[DesireCourse.Net] Udemy - Credit Risk Modeling in Python 2020

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[DesireCourse.Net] Udemy - Credit Risk Modeling in Python 2020

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收录时间：2021-03-07
最近下载：2024-11-29

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文件列表

  • 13. Calculating expected loss/1. Calculating expected loss.mp4 132.9 MB
  • 5. PD Model Data Preparation/25. Data preparation. Preprocessing continuous variables creating dummies (Part 2).mp4 117.2 MB
  • 9. PD model monitoring/3. Population stability index preprocessing.mp4 110.4 MB
  • 1. Introduction/10. Different facility types (asset classes) and credit risk modeling approaches.mp4 109.5 MB
  • 6. PD model estimation/5. Build a logistic regression model with p-values.mp4 107.4 MB
  • 1. Introduction/8. Basel II approaches SA, F-IRB, and A-IRB.mp4 107.4 MB
  • 5. PD Model Data Preparation/28. Data preparation. Preprocessing continuous variables creating dummies (Part 3).mp4 105.9 MB
  • 8. Applying the PD Model for decision making/2. Creating a scorecard.mp4 102.2 MB
  • 5. PD Model Data Preparation/18. Data preparation. Preprocessing discrete variables creating dummies (Part 2).mp4 97.8 MB
  • 9. PD model monitoring/4. Population stability index calculation and interpretation.mp4 96.1 MB
  • 4. General preprocessing/3. Preprocessing few continuous variables.mp4 87.8 MB
  • 8. Applying the PD Model for decision making/8. Setting cut-offs.mp4 79.7 MB
  • 7. PD model validation/3. Evaluation of model performance accuracy and area under the curve (AUC).mp4 79.6 MB
  • 1. Introduction/1. What does the course cover.mp4 76.5 MB
  • 7. PD model validation/5. Evaluation of model performance Gini and Kolmogorov-Smirnov.mp4 73.3 MB
  • 5. PD Model Data Preparation/15. Data preparation. Preprocessing discrete variables visualizing results.mp4 69.6 MB
  • 3. Dataset description/3. Dependent variables and independent variables.mp4 69.1 MB
  • 6. PD model estimation/1. The PD model. Logistic regression with dummy variables.mp4 63.4 MB
  • 5. PD Model Data Preparation/9. Data preparation. Splitting data.mp4 62.3 MB
  • 1. Introduction/2. What is credit risk and why is it important.mp4 61.0 MB
  • 5. PD Model Data Preparation/5. Fine classing, weight of evidence, and coarse classing.mp4 58.0 MB
  • 7. PD model validation/1. Out-of-sample validation (test).mp4 55.0 MB
  • 1. Introduction/6. Capital adequacy, regulations, and the Basel II accord.mp4 53.5 MB
  • 10. LGD and EAD Models Preparing the data/1. LGD and EAD models independent variables..mp4 52.5 MB
  • 5. PD Model Data Preparation/11. Data preparation. An example.mp4 52.3 MB
  • 5. PD Model Data Preparation/16. Data preparation. Preprocessing discrete variables creating dummies (Part 1).mp4 52.1 MB
  • 12. EAD model/1. EAD model estimation and interpretation.mp4 50.3 MB
  • 1. Introduction/4. Expected loss (EL) and its components PD, LGD and EAD.mp4 50.3 MB
  • 4. General preprocessing/6. Preprocessing few discrete variables.mp4 48.5 MB
  • 5. PD Model Data Preparation/21. Data preparation. Preprocessing continuous variables Automating calculations.mp4 47.2 MB
  • 5. PD Model Data Preparation/7. Information value.mp4 46.9 MB
  • 5. PD Model Data Preparation/23. Data preparation. Preprocessing continuous variables creating dummies (Part 1).mp4 46.2 MB
  • 5. PD Model Data Preparation/13. Data preparation. Preprocessing discrete variables automating calculations.mp4 45.8 MB
  • 6. PD model estimation/3. Loading the data and selecting the features.mp4 45.4 MB
  • 11. LGD model/2. LGD model testing the model.mp4 44.7 MB
  • 8. Applying the PD Model for decision making/4. Calculating credit score.mp4 43.1 MB
  • 10. LGD and EAD Models Preparing the data/3. LGD and EAD models dependent variables.mp4 42.3 MB
  • 10. LGD and EAD Models Preparing the data/5. LGD and EAD models distribution of recovery rates and credit conversion factors.mp4 42.0 MB
  • 8. Applying the PD Model for decision making/1. Calculating probability of default for a single customer.mp4 41.7 MB
  • 9. PD model monitoring/1. PD model monitoring via assessing population stability.mp4 40.9 MB
  • 5. PD Model Data Preparation/3. Dependent variable Good Bad (default) definition.mp4 40.9 MB
  • 5. PD Model Data Preparation/1. How is the PD model going to look like.mp4 39.4 MB
  • 3. Dataset description/1. Our example consumer loans. A first look at the dataset.mp4 38.5 MB
  • 11. LGD model/6. LGD model stage 2 – linear regression.mp4 37.8 MB
  • 6. PD model estimation/7. Interpreting the coefficients in the PD model.mp4 36.9 MB
  • 11. LGD model/4. LGD model estimating the accuracy of the model.mp4 36.5 MB
  • 4. General preprocessing/1. Importing the data into Python.mp4 34.5 MB
  • 5. PD Model Data Preparation/31. Data preparation. Preprocessing the test dataset.mp4 31.4 MB
  • 12. EAD model/3. EAD model validation.mp4 31.3 MB
  • 2. Setting up the working environment/3. Installing Anaconda.mp4 30.7 MB
  • 2. Setting up the working environment/2. Why Python and why Jupyter.mp4 30.7 MB
  • 11. LGD model/8. LGD model stage 2 – linear regression evaluation.mp4 28.1 MB
  • 4. General preprocessing/8. Check for missing values and clean.mp4 26.3 MB
  • 6. PD model estimation/4. PD model estimation.mp4 26.1 MB
  • 11. LGD model/1. LGD model preparing the inputs.mp4 25.4 MB
  • 11. LGD model/10. LGD model combining stage 1 and stage 2.mp4 25.1 MB
  • 2. Setting up the working environment/5. Jupyter Dashboard - Part 2.mp4 25.1 MB
  • 11. LGD model/5. LGD model saving the model.mp4 25.0 MB
  • 8. Applying the PD Model for decision making/6. From credit score to PD.mp4 24.3 MB
  • 2. Setting up the working environment/4. Jupyter Dashboard - Part 1.mp4 12.1 MB
  • 2. Setting up the working environment/6. Installing the sklearn package.mp4 10.1 MB
  • 2. Setting up the working environment/1. Setting up the environment - Do not skip, please!.mp4 6.3 MB
  • 2. Setting up the working environment/5.1 Shortcuts-for-Jupyter.pdf 644.3 kB
  • 13. Calculating expected loss/1. Calculating expected loss.srt 20.7 kB
  • 3. Dataset description/1.1 LCDataDictionary.xlsx 20.1 kB
  • 5. PD Model Data Preparation/25. Data preparation. Preprocessing continuous variables creating dummies (Part 2).srt 19.8 kB
  • 4. General preprocessing/3. Preprocessing few continuous variables.srt 17.7 kB
  • 5. PD Model Data Preparation/28. Data preparation. Preprocessing continuous variables creating dummies (Part 3).srt 17.3 kB
  • 8. Applying the PD Model for decision making/2. Creating a scorecard.srt 17.2 kB
  • 5. PD Model Data Preparation/18. Data preparation. Preprocessing discrete variables creating dummies (Part 2).srt 15.4 kB
  • 9. PD model monitoring/3. Population stability index preprocessing.srt 15.1 kB
  • 6. PD model estimation/5. Build a logistic regression model with p-values.srt 14.8 kB
  • 7. PD model validation/3. Evaluation of model performance accuracy and area under the curve (AUC).srt 14.7 kB
  • 9. PD model monitoring/4. Population stability index calculation and interpretation.srt 14.6 kB
  • 7. PD model validation/5. Evaluation of model performance Gini and Kolmogorov-Smirnov.srt 13.8 kB
  • 5. PD Model Data Preparation/15. Data preparation. Preprocessing discrete variables visualizing results.srt 13.2 kB
  • 1. Introduction/8. Basel II approaches SA, F-IRB, and A-IRB.srt 12.9 kB
  • 1. Introduction/10. Different facility types (asset classes) and credit risk modeling approaches.srt 12.2 kB
  • 5. PD Model Data Preparation/9. Data preparation. Splitting data.srt 11.8 kB
  • 8. Applying the PD Model for decision making/8. Setting cut-offs.srt 11.7 kB
  • 5. PD Model Data Preparation/11. Data preparation. An example.srt 11.4 kB
  • 6. PD model estimation/1. The PD model. Logistic regression with dummy variables.srt 10.8 kB
  • 5. PD Model Data Preparation/23. Data preparation. Preprocessing continuous variables creating dummies (Part 1).srt 10.1 kB
  • 5. PD Model Data Preparation/16. Data preparation. Preprocessing discrete variables creating dummies (Part 1).srt 9.8 kB
  • 4. General preprocessing/6. Preprocessing few discrete variables.srt 9.1 kB
  • 7. PD model validation/1. Out-of-sample validation (test).srt 9.0 kB
  • 5. PD Model Data Preparation/5. Fine classing, weight of evidence, and coarse classing.srt 8.9 kB
  • 10. LGD and EAD Models Preparing the data/1. LGD and EAD models independent variables..srt 8.5 kB
  • 12. EAD model/1. EAD model estimation and interpretation.srt 8.2 kB
  • 3. Dataset description/3. Dependent variables and independent variables.srt 8.2 kB
  • 6. PD model estimation/7. Interpreting the coefficients in the PD model.srt 8.2 kB
  • 1. Introduction/1. What does the course cover.srt 8.2 kB
  • 5. PD Model Data Preparation/13. Data preparation. Preprocessing discrete variables automating calculations.srt 8.0 kB
  • 10. LGD and EAD Models Preparing the data/5. LGD and EAD models distribution of recovery rates and credit conversion factors.srt 7.9 kB
  • 8. Applying the PD Model for decision making/4. Calculating credit score.srt 7.7 kB
  • 6. PD model estimation/3. Loading the data and selecting the features.srt 7.5 kB
  • 5. PD Model Data Preparation/3. Dependent variable Good Bad (default) definition.srt 7.3 kB
  • 10. LGD and EAD Models Preparing the data/3. LGD and EAD models dependent variables.srt 7.1 kB
  • 9. PD model monitoring/1. PD model monitoring via assessing population stability.srt 7.0 kB
  • 5. PD Model Data Preparation/7. Information value.srt 7.0 kB
  • 11. LGD model/2. LGD model testing the model.srt 7.0 kB
  • 2. Setting up the working environment/5. Jupyter Dashboard - Part 2.srt 6.8 kB
  • 5. PD Model Data Preparation/21. Data preparation. Preprocessing continuous variables Automating calculations.srt 6.8 kB
  • 2. Setting up the working environment/2. Why Python and why Jupyter.srt 6.6 kB
  • 1. Introduction/2. What is credit risk and why is it important.srt 6.2 kB
  • 11. LGD model/4. LGD model estimating the accuracy of the model.srt 6.1 kB
  • 1. Introduction/6. Capital adequacy, regulations, and the Basel II accord.srt 5.9 kB
  • 12. EAD model/3. EAD model validation.srt 5.8 kB
  • 4. General preprocessing/1. Importing the data into Python.srt 5.7 kB
  • 8. Applying the PD Model for decision making/1. Calculating probability of default for a single customer.srt 5.7 kB
  • 5. PD Model Data Preparation/31. Data preparation. Preprocessing the test dataset.srt 5.6 kB
  • 5. PD Model Data Preparation/1. How is the PD model going to look like.srt 5.4 kB
  • 11. LGD model/6. LGD model stage 2 – linear regression.srt 5.4 kB
  • 1. Introduction/4. Expected loss (EL) and its components PD, LGD and EAD.srt 5.4 kB
  • 6. PD model estimation/4. PD model estimation.srt 5.0 kB
  • 11. LGD model/8. LGD model stage 2 – linear regression evaluation.srt 4.7 kB
  • 4. General preprocessing/8. Check for missing values and clean.srt 4.7 kB
  • 2. Setting up the working environment/3. Installing Anaconda.srt 4.7 kB
  • 11. LGD model/1. LGD model preparing the inputs.srt 4.5 kB
  • 11. LGD model/10. LGD model combining stage 1 and stage 2.srt 4.3 kB
  • 8. Applying the PD Model for decision making/6. From credit score to PD.srt 4.2 kB
  • 11. LGD model/5. LGD model saving the model.srt 4.1 kB
  • 3. Dataset description/1. Our example consumer loans. A first look at the dataset.srt 4.1 kB
  • 2. Setting up the working environment/4. Jupyter Dashboard - Part 1.srt 3.3 kB
  • 2. Setting up the working environment/6. Installing the sklearn package.srt 2.0 kB
  • 5. PD Model Data Preparation/27. Data preparation. Preprocessing continuous variables creating dummies. Homework.html 1.9 kB
  • 13. Calculating expected loss/4. Completing 100%.html 1.9 kB
  • 11. LGD model/12. Homework building an updated LGD model.html 1.5 kB
  • 5. PD Model Data Preparation/30. Data preparation. Preprocessing continuous variables creating dummies. Homework.html 1.4 kB
  • 2. Setting up the working environment/1. Setting up the environment - Do not skip, please!.srt 1.3 kB
  • 5. PD Model Data Preparation/20. Data preparation. Preprocessing discrete variables. Homework..html 1.3 kB
  • 13. Calculating expected loss/3. Homework calculate expected loss on more recent data.html 974 Bytes
  • 8. Applying the PD Model for decision making/10. Setting cut-offs. Homework.html 957 Bytes
  • 4. General preprocessing/5. Preprocessing few continuous variables Homework.html 919 Bytes
  • 12. EAD model/5. Homework building an updated EAD model.html 875 Bytes
  • 9. PD model monitoring/6. Homework building an updated PD model.html 820 Bytes
  • 4. General preprocessing/10. Check for missing values and clean Homework.html 668 Bytes
  • 13. Calculating expected loss/1.1 Calculating expected loss with comments.html 207 Bytes
  • 13. Calculating expected loss/3.1 Calculating expected loss complete notebook with comments.html 207 Bytes
  • 10. LGD and EAD Models Preparing the data/1.1 LGD and EAD models independent variables with comments.html 202 Bytes
  • 10. LGD and EAD Models Preparing the data/3.2 LGD and EAD models dependent variables with comments.html 202 Bytes
  • 10. LGD and EAD Models Preparing the data/5.1 LGD and EAD models distribution of recovery rates and credit conversion factors with comments.html 202 Bytes
  • 11. LGD model/1.1 LGD model preparing the inputs with comments.html 202 Bytes
  • 11. LGD model/10.2 LGD model combining stage 1 and stage 2 with comments.html 202 Bytes
  • 11. LGD model/2.1 LGD model testing the model with comments.html 202 Bytes
  • 11. LGD model/4.1 LGD model estimating the accuracy of the model with comments.html 202 Bytes
  • 11. LGD model/5.1 LGD model saving the model with comments.html 202 Bytes
  • 11. LGD model/6.2 LGD model stage 2 – linear regression with comments.html 202 Bytes
  • 11. LGD model/8.2 LGD model stage 2 – linear regression evaluation with comments.html 202 Bytes
  • 12. EAD model/1.1 EAD model estimation and interpretation with comments.html 202 Bytes
  • 12. EAD model/3.2 EAD model validation with comments.html 202 Bytes
  • 5. PD Model Data Preparation/18.2 Data preparation. Preprocessing discrete variables creating dummies (Part 2) with comments.html 189 Bytes
  • 5. PD Model Data Preparation/20.1 Data preparation. Preprocessing discrete variables. Homework with comments.html 189 Bytes
  • 5. PD Model Data Preparation/21.1 Data preparation. Preprocessing continuous variables Automating calculations with comments.html 189 Bytes
  • 5. PD Model Data Preparation/23.2 Data preparation. Preprocessing continuous variables creating dummies (Part 1) with comments.html 189 Bytes
  • 5. PD Model Data Preparation/25.2 Data preparation. Preprocessing continuous variables creating dummies (Part 2) with comments.html 189 Bytes
  • 5. PD Model Data Preparation/27.1 Data preparation. Preprocessing continuous variables creating dummies. Homework with comments.html 189 Bytes
  • 5. PD Model Data Preparation/28.2 Data preparation. Preprocessing continuous variables creating dummies (Part 3) with comments.html 189 Bytes
  • 5. PD Model Data Preparation/30.2 Data preparation. Preprocessing continuous variables creating dummies. Homework with comments.html 189 Bytes
  • 5. PD Model Data Preparation/31.1 Data preparation. Preprocessing the test dataset with comments.html 189 Bytes
  • 3. Dataset description/1.2 Data preparation with comments.html 188 Bytes
  • 4. General preprocessing/1.1 Importing the data into Python with comments.html 188 Bytes
  • 4. General preprocessing/10.2 Check for missing values and clean the data Homework - Solution with comments.html 188 Bytes
  • 4. General preprocessing/3.1 Preprocessing few continuous variables with comments.html 188 Bytes
  • 4. General preprocessing/5.2 Preprocessing few continuous variables Homework - Solution with comments.html 188 Bytes
  • 4. General preprocessing/6.1 Preprocessing few discrete variables with comments.html 188 Bytes
  • 4. General preprocessing/8.2 Check for missing values and clean with comments.html 188 Bytes
  • 5. PD Model Data Preparation/11.1 Data preparation. An example with comments.html 188 Bytes
  • 5. PD Model Data Preparation/13.2 Data preparation. Preprocessing discrete variables automating calculations with comments.html 188 Bytes
  • 5. PD Model Data Preparation/15.1 Data preparation. Preprocessing discrete variables visualizing results with comments.html 188 Bytes
  • 5. PD Model Data Preparation/16.1 Data preparation. Preprocessing discrete variables creating dummies (Part 1) with comments.html 188 Bytes
  • 5. PD Model Data Preparation/3.2 Dependent variable GoodBad with comments.html 188 Bytes
  • 5. PD Model Data Preparation/9.2 Data preparation. Splitting data with comments.html 188 Bytes
  • 6. PD model estimation/3.2 Loading the data and selecting the features with comments.html 187 Bytes
  • 6. PD model estimation/4.2 PD model estimation with comments.html 187 Bytes
  • 6. PD model estimation/5.2 Build a logistic regression model with p-values with comments.html 187 Bytes
  • 7. PD model validation/1.2 Out-of-sample validation (test) with comments.html 187 Bytes
  • 7. PD model validation/3.1 Evaluation of model performance accuracy and area under the curve (AUC) with comments.html 187 Bytes
  • 7. PD model validation/5.1 Evaluation of model performance Gini and Kolmogorov-Smirnov with comments.html 187 Bytes
  • 8. Applying the PD Model for decision making/1.1 Calculating probability of default for a single customer with comments.html 187 Bytes
  • 8. Applying the PD Model for decision making/2.1 Creating a scorecard with comments.html 187 Bytes
  • 8. Applying the PD Model for decision making/4.2 Calculating credit score with comments.html 187 Bytes
  • 8. Applying the PD Model for decision making/6.2 From credit score to PD with comments.html 187 Bytes
  • 8. Applying the PD Model for decision making/8.2 Setting cut-offs with comments.html 187 Bytes
  • 13. Calculating expected loss/1.2 Calculating expected loss.html 185 Bytes
  • 13. Calculating expected loss/3.2 Calculating expected loss complete notebook.html 185 Bytes
  • 10. LGD and EAD Models Preparing the data/1.2 LGD and EAD models independent variables..html 180 Bytes
  • 10. LGD and EAD Models Preparing the data/3.1 LGD and EAD models dependent variables.html 180 Bytes
  • 10. LGD and EAD Models Preparing the data/5.2 LGD and EAD models distribution of recovery rates and credit conversion factors.html 180 Bytes
  • 11. LGD model/1.3 LGD model preparing the inputs.html 180 Bytes
  • 11. LGD model/10.1 LGD model combining stage 1 and stage 2.html 180 Bytes
  • 11. LGD model/2.2 LGD model testing the model.html 180 Bytes
  • 11. LGD model/4.2 LGD model estimating the accuracy of the model.html 180 Bytes
  • 11. LGD model/5.2 LGD model saving the model.html 180 Bytes
  • 11. LGD model/6.1 LGD model stage 2 – linear regression.html 180 Bytes
  • 11. LGD model/8.1 LGD model stage 2 – linear regression evaluation.html 180 Bytes
  • 12. EAD model/1.2 EAD model estimation and interpretation.html 180 Bytes
  • 12. EAD model/3.1 EAD model validation.html 180 Bytes
  • 5. PD Model Data Preparation/32.2 PD model data preparation with comments.html 178 Bytes
  • 8. Applying the PD Model for decision making/11.1 PD model complete with comments.html 177 Bytes
  • 9. PD model monitoring/4.2 Monitoring with comments.html 177 Bytes
  • 5. PD Model Data Preparation/18.1 Data preparation. Preprocessing discrete variables creating dummies (Part 2).html 167 Bytes
  • 5. PD Model Data Preparation/20.2 Data preparation. Preprocessing discrete variables Homework - Soluton.html 167 Bytes
  • 5. PD Model Data Preparation/21.2 Data preparation. Preprocessing continuous variables Automating calculations.html 167 Bytes
  • 5. PD Model Data Preparation/23.1 Data preparation. Preprocessing continuous variables creating dummies (Part 1).html 167 Bytes
  • 5. PD Model Data Preparation/25.1 Data preparation. Preprocessing continuous variables creating dummies (Part 2).html 167 Bytes
  • 5. PD Model Data Preparation/27.2 Data preparation. Preprocessing continuous variables creating dummies. Homework.html 167 Bytes
  • 5. PD Model Data Preparation/28.1 Data preparation. Preprocessing continuous variables creating dummies (Part 3).html 167 Bytes
  • 5. PD Model Data Preparation/30.1 Data preparation. Preprocessing continuous variables creating dummies Homework - Solution.html 167 Bytes
  • 5. PD Model Data Preparation/31.2 Data preparation. Preprocessing the test dataset.html 167 Bytes
  • 3. Dataset description/1.3 Data Preparation.html 166 Bytes
  • 4. General preprocessing/1.2 Importing the data into Python.html 166 Bytes
  • 4. General preprocessing/10.1 Check for missing values and clean the data Homework - Solution.html 166 Bytes
  • 4. General preprocessing/3.2 Preprocessing few continuous variables.html 166 Bytes
  • 4. General preprocessing/5.1 Preprocessing few continuous variables Homework - Solution.html 166 Bytes
  • 4. General preprocessing/6.2 Preprocessing few discrete variables.html 166 Bytes
  • 4. General preprocessing/8.1 Check for missing values and clean.html 166 Bytes
  • 5. PD Model Data Preparation/11.2 Data preparation. An example.html 166 Bytes
  • 5. PD Model Data Preparation/13.1 Data preparation. Preprocessing discrete variables automating calculations.html 166 Bytes
  • 5. PD Model Data Preparation/15.2 Data preparation. Preprocessing discrete variables visualizing results.html 166 Bytes
  • 5. PD Model Data Preparation/16.2 Data preparation. Preprocessing discrete variables creating dummies (Part 1).html 166 Bytes
  • 5. PD Model Data Preparation/3.1 Dependent variable GoodBad.html 166 Bytes
  • 5. PD Model Data Preparation/9.1 Data preparation. Splitting data.html 166 Bytes
  • 6. PD model estimation/3.1 Loading the data and selecting the features.html 165 Bytes
  • 6. PD model estimation/4.1 PD model estimation.html 165 Bytes
  • 6. PD model estimation/5.1 Build a logistic regression model with p-values.html 165 Bytes
  • 7. PD model validation/1.1 Out-of-sample validation (test).html 165 Bytes
  • 7. PD model validation/3.2 Evaluation of model performance accuracy and area under the curve (AUC).html 165 Bytes
  • 7. PD model validation/5.2 Evaluation of model performance Gini and Kolmogorov-Smirnov.html 165 Bytes
  • 8. Applying the PD Model for decision making/1.2 Calculating probability of default for a single customer.html 165 Bytes
  • 8. Applying the PD Model for decision making/2.2 Creating a scorecard.html 165 Bytes
  • 8. Applying the PD Model for decision making/4.1 Calculating credit score.html 165 Bytes
  • 8. Applying the PD Model for decision making/6.1 From credit score to PD.html 165 Bytes
  • 8. Applying the PD Model for decision making/8.1 Setting cut-offs.html 165 Bytes
  • 5. PD Model Data Preparation/32.1 PD model data preparation.html 156 Bytes
  • 8. Applying the PD Model for decision making/11.2 PD model complete.html 155 Bytes
  • 9. PD model monitoring/4.1 Monitoring.html 155 Bytes
  • 10. LGD and EAD Models Preparing the data/1.3 loan_data_2007_2014_preprocessed.csv.html 144 Bytes
  • 11. LGD model/1.2 loan_data_2007_2014_preprocessed.csv.html 144 Bytes
  • 1. Introduction/11. Different facility types (asset classes) and credit risk modeling approaches.html 141 Bytes
  • 1. Introduction/3. What is credit risk and why is it important.html 141 Bytes
  • 1. Introduction/5. Expected loss (EL) and its components PD, LGD and EAD.html 141 Bytes
  • 1. Introduction/7. Capital adequacy, regulations, and the Basel II accord.html 141 Bytes
  • 1. Introduction/9. Basel II approaches SA, F-IRB, and A-IRB.html 141 Bytes
  • 10. LGD and EAD Models Preparing the data/2. LGD and EAD models independent variables.html 141 Bytes
  • 10. LGD and EAD Models Preparing the data/4. LGD and EAD models dependent variables.html 141 Bytes
  • 10. LGD and EAD Models Preparing the data/6. LGD and EAD models distribution of recovery rates and credit conversion factors.html 141 Bytes
  • 11. LGD model/11. LGD model combining stage 1 and stage 2.html 141 Bytes
  • 11. LGD model/3. LGD model testing the model.html 141 Bytes
  • 11. LGD model/7. LGD model stage 2 – linear regression with comments.html 141 Bytes
  • 11. LGD model/9. LGD model stage 2 – linear regression evaluation.html 141 Bytes
  • 12. EAD model/2. EAD model estimation and interpretation.html 141 Bytes
  • 12. EAD model/4. EAD model validation.html 141 Bytes
  • 13. Calculating expected loss/2. Calculating expected loss.html 141 Bytes
  • 3. Dataset description/2. Our example consumer loans. A first look at the dataset.html 141 Bytes
  • 3. Dataset description/4. Dependent variables and independent variables.html 141 Bytes
  • 4. General preprocessing/2. Importing the data into Python.html 141 Bytes
  • 4. General preprocessing/4. Preprocessing few continuous variables.html 141 Bytes
  • 4. General preprocessing/7. Preprocessing few discrete variables.html 141 Bytes
  • 4. General preprocessing/9. Check for missing values and clean.html 141 Bytes
  • 5. PD Model Data Preparation/10. Data preparation. Splitting data.html 141 Bytes
  • 5. PD Model Data Preparation/12. Data preparation. An example.html 141 Bytes
  • 5. PD Model Data Preparation/14. Data preparation. Preprocessing discrete variables automating calculations.html 141 Bytes
  • 5. PD Model Data Preparation/17. Data preparation. Preprocessing discrete variables creating dummies (Part 1).html 141 Bytes
  • 5. PD Model Data Preparation/19. Data preparation. Preprocessing discrete variables creating dummies (Part 2).html 141 Bytes
  • 5. PD Model Data Preparation/2. How is the PD model going to look like.html 141 Bytes
  • 5. PD Model Data Preparation/22. Data preparation. Preprocessing continuous variables Automating calculations.html 141 Bytes
  • 5. PD Model Data Preparation/24. Data preparation. Preprocessing continuous variables creating dummies (Part 1).html 141 Bytes
  • 5. PD Model Data Preparation/26. Data preparation. Preprocessing continuous variables creating dummies (Part 2).html 141 Bytes
  • 5. PD Model Data Preparation/29. Data preparation. Preprocessing continuous variables creating dummies (Part 3).html 141 Bytes
  • 5. PD Model Data Preparation/4. Dependent variable Good Bad (default) definition.html 141 Bytes
  • 5. PD Model Data Preparation/6. Fine classing, weight of evidence, and coarse classing.html 141 Bytes
  • 5. PD Model Data Preparation/8. Information value.html 141 Bytes
  • 6. PD model estimation/2. The PD model. Logistic regression with dummy variables.html 141 Bytes
  • 6. PD model estimation/6. Build a logistic regression model with p-values.html 141 Bytes
  • 6. PD model estimation/8. Interpreting the coefficients in the PD model.html 141 Bytes
  • 7. PD model validation/2. Out-of-sample validation (test).html 141 Bytes
  • 7. PD model validation/4. Evaluation of model performance accuracy and area under the curve (AUC).html 141 Bytes
  • 7. PD model validation/6. Evaluation of model performance Gini and Kolmogorov-Smirnov.html 141 Bytes
  • 8. Applying the PD Model for decision making/3. Creating a scorecard.html 141 Bytes
  • 8. Applying the PD Model for decision making/5. Calculating credit score.html 141 Bytes
  • 8. Applying the PD Model for decision making/7. From credit score to PD.html 141 Bytes
  • 8. Applying the PD Model for decision making/9. Setting cut-offs.html 141 Bytes
  • 9. PD model monitoring/2. PD model monitoring via assessing population stability.html 141 Bytes
  • 9. PD model monitoring/5. Population stability index calculation and interpretation.html 141 Bytes
  • 3. Dataset description/1.4 Dataset for the course.html 131 Bytes
  • 3. Dataset description/3.1 Dataset for the course.html 131 Bytes
  • 11. LGD model/12.1 Dataset with new data (loan_data_2015.csv).html 126 Bytes
  • 9. PD model monitoring/6.1 Dataset with new data (loan_data_2015.csv).html 126 Bytes
  • 5. PD Model Data Preparation/32. PD model data preparation notebooks.html 85 Bytes
  • 8. Applying the PD Model for decision making/11. PD model logistic regression notebooks.html 73 Bytes
  • 0. Websites you may like/[DesireCourse.Net].url 51 Bytes
  • 1. Introduction/[DesireCourse.Net].url 51 Bytes
  • 12. EAD model/[DesireCourse.Net].url 51 Bytes
  • 7. PD model validation/[DesireCourse.Net].url 51 Bytes
  • [DesireCourse.Net].url 51 Bytes
  • 0. Websites you may like/[CourseClub.Me].url 48 Bytes
  • 1. Introduction/[CourseClub.Me].url 48 Bytes
  • 12. EAD model/[CourseClub.Me].url 48 Bytes
  • 7. PD model validation/[CourseClub.Me].url 48 Bytes
  • [CourseClub.Me].url 48 Bytes

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