This repository contains the code and documentation for our layoff data analysis project using MySQL. In this project, we aim to analyze layoff data to gain insights into various aspects such as maximum and minimum layoffs, total layoffs by company, industry, and country, trends over time, and more.
The initial phase of our project involved cleaning the raw layoff data to ensure accuracy and consistency in our analysis. Here are the steps we took in the cleaning process:
- Removing Duplicates: Eliminated duplicate entries to maintain data integrity and avoid redundancy.
- Standardizing Data: Ensured uniformity in data format and structure for easy analysis.
- Handling Null Values and Blanks: Addressed missing or incomplete data points by either filling in the gaps or removing them as necessary.
- Removing Columns and Rows: Streamlined the dataset by removing irrelevant columns and rows that do not contribute to our analysis objectives.
Once the data was cleaned and preprocessed, we embarked on our analysis journey to derive meaningful insights. Here are some of the key analyses we performed:
- Maximum and Minimum Layoffs: Identified the companies and industries with the highest and lowest number of layoffs.
- Total Layoffs: Determined the total number of layoffs and identified which companies, industries, and countries were most affected.
- Window Functions: Utilized window functions to explore trends such as the minimum and maximum layoffs for each country, the most layoffs by year, month, and more.
- Average Layoffs by Date: Calculated the average number of layoffs per month and year to understand the overall trend.
Happy Analyzing 🚀