https://github.com/pythonbyte/default-loans-analysis
This repository displays a CRISP-DM approach to understand about how Loans enter on default status.
https://github.com/pythonbyte/default-loans-analysis
Last synced: 12 months ago
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This repository displays a CRISP-DM approach to understand about how Loans enter on default status.
- Host: GitHub
- URL: https://github.com/pythonbyte/default-loans-analysis
- Owner: pythonbyte
- License: apache-2.0
- Created: 2021-09-07T12:19:58.000Z (almost 5 years ago)
- Default Branch: main
- Last Pushed: 2021-09-07T12:59:30.000Z (almost 5 years ago)
- Last Synced: 2025-03-05T11:35:44.173Z (over 1 year ago)
- Language: Jupyter Notebook
- Size: 125 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
### Table of Contents
1. [Installation](#installation)
2. [Data](#data)
3. [Project Motivation](#motivation)
4. [File Descriptions](#files)
5. [Results](#results)
6. [Licensing, Authors, and Acknowledgements](#licensing)
For this project, some main data science libraries were used and will be needed for properly running the notebook.
These are the libraries:
* numpy
* pandas
* matplotlib
* seaborn
* scikit-learn
The data used for this project can be found here on this publicly available repository of [Kaggle](https://www.kaggle.com/gauravduttakiit/loan-defaulter).
The area of Loans has a really interesting machine learning problem which is predicting who has a defaulted loan based on the customer's characteristics. This motivated me to pursue the answer to the following questions.
* Which type of contract is the most defaulted?
* How the default behavior is divided across the customer's social context.
* What are the factors that most relate a customer to default a loan?
The file distribution on this project is pretty straightforward, the CRISP-DM approach used to analyze the Loan dataset is on the default-loans-eda.ipynb notebook, and the rest of the files are license, readme, gitignore.
The results from this data analysis can be found on this vailable [Post](https://medium.com/@eduardommelgaco/this-data-analysis-will-make-you-rethink-how-loans-are-given-bee93bb8fb87).
## Licensing, Authors, Acknowledgements
If you want to chat about this analysis or other approaches you can find me on [Twitter](https://twitter.com/python_byte). Feel free to use this code for any reference you need. And a great thanks to Kaggle for having this community that provides great datasets and Udacity for enabling this project to happen.