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README.md

cs109a-project

Predicting Loan Outcomes using Machine Learning

Summary

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. Use notebooks/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.


How to install -- Depricated

  • Python - 2.7.10
  • virtualenv

OSX:

  • run make create_environment
  • run workon cs109a-Predicting_loan_outcomes

Project Organization

├── 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