This project focuses on analyzing and cleaning a dataset that captures information about layoffs across the globe. The primary goal is to ensure data integrity, uncover trends, and extract meaningful insights that can support decision-making and further analytical processes.
- Data Exploration: Understand the structure, quality, and distribution of the dataset.
- Data Cleaning: Address missing values, resolve inconsistencies, and remove duplicates to prepare the data for analysis.
- Data Analysis: Identify trends, patterns, and regional impacts of layoffs over time.
- Preparation for Advanced Analytics: Create a clean, structured dataset for visualization or predictive modeling.
- Assessing the completeness and accuracy of the data.
- Ensuring standardized formats for dates, text, and numeric fields.
- Investigating layoff trends by region, industry, and time periods.
- Summarizing key findings to derive actionable insights.
This analysis provides a foundation for understanding global layoff patterns and facilitates informed decision-making for researchers, policymakers, and stakeholders.