Articles liés à Deep Credit Risk: Machine Learning with Python

Deep Credit Risk: Machine Learning with Python - Couverture souple

Rösch, Daniel; Scheule, Harald

 
9798617590199: Deep Credit Risk: Machine Learning with Python
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

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

Éditeur
Independently published
Date d'édition
2020
Langue
anglais
ISBN 13 
9798617590199
Reliure
Broché
Nombre de pages
473
Coordonnées du fabricant
Manufactured by Amazon on behalf of the author
https://www.amazon.fr/hz/contact-us

c/o Amazon Media EU S.à.r.l., 38 Avenue John F. Kennedy
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Luxembourg