Chief Science Officer at DataPrime, Inc.
Senior Data Platform Engineer, GoodRx
Senior Data Engineer, Slintel-6sense company
Data Engineering Manager, Microsoft Corporation
In this Machine Learning Project, you will build a classification model for default prediction with LightGBM.
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Business Overview
Credit Risk is the possibility of a loss resulting from a borrower's failure to repay a
loan or meet a contractual obligation. The primary goal of a credit risk assessment is to find out whether potential borrowers are creditworthy and have the means to repay their debts so that credit risk or loss can be minimized and the loan is granted to only creditworthy applicants.
If the borrower shows an acceptable level of default risk, then their loan application can be approved upon agreed terms.
This project involves understanding financial terminologies attached to credit risk and building a classification model for default prediction with LightGBM. Hyperparameter Optimization is done using the Hyperopt library and SHAP is used for model explainability.
Aim
To predict loan defaulters and minimize the risk of loss on the basis of credit history, employment, and demographic data.
Data Description
The dataset contains information about 143727 borrowers’ on various attributes such as employment type, work experience, income, dependents, total loans, total payment done, etc.
Tech Stack
Language: Python
Libraries: pandas, numpy, matplotlib, seaborn, scikit_learn, lightgbm, hyperopt, shap
Approach
Data Reading
Data Processing
Drop Columns
Split Data
Define Label
Roll Rate Analysis
Window Roll Analysis
Feature Engineering
Label
% Amount Paid as interest in past Loan Repayment
% of Loans defaulted in the last 2 years
Exploratory Data Analysis (EDA)
Univariate Analysis
Numerical Summary: Min, Max, Mean, Median, etc
Categorical Summary: Top, Unique, Count, etc
Bivariate Analysis
Correlation Plot
Box Plots
Target Encoding
Feature Selection
Random Forest
Decision Tree
ML Model Development
LightGBM
Hyperparameter Tuning using Hyperopt
Model Selection
Model Evaluation
ROC AUC
PR AUC
Score Distribution
Feature Importance
Split and Gain
SHAP
Class Rate Curve and Right Threshold
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