Lending club Loan data analysis with Keras and TensorFlow – AI Project for Loans Data

Deep Learning with Keras and TensorFlow
Project – Lending Club Loan Data Analysis
Objective: Create a model that predicts whether or not a loan will be default using 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 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 (the 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 (the 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 has 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).
● not.fully.paid: 0 → The loan was fully paid. 1 → The loan was not fully paid (i.e., defaulted, charged off, or missed payments).
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:
1. Feature Transformation
●
Transform categorical values into numerical values (discrete)
2. Exploratory data analysis of different factors in the dataset.
3. Additional Feature Engineering
●
You will check the correlation between features and drop those features that have a strong correlation.
●
This will help reduce the number of features and leave you with the most relevant features.
4. 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.

 

Solution – lending_club_loan_default_predictionipynb