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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 $0\mathrm{.}11).$ 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$\mathrm{.}6$mths: The borrower's number of inquiries by creditors in the last $6$ months.

$$ delinq$\mathrm{.}2$yrs: 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$).$

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.

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