Skip to content

README.md

Latest commit

 

History

History
102 lines (65 loc) · 2.15 KB

README.md

File metadata and controls

102 lines (65 loc) · 2.15 KB

🧹 SQL Data Cleaning Project

MySQL Data Cleaning Status GitHub Repo Size

📌 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.

🎯 Objectives

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

🛠️ Tools & Technologies

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

📂 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.

⚙️ 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

📊 Key SQL Concepts Used

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

📈 Outcome

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

👩‍💻 Author

Khushbir Kaur Bamrah Artificial Intelligence & Data Science Student