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.
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.
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.
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 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
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.
This project is licensed under the MIT License. See the LICENSE file for more details.
This project had been finished at March 2024, no changes shall be made to this main repository. No edits will be approved.
If you have any questions or need further information, please contact our team at: connectnathaniel@gmail.com
Authors:
- Nathaniel Zhu
- Ankit Malhotra