Deep Credit Risk: Machine Learning with Python
RÃ sch, Daniel,Scheule, Harald
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Language: English
Published by Independently published, 2020
- Softcover
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Softcover
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- Title
- Deep Credit Risk: Machine Learning with Python
- Author
- RÃ sch, Daniel,Scheule, Harald
- Publisher
- Independently published
- Publication year
- 2020
- Condition
- Good
- Binding
- paperback
- Language
- English
- ISBN 13
- 9798617590199
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
"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
"Synopsis" may belong to another edition of this title.
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