https://github.com/huacenxu/predict-loan-status
Using the Cross-Industry Standard Process of Data Mining (CRISP-DM), this project analyzes loan data from Prosper to identify key factors that predict loan status.
https://github.com/huacenxu/predict-loan-status
bootcamp-project data-science data-visualization data-w loan-prediction-analysis
Last synced: 3 months ago
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Using the Cross-Industry Standard Process of Data Mining (CRISP-DM), this project analyzes loan data from Prosper to identify key factors that predict loan status.
- Host: GitHub
- URL: https://github.com/huacenxu/predict-loan-status
- Owner: huacenxu
- Created: 2022-02-19T18:57:07.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2022-06-01T18:47:19.000Z (about 3 years ago)
- Last Synced: 2025-01-19T14:59:12.328Z (5 months ago)
- Topics: bootcamp-project, data-science, data-visualization, data-w, loan-prediction-analysis
- Language: Jupyter Notebook
- Homepage:
- Size: 501 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Predict Loan Status
## Project Motivation
Based on the Cross-Industry Standard Process of Data Mining (CRISP-DM), a loan data from Prosper is used to study key factors that predict loan Status. Specifically, I asked the following three questions:
- How do homeownership and employment status predict Loan amount?
- How do homeownership and employment status predict borrowers’ APR?
- How does Loan Status vary by homeownership status and employment status?
## File Description
- A Descriptive Jupyter Notebook
- A README file
## Installation
- NumPy
- Pandas
- Seaborn
- Matplotlib
No additional installations beyond the Anaconda distribution of Python and Jupyter notebooks.
## Analysis Results
Key results and findings were listed below. Find more on [Medium](https://medium.com/@brinxu1/predict-loan-status-9784e36a5736)
- For those with a home, I found that borrows' APR is the lowest for full-time employed.
- For those without a home, it is one of the highest for those who do not have a home.
- Putting together, it looks like those who are full-time employed and have a home enjoy the highest loan amount as well we the lowest borrower APR.
## Acknowlegements
- Dataset is provided by [Kaggle](https://www.kaggle.com/yousuf28/prosper-loan), an open-source data community.
- The analysis is benefited from the Udacity instructor and mentor team's help and support.