Small and Medium-sized Enterprises Closure Prediction Project

Project Summary

This project focuses on predicting the closure of small and medium-sized enterprises (SMEs) using Business Trends and Outlook Survey Data. Key aspects include:

• Data Utilization: Leveraged survey data to analyze and predict SME closures.
• Machine Learning Models: Implemented models using R, with packages such as randomForest, catboost, and BART.
• Performance Evaluation: Assessed models with metrics like AUROC, F1 score, and accuracy.
• Key Findings: Highlighted the importance of including non-financial data for accurate closure predictions.

Files Description

docs/

• About the project in Korean.pdf: Comprehensive project documentation in Korean, covering the project overview, data details, ML models used, performance results, and key findings. Includes detailed preprocessing information.
• About the project.pdf: Summary of the project in English.
• Summary statistics.pdf: Contains summary statistics for the variables used in the analysis.
• Numble reflections.pdf: Reflections on the project, written in Korean, detailing insights and lessons learned.

code/

• Numble Project.Rmd: R Markdown file with complete project code, from data preprocessing to model evaluation.
• Numble Project.R: R script with all code for data preprocessing, model training, and evaluation.

Important Note

Due to a contract with the competition organization, the dataset used in this project cannot be uploaded. While the provided code will not include the dataset, it offers a comprehensive understanding of the project’s methodology and analysis.

Contributors

• Nayeon Kwon - Sourcing non-financial data, data preprocessing, supporting building ML models, documentation
• Younghoon Yoo - Automated data preprocessing, building ML models, code optimization

Feel free to explore the repository and check out the PDFs for detailed project information.