In this Python video we show you how to:
✔️ Describe data
✔️ Plot data
✔️ Generate new variables
✔️ Transform variables
✔️ Subset data
✔️ Fir regression models
More details on: [ Ссылка ]
"Deep Credit Risk — Machine Learning in 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 value-at-risk and expected shortfall;
✔️ Run over 1,500 lines of R code; and
✔️ Access real credit data and much more …
Courses: [ Ссылка ]
#CreditRiskModelling #CreditRiskModelingInRCourse #datascienceprojects #machinelearningprojects #artificialintelligenceprojects #rprogramming #coding #dataanalytics #statistics #ml #deeplearning #rtutorial #deepcreditrisk
