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Capstone project for NYCDSA to study Lending Club peer-to-peer loans
Jupyter Notebook 99.90%
Python 0.10%
lending-club's Introduction
Lending Club Loan Analysis
- A joint project by fellows of the New York City Data Science Academy
- By running
import config, a data folder will be created.
- Download
accepted_2007_to_2018Q4.csv from https://www.kaggle.com/wordsforthewise/lending-club
- Place the downloaded file in the data folder.
- The code in the Jupyter Notebooks will execute as expected without error.
- Run the
Create_Working_DataFrame.ipynb Jupyter Notebook to create the working data file.
- You can see an example Jupyter Notebook in EDA/Sample_EDA.ipynb.
- data: Storage for the data used by EDA and the models
- data_prep: Jupyter Notebooks to manage getting the data and shaping it for analysis
- EDA: Jupyter Notebooks used to explore the data
- lending_club: Python package used by the Jupyter Notebooks
- models: Machine Learning models for predicting defaults
- Project Documentations: Background about the project
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