Predicting Loan Outcomes using Machine Learning
This was our final project for the Fall 2016 class of CS109A - Introduction to Data Science (Harvard). The framework below was based on the cookiecutter data science project template.
-
Final project website: https://quinnlee.github.io/cs109a-Project/
-
Download the data: Get the data from https://www.kaggle.com/wendykan/lending-club-loan-data. Move
loan.csv
to/data/raw/lending-club-loan-data/
-
To replicate the main analysis: Run
jupyter notebook
to launch notebooks. Usenotebooks/Final-project.ipynb
for the complete model. -
Other info:
src/data
has the code we used to clean the data. Other notebooks in the/notebooks/
can be used to follow exploratory data analysis, draft visualizations, and the baseline model.
- Python - 2.7.10
- virtualenv
OSX:
- run
make create_environment
- run
workon cs109a-Predicting_loan_outcomes
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── src <- Source code for use in this project.
│ ├── __init__.py <- Makes src a Python module
│ │
│ ├── data <- Scripts to download or generate data
│ │ └── make_dataset.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── build_features.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ ├── predict_model.py
│ │ └── train_model.py
│ │
│ └── visualization <- Scripts to create exploratory and results oriented visualizations
│ └── visualize.py
│
└── tox.ini <- tox file with settings for running tox; see tox.testrun.org