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

• 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, an open-source data community.
• The analysis is benefited from the Udacity instructor and mentor team's help and support.