LendingClub is a US peer-to-peer lending company. The company claims that $15.98 billion in loans had been originated through its platform up to December 31, 2015. Lending Club enables borrowers to create unsecured personal loans between $1,000 and $40,000. The standard loan period is three years. Investors can search and browse the loan listings on Lending Club website and select loans that they want to invest in based on the information supplied about the borrower, amount of loan, loan grade, and loan purpose. Investors make money from interest. Lending Club makes money by charging borrowers an origination fee and investors a service fee
In this project, we are helping out client, an investor who wants to invest with lending club with the motivation to have a safe invest and avoid risk. Analysis on Lending club's dataset and applying different Machine Learning models to predict the interest rate for clients like Rick, who are risk averse investors.
https://www.kaggle.com/wendykan/lending-club-loan-data
Problem Framing:
, Understanding my client
, Exploratory data analysis
Data preparation:
Data cleansing
, Pre-processing
, Feature engineering vs Automated Feature Engineering (Feature Tools)
Models:
Regression
, Random forest
, Neural Networks
Cross Validation:
5-fold cross validation
Hyper-parameter optimization:
Regression: L1, L2, Elasticnet regularization
-Neural networks: Change epochs, optimizers, learning rate
, Random forest: No of trees, Tree depth
AutoML:
TPOT
, AutSKLearn
, H2o.ai
Manual model vs AutoML approaches with respect to:
, Interpretability
, Reproducibility
https://codelabs-preview.appspot.com/?file_id=1z-wMfffKigA9fq35taRTpSKoXeRIOf76y6PBzw7_Ufs#1