Python-based predictive and financial modeling project built during the JPMorgan Chase & Co. Quantitative Research Virtual Experience (Forage).
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Price Forecasting & Credit Risk Models is a collection of Python-based tasks completed for the JPMorgan Chase & Co. Quantitative Research Virtual Experience Program (Forage, Nov 2025).
It showcases practical applications of time-series forecasting, quantitative pricing models, and credit risk prediction using machine learning.
📈 Built linear regression models to forecast natural gas prices based on historical monthly data
📊 Designed a pricing engine for gas storage contracts, simulating injection, withdrawal, and cost parameters
🤖 Developed an XGBoost-based classifier to estimate loan default probability (PD) and compute expected loss with 90%+ accuracy
📉 Visualized feature importance and price trends to interpret model behavior
- Loaded and preprocessed historical natural gas prices from CSV files
- Used Linear Regression to extrapolate monthly prices one year into the future
- Implemented
calculate_contract()to simulate gas injection, withdrawal, and cost-based cash flows - Visualized actual vs predicted price trends using Matplotlib
- Processed borrower dataset and trained an XGBoostClassifier
- Evaluated model performance with Accuracy, F1 Score, and AUC metrics
- Created
predict_loss_simple()to compute expected credit loss using PD and recovery rate assumptions - Displayed feature importance and decision trees for interpretability
XGBoost Model importance score - Analyzed a book of loans to estimate a customer's probability of default
