SQL DATA CLEANING AND EXPLORATORY DATA ANALYSIS(EDA)

This project focuses on analyzing a dataset that tracks layoffs across various companies. The dataset contains information such as the number of employees laid off, the date of the layoffs, the industry sectors affected, and the locations where these layoffs occurred. The primary objective of this analysis is to understand trends in layoffs, identify which industries and regions are most impacted, and explore potential correlations between economic conditions and layoff patterns. By utilizing SQL for data cleaning and analysis, this project aims to provide valuable insights into workforce reductions and their implications for the broader economy.

The main goals for this project were:

Data cleaning

Exploratory data analysis (EDA)

Reporting insights

- The dataset contained duplicate entries that were effectively removed, ensuring the accuracy of subsequent analyses.

1. Data Standardization:

- Cleaning steps, such as trimming whitespace and handling null values, improved data quality and reliability.

2. Layoff Trends:

- Significant layoffs occurred across various industries and countries.

- Notable high-impact layoffs where entire workforces were laid off were identified.

- Temporal analysis revealed trends over specific years and months, highlighting periods of increased layoff activity.

3. Company Rankings:

- Companies were ranked based on yearly layoffs, providing insights into the most affected firms annually.