Loan Default

This repository contains scripts and notebooks to:

• Analyze the relationship between different features and loan default status
• Build machine learning models to predict which companies will default on their loans
• Explain how different features impact model predictions

Setup Instructions

The instructions here are required only if you wish to view/run the Jupyter Notebook on your local machine. Otherwise, you can just proceed to Viewing Instructions: To view the HTML version of the results, which does not require any prior setup.

Run setup.sh to setup your conda python environment and install the necessary libraries. This set of instructions assumes that you are using a linux system with conda pre-installed.

chmod +x ./setup.sh
./setup.sh
conda activate loan-default

Viewing Instructions

To view the Jupyter Notebook version of the results:

• From the Home Page of the Jupyter Notebook, navigate to and open code_addison.ipynb.
• Run all the cells in the notebook from top to bottom (Need to execute this step to view the interactive visualizations).

To view the HTML version of the results:

• Open code_addison.html in your browser.

To view the helper scripts used in code_addison.ipynb:

• Navigate to and open utils/__init__.py

Files

code_addison.ipynb

• Jupyter Notebook containing the analysis results

code_addison.html

• HTML version of code_addison.ipynb

utils/__init__.py

• Contains helper classes and functions for analysis

requirements.txt

• Contains the list of dependencies required to run code_addison.ipynb and utils/__init__.py

README.md

• Contains the instructions for viewing the analysis results

data/train.csv

• Provided train dataset for analysis and modelling

data/test.csv

• Provided unseen test dataset for model inference

submissions_addison.csv

• Test submission, containing the model inference on the unseen test dataset

presentation_addison.pdf

• Presentation slides