https://github.com/vishnu-vamshii/layoffs-data-analysis-in-sql

This project focuses on the cleaning and exploratory analysis of a dataset containing layoff information. It includes data deduplication, standardization of columns, handling null and blank values, and analyzing layoffs by company, industry, country, and date. Various SQL queries are used to explore trends and patterns in layoffs over time.
https://github.com/vishnu-vamshii/layoffs-data-analysis-in-sql

data-analysis eda mysql

Last synced: 11 days ago
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This project focuses on the cleaning and exploratory analysis of a dataset containing layoff information. It includes data deduplication, standardization of columns, handling null and blank values, and analyzing layoffs by company, industry, country, and date. Various SQL queries are used to explore trends and patterns in layoffs over time.

• Host: GitHub
• URL: https://github.com/vishnu-vamshii/layoffs-data-analysis-in-sql
• Owner: vishnu-vamshii
• Created: 2024-09-09T02:02:23.000Z (5 months ago)
• Default Branch: main
• Last Pushed: 2024-09-21T02:10:42.000Z (5 months ago)
• Last Synced: 2024-11-30T22:20:02.529Z (2 months ago)
• Topics: data-analysis, eda, mysql
• Homepage:
• Size: 55.7 KB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Layoffs Data Cleaning and Exploratory Analysis

## Overview

This project involves cleaning and exploring a dataset of company layoffs. The data cleaning steps include removing duplicates, standardizing columns, handling blank or null values, and removing irrelevant columns. The analysis explores key trends such as layoffs by industry, country, and date. SQL is used to carry out these transformations and exploratory data analysis (EDA).

## Table of Contents

- [Data Cleaning](#data-cleaning)

  - [Removing Duplicates](#removing-duplicates)

  - [Standardizing the Data](#standardizing-the-data)

  - [Handling Null or Blank Values](#handling-null-or-blank-values)

  - [Removing Irrelevant Columns](#removing-irrelevant-columns)

- [Exploratory Data Analysis (EDA)](#exploratory-data-analysis-eda)

  - [Layoffs by Company](#layoffs-by-company)

  - [Layoffs by Industry](#layoffs-by-industry)

  - [Layoffs by Country](#layoffs-by-country)

  - [Layoffs by Date](#layoffs-by-date)

- [Conclusion](#conclusion)

## Data Cleaning

### Removing Duplicates

Duplicates were identified and removed based on key fields like company, location, industry, and total layoffs using SQL `ROW_NUMBER()`.

### Standardizing the Data

Company names and industry values were standardized by trimming whitespace and formatting inconsistencies. The date column was converted into a standard format for further analysis.

### Handling Null or Blank Values

Null and blank values were addressed for key columns such as `total_laid_off`, `percentage_laid_off`, and `industry`. In cases where relevant, missing values were filled by joining related rows.

### Removing Irrelevant Columns

Non-essential columns, such as the row number, were dropped to streamline the dataset for analysis.

## Exploratory Data Analysis (EDA)

### Layoffs by Company

The analysis includes identifying the companies with the highest total layoffs.

### Layoffs by Industry

An analysis was performed to find which industries were most affected by layoffs.

### Layoffs by Country

The number of layoffs per country was aggregated to understand the geographical distribution.

### Layoffs by Date

Layoffs over time were explored, with the data grouped by year and month to identify trends.

## Conclusion

This project successfully cleans and explores a dataset related to layoffs. Through SQL queries, we derive insights into which companies, industries, and countries are most affected by layoffs, as well as the timeframes in which layoffs peak.

## Usage

To run the SQL commands, a compatible database such as MySQL or PostgreSQL is required.

## License

This project is licensed under the MIT License.