Real-Time Credit Default Probability Prediction Model

Develop an advanced credit risk assessment spreadsheet utilizing machine learning algorithms to predict default probabilities in real-time. Integrate multiple data sources including financial statements, market indicators, and alternative credit data. Implement ensemble machine learning techniques combining logistic regression, decision trees, and neural network approaches to generate probabilistic default risk scores. Create interactive visualization tools that demonstrate model confidence and feature importance.