DESCRIPTION
Create a model that predicts whether or not a loan will be default using the historical data.
Problem Statement:
For companies like Lending Club correctly predicting whether or not a loan will be a default is very important. In this project, using the historical data from 2007 to 2015, you have to build a deep learning model to predict the chance of default for future loans. As you will see later this dataset is highly imbalanced and includes a lot of features that make this problem more challenging.
Domain: Finance
Analysis to be done: Perform data preprocessing and build a deep learning prediction model.
Content:
Dataset columns and definition:
credit.policy: 1 if the customer meets the credit underwriting criteria of LendingClub.com, and 0 otherwise.
purpose: The purpose of the loan (takes values "credit_card", "debt_consolidation", "educational", "major_purchase", "small_business", and "all_other").
int.rate: The interest rate of the loan, as a proportion (a rate of 11% would be stored as 0.11). Borrowers judged by LendingClub.com to be more risky are assigned higher interest rates.
installment: The monthly installments owed by the borrower if the loan is funded.
log.annual.inc: The natural log of the self-reported annual income of the borrower.
dti: The debt-to-income ratio of the borrower (amount of debt divided by annual income).
fico: The FICO credit score of the borrower.
days.with.cr.line: The number of days the borrower has had a credit line.
revol.bal: The borrower's revolving balance (amount unpaid at the end of the credit card billing cycle).
revol.util: The borrower's revolving line utilization rate (the amount of the credit line used relative to total credit available).
inq.last.6mths: The borrower's number of inquiries by creditors in the last 6 months.
delinq.2yrs: The number of times the borrower had been 30+ days past due on a payment in the past 2 years.
pub.rec: The borrower's number of derogatory public records (bankruptcy filings, tax liens, or judgments).
Steps to perform:
Perform exploratory data analysis and feature engineering and then apply feature engineering. Follow up with a deep learning model to predict whether or not the loan will be default using the historical data.
Tasks:
-
Feature Transformation
Transform categorical values into numerical values (discrete)
-
Exploratory data analysis of different factors of the dataset. -
Additional Feature Engineering
You will check the correlation between features and will drop those features which have a strong correlation
This will help reduce the number of features and will leave you with the most relevant features
-
Modeling
After applying EDA and feature engineering, you are now ready to build the predictive models
In this part, you will create a deep learning model using Keras with Tensorflow backend