https://github.com/jen-uis/loan-status-prediction

This repository contains project materials for the Winter STAT 206 class, University of California, Riverside, A. Gary Anderson School of Management.
https://github.com/jen-uis/loan-status-prediction

data data-analysis data-analytics data-cleaning data-visualization descriptive-analytics julia julia-language jupyter-notebook predictive-analytics predictive-modeling team-collaboration

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This repository contains project materials for the Winter STAT 206 class, University of California, Riverside, A. Gary Anderson School of Management.

• Host: GitHub
• URL: https://github.com/jen-uis/loan-status-prediction
• Owner: Jen-uis
• License: mit
• Created: 2024-05-23T02:25:09.000Z (8 months ago)
• Default Branch: main
• Last Pushed: 2024-05-23T02:53:27.000Z (8 months ago)
• Last Synced: 2024-05-23T03:31:01.352Z (8 months ago)
• Topics: data, data-analysis, data-analytics, data-cleaning, data-visualization, descriptive-analytics, julia, julia-language, jupyter-notebook, predictive-analytics, predictive-modeling, team-collaboration
• Language: Jupyter Notebook
• Homepage:
• Size: 3.25 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Loan-Status-Prediction

This repository contains project materials for the Winter STAT 206 class, University of California, Riverside, A. Gary Anderson School of Management. This project is completed in Julia (or .jl). We used Jupyter Notebook to store Julia codes.

## Introduction

For those who are new to this folder, the `Project-Code.ipynb` and `Project-Code.html` files are our main coding files. The data is originally obtained from Kaggle.com, link will be attached below. Feel free to explore more options beyond this analysis report.

### Project Idea

This project aims to develop a predictive model for loan status using a dataset containing various borrower attributes and loan details. By applying machine learning techniques, we strive to accurately predict whether a loan will be fully paid or defaulted. The analysis involves data preprocessing, feature engineering, and model training and evaluation. The insights gained from this project can help financial institutions assess loan applications more effectively, reducing the risk of defaults and improving overall decision-making in the lending process.

## Contents

- **`Project-Code.ipynb`**: A jupyter notebook which contains Julia codes.

- **`Project-Code.html`**: HTML export of the Jupyter Notebook for easy reading.

- **`Final-Report.docs`**: Documents which reports our finding and analysis during the project.

- **`Data Folder`**: Contains the datasets used for analysis. This dataset is uploaded by Bhavik Jikadara. **Disclaimer**: The data is obtained from Kaggle.com [Loan Status Prediction](https://www.kaggle.com/datasets/bhavikjikadara/loan-status-prediction/code) published by City of Los Angeles. All data are used for educational purposes only. Do not republish Jikadara's work without approval. License: Data files are copyrighted by the original authors

## Note for Reader

In the final part of the `Project-Code.ipynb`, I tried to re-run the Jupyter Notebook and encountered problems. However, for analysis purposes, we already imported the results from Jupyter Notebook to the `Final-Report.docs`, check here for detailed analysis.

## License

This project is licensed under the MIT License. See the **LICENSE** file for more details.

## Contributing

This project had been finished at March 2024, no changes shall be made to this main repository. No edits will be approved.

## Contact

If you have any questions or need further information, please contact our team at: [email protected]

Authors:

- Nathaniel Zhu

- Ankit Malhotra