SQL Data Cleaning Project

📌 Project Overview

This project demonstrates the process of cleaning a real-world layoffs dataset using MySQL. The objective was to transform raw and inconsistent data into a clean dataset that is ready for analysis.

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Dataset

The dataset contains information about company layoffs, including:

• Company
• Location
• Industry
• Total Laid Off
• Percentage Laid Off
• Date
• Stage
• Country
• Funds Raised (Millions)

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Objectives

The main objectives of this project were to:

• Preserve the original dataset using a staging table.
• Remove duplicate records.
• Standardize inconsistent data.
• Handle NULL and missing values.
• Convert incorrect data types.
• Prepare the dataset for future analysis.

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Tools Used

• MySQL
• MySQL Workbench

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SQL Concepts Used

• CREATE TABLE
• INSERT INTO
• SELECT
• UPDATE
• DELETE
• ALTER TABLE
• Common Table Expressions (CTEs)
• Window Functions (ROW_NUMBER())
• Self Joins
• Data Cleaning Techniques

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Data Cleaning Process

1. Created a Staging Table

A staging table was created to preserve the original dataset before making any modifications.

2. Removed Duplicate Records

Used the ROW_NUMBER() window function and a Common Table Expression (CTE) to identify duplicate records and remove them.

3. Standardized Data

• Removed extra spaces using TRIM().
• Standardized industry values.
• Standardized country names.

4. Converted Date Format

Converted the date column from TEXT to DATE using STR_TO_DATE().

5. Handled Missing Values

• Replaced blank industries with NULL.
• Filled missing industry values using a self join.
• Removed records where both Total Laid Off and Percentage Laid Off were missing.

6. Final Cleanup

Dropped the temporary row_num column after duplicate removal.

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Skills Demonstrated

• SQL
• MySQL
• Data Cleaning
• Window Functions
• Common Table Expressions (CTEs)
• Self Joins
• Data Preprocessing
• Database Management

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Project Outcome

Successfully transformed a raw layoffs dataset into a clean, consistent, and analysis-ready dataset using SQL.

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Future Improvements

• Perform Exploratory Data Analysis (EDA)
• Build dashboards using Power BI
• Generate business insights from the cleaned dataset

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Author

Mohammad Shakib Babu

LinkedIn: www.linkedin.com/in/mohammad-shakib-babu-671643320

GitHub: https://github.com/babumohammadshakib