This project investigates the Loan Data from Prosper and shows the relationship within the data. According to the provided documents this data set contains 113,937 loans with 81 variables on each loan, including loan amount, borrower rate (or interest rate), current loan status, borrower income, and many others.
The main question in this investigation is: What factors affect a loan’s outcome status?
The Dataset can be optained from the following link:
This project is part of the Data Analyst Nanodegree Program by Udacity and follows the two explained steps: Exploratory analyses and Explanatory visualization
In the first step the data is analysed (exploration_template.ipynb and exploration_template.html):
- Extract from the given csv
- Cleaned: i.e. deleted rows with mising data, combined elements.
- Explore: Univariate, Bivariate and Multivariate Exploration are used
In the second step the key findings are visualizatied (slide_deck_template.ipynb and slide_deck_template.slides.html)
I focus on pointing out the correlation between Loan Status, Debt To Income Ratio and Income Range.
- 10 Tips for Writing Cleaner & Better Code - https://www.makeuseof.com/tag/10-tips-writing-cleaner-better-code/
- Use functions to avoid code repetition - https://www.cs.utah.edu/~germain/PPS/Topics/functions.html
- Why should we comment code? - https://www.cs.utah.edu/~germain/PPS/Topics/commenting.html