Data Cleaning Project - Layoffs Dataset

📌 Project Overview

This project focuses on cleaning and preprocessing a dataset containing information on company layoffs. The dataset includes details such as company names, locations, industries, total layoffs, funding raised, and more. The goal of this project is to enhance data quality by removing inconsistencies, handling missing values, and ensuring standardized formatting using MySQL.

📊 Dataset Information

• File Name: layoffs.csv
• Total Entries: 2,361 rows
• Columns: 9
  • company: Name of the company
  • location: Company headquarters
  • industry: Industry category
  • total_laid_off: Number of employees laid off
  • percentage_laid_off: Percentage of workforce laid off
  • date: Layoff announcement date
  • stage: Funding stage of the company
  • country: Country of the company
  • funds_raised_millions: Total funds raised in millions

🔧 Data Cleaning Steps

1️⃣ Removing Duplicates

• Used a Common Table Expression (CTE) to detect and eliminate duplicate records.

2️⃣ Standardizing Data

• Ensured consistency in text formatting (e.g., removing extra spaces, capitalization issues).

3️⃣ Handling Missing Values

• Identified NULL or blank values and addressed them appropriately.
• Applied imputation techniques where necessary.

4️⃣ Dropping Irrelevant Columns

• Removed columns that were unnecessary for analysis.

5️⃣ Creating Staging Tables

• Created two staging tables (layoffs_staging and layoffs_staging2) to preserve original data and perform transformations efficiently.

💻 Technologies Used

• MySQL – Used for executing SQL queries and performing data cleaning operations.
• Jupyter Notebook / Python (Optional) – Could be used for further exploratory data analysis (EDA).

📜 SQL Queries Used

The cleaning process was executed using SQL queries, including:

• CREATE TABLE – To create staging tables for transformation.
• INSERT INTO – To populate the staging tables.
• ROW_NUMBER() OVER(PARTITION BY...) – To detect duplicate records.
• DELETE – To remove unwanted rows.

📌 Why This Project?

Data cleaning is a crucial step in data analysis and machine learning. Unclean data can lead to inaccurate insights and faulty decision-making. By applying systematic data cleaning techniques, we ensure that our dataset is reliable, consistent, and ready for further analysis.

🚀 How to Use This Repository

1. Clone this repository to your local system.

   git clone https://github.com/your-username/data-cleaning-project.git