Lending Club Loan Analysis A joint project by fellows of the New York City Data Science Academy to evaluate investment opportunities in Lending Club loans The write-up for this project can be found at https://nycdatascience.com/blog/student-works/are-lending-club-notes-a-good-investment/ Setup 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 in the data_prep folder to create the working data file. You can see an example Jupyter Notebook in EDA/Sample_EDA.ipynb. Folder Structure 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