focuses on cleaning and transforming raw data using SQL. The dataset contains layoffs-related information, and the goal is to make it accurate, consistent, and ready for analysis.

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🎯 Objectives

• Remove duplicate records
• Handle missing/null values
• Standardize inconsistent data formats
• Convert data types properly
• Prepare dataset for further analysis

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🛠️ Tools & Technologies

• SQL (MySQL)
• Database Management System (DBMS)

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📂 Dataset Description

The dataset includes information such as:

• Company name
• Industry
• Total layoffs
• Date
• Country
• Stage

📌 Note:

• The original raw dataset (uncleaned) is also included in this repository.
• It contains inconsistencies, null values, and duplicates.
• The SQL script demonstrates how this raw data is cleaned step-by-step.

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⚙️ Data Cleaning Steps

1. Removing Duplicates

• Identified duplicate rows using window functions
• Deleted redundant records

2. Standardizing Data

• Fixed inconsistent company names
• Cleaned industry and country fields
• Trimmed unwanted spaces

3. Handling Null Values

• Replaced or removed null values where necessary
• Ensured meaningful data consistency

4. Date Formatting

• Converted date column into proper SQL DATE format using STR_TO_DATE()

5. Data Transformation

• Structured dataset for better querying and analysis

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📊 Key SQL Concepts Used

• ROW_NUMBER()
• PARTITION BY
• CTE (Common Table Expressions)
• UPDATE statements
• CASE WHEN
• STR_TO_DATE()
• Data filtering and transformation

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📈 Outcome

• Clean and structured dataset
• Improved data quality
• Ready for analysis and visualization

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👩‍💻 Author

Khushbir Kaur Bamrah Artificial Intelligence & Data Science Student