This ML model aims to predict the number of layoffs that may occur within a company by analyzing various factors, including location, industry type, date of layoff, funding stage, and total funds raised.
The model was developed using a comprehensive dataset comprising 2,257 tech companies that experienced layoffs from COVID 2019 to 2024. This dataset, sourced from Kaggle, provides valuable insights into layoff trends and factors influencing workforce reductions.
You can access the dataset at https://www.kaggle.com/datasets/swaptr/layoffs-2022
To use this model, follow these steps:
- Clone the repository:
git clone https://github.com/lrosok/Layoff-Predictor.git
- Navigate to the project directory:
cd Layoff-Predictor - Open the Jupyter Notebook in your terminal:
jupyter notebook
- Run the notebook:
- Open the Layoffs_ML.ipynb notebook.
- Follow the instructions within the notebook to load the dataset and execute the model.
Note: Ensure you have Jupyter installed. If not, you can install it via pip: pip install notebook
- Predicts potential layoffs based on historical data.
- Utilizes machine learning algorithms for accurate predictions.
- Provides insights into the factors contributing to layoffs.
This project is licensed under the MIT License.