https://github.com/dataquestio/loan-prediction

Predict which loans will be foreclosed on.
https://github.com/dataquestio/loan-prediction

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Predict which loans will be foreclosed on.

• Host: GitHub
• URL: https://github.com/dataquestio/loan-prediction
• Owner: dataquestio
• Created: 2016-06-29T00:07:26.000Z (over 8 years ago)
• Default Branch: master
• Last Pushed: 2023-05-10T09:22:52.000Z (over 1 year ago)
• Last Synced: 2024-11-05T19:34:12.932Z (7 days ago)
• Language: Python
• Homepage:
• Size: 4.88 KB
• Stars: 216
• Watchers: 22
• Forks: 141
• Open Issues: 5
• Metadata Files:
  • Readme: README.md

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README

        
Loan Prediction

-----------------------

Predict whether or not loans acquired by Fannie Mae will go into foreclosure.  Fannie Mae acquires loans from other lenders as a way of inducing them to lend more.  Fannie Mae releases data on the loans it has acquired and their performance afterwards [here](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html).

Installation

----------------------

### Download the data

* Clone this repo to your computer.

* Get into the folder using `cd loan-prediction`.

* Run `mkdir data`.

* Switch into the `data` directory using `cd data`.

* Download the data files from Fannie Mae into the `data` directory.  

    * You can find the data [here](http://www.fanniemae.com/portal/funding-the-market/data/loan-performance-data.html).

    * You'll need to register with Fannie Mae to download the data.

    * It's recommended to download all the data from 2012 Q1 to present.

* Extract all of the `.zip` files you downloaded.

    * On OSX, you can run `find ./ -name \*.zip -exec unzip {} \;`.

    * At the end, you should have a bunch of text files called `Acquisition_YQX.txt`, and `Performance_YQX.txt`, where `Y` is a year, and `X` is a number from `1` to `4`.

* Remove all the zip files by running `rm *.zip`.

* Switch back into the `loan-prediction` directory using `cd ..`.

### Install the requirements

 

* Install the requirements using `pip install -r requirements.txt`.

    * Make sure you use Python 3.

    * You may want to use a virtual environment for this.

Usage

-----------------------

* Run `mkdir processed` to create a directory for our processed datasets.

* Run `python assemble.py` to combine the `Acquisition` and `Performance` datasets.

    * This will create `Acquisition.txt` and `Performance.txt` in the `processed` folder.

* Run `python annotate.py`.

    * This will create training data from `Acquisition.txt` and `Performance.txt`.

    * It will add a file called `train.csv` to the `processed` folder.

* Run `python predict.py`.

    * This will run cross validation across the training set, and print the accuracy score.

Extending this

-------------------------

If you want to extend this work, here are a few places to start:

* Generate more features in `annotate.py`.

* Switch algorithms in `predict.py`.

* Add in a way to make predictions on future data.

* Try seeing if you can predict if a bank should have issued the loan.

    * Remove any columns from `train` that the bank wouldn't have known at the time of issuing the loan.

        * Some columns are known when Fannie Mae bought the loan, but not before

    * Make predictions.

* Explore seeing if you can predict columns other than `foreclosure_status`.

    * Can you predict how much the property will be worth at sale time?

* Explore the nuances between performance updates.

    * Can you predict how many times the borrower will be late on payments?

    * Can you map out the typical loan lifecycle?