Company layoffs have become a major challenge worldwide, caused by sluggish consumer spending, high interest rates, and a looming recession. The number of layoffs has been increasing as the economy slows down, with recent layoffs mostly affecting the tech industry. Large companies such as Spotify, Meta, and Google have announced significant workforce reductions. In such a scenario, data science can provide valuable insights by predicting future layoffs and identifying which industries and regions are more vulnerable to layoffs. The goal of this project is to analyze the dataset provided for job seekers can take these insights into account when deciding which jobs/careers to pursue.
This dataset will be used to observe the following features for each company: Kaggle Dataset of Company Layoffs
The dataset ranges from the date of March 11, 2020 to Feburary 14, 2023.
import pandas as pd
df = pd.read_csv('layoffs.csv')
df.head()
| company | location | industry | total_laid_off | percentage_laid_off | date | stage | country | funds_raised | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | Immutable | Sydney | Crypto | NaN | 0.11 | 2023-02-22 | Series C | Australia | 279.0 |
| 1 | Locomation | Pittsburgh | Transportation | NaN | 1.00 | 2023-02-22 | Seed | United States | 57.0 |
| 2 | Polygon | Bengaluru | Crypto | 100.0 | 0.20 | 2023-02-21 | Unknown | India | 451.0 |
| 3 | Crunchyroll | Tokyo | Media | 85.0 | NaN | 2023-02-21 | Unknown | Japan | 26.0 |
| 4 | Bolt | Lagos | Transportation | 17.0 | NaN | 2023-02-21 | Series F | Nigeria | NaN |
This dataset can be used to perform regression analysis to predict the percentage of layoffs based on variables such as location, industry, and stage. This can be used to help job seekers make important decisions on their employment based on the likelihood of layoffs in specific sectors or locations.