Deep Credit Risk: Machine Learning with Python - Couverture souple

Rösch, Daniel; Scheule, Harald

Couverture souple

ISBN 13 : 9798617590199

Editeur : Independently published, 2020

Synopsis

www.deepcreditrisk.com provides real credit data, apps and much more.

"Deep Credit Risk - Machine Learning with Python" aims at starters and pros alike to enable you to:
- Understand the role of liquidity, equity and many other key banking features
- Engineer and select features
- Predict defaults, payoffs, loss rates and exposures
- Predict downturn and crisis outcomes using pre-crisis features
- Understand the implications of COVID-19
- Apply innovative sampling techniques for model training and validation
- Deep-learn from Logit Classifiers to Random Forests and Neural Networks
- Do unsupervised Clustering, Principal Components and Bayesian Techniques
- Build multi-period models for CECL, IFRS 9 and CCAR
- Build credit portfolio correlation models for VaR and Expected Shortfall
- Run over 1,500 lines of pandas, statsmodels and scikit-learn Python code

Détails de l'édition

• Éditeur: Independently published
• Date d'édition: 2020
• Langue: anglais
• ISBN 13: 9798617590199
• Reliure: Broché
• Nombre de pages: 473
• Coordonnées du fabricant: non disponible
• Personne responsable: non disponible