| 1. Calculating expected loss.mp4 | 126.7 MB | ||
| 1. Calculating expected loss.srt | 20.2 KB | ||
| 1. Calculating probability of default for a single customer.mp4 | 39.7 MB | ||
| 1. Calculating probability of default for a single customer.srt | 5.5 KB | ||
| 1. EAD model estimation and interpretation.mp4 | 48 MB | ||
| 1. EAD model estimation and interpretation.srt | 8.1 KB | ||
| 1. How is the PD model going to look like.mp4 | 37.6 MB | ||
| 1. How is the PD model going to look like.srt | 5.3 KB | ||
| 1. Importing the data into Python.mp4 | 32.9 MB | ||
| 1. Importing the data into Python.srt | 5.6 KB | ||
| 1. LGD and EAD models independent variables..mp4 | 50 MB | ||
| 1. LGD and EAD models independent variables..srt | 8.3 KB | ||
| 1. LGD model preparing the inputs.mp4 | 24.2 MB | ||
| 1. LGD model preparing the inputs.srt | 4.4 KB | ||
| 1. Our example consumer loans. A first look at the dataset.mp4 | 36.7 MB | ||
| 1. Our example consumer loans. A first look at the dataset.srt | 4 KB | ||
| 1. Out-of-sample validation (test).mp4 | 52.4 MB | ||
| 1. Out-of-sample validation (test).srt | 8.8 KB | ||
| 1. PD model monitoring via assessing population stability.mp4 | 39 MB | ||
| 1. PD model monitoring via assessing population stability.srt | 6.9 KB | ||
| 1. Setting up the environment - Do not skip, please!.mp4 | 6 MB | ||
| 1. Setting up the environment - Do not skip, please!.srt | 1.3 KB | ||
| 1. The PD model. Logistic regression with dummy variables.mp4 | 60.5 MB | ||
| 1. The PD model. Logistic regression with dummy variables.srt | 10.6 KB | ||
| 1. What does the course cover.mp4 | 72.9 MB | ||
| 1. What does the course cover.srt | 8 KB | ||
| 1.1 Calculating expected loss with comments.html | 204 B | ||
| 1.1 Calculating probability of default for a single customer with comments.html | 204 B | ||
| 1.1 EAD model estimation and interpretation with comments.html | 204 B | ||
| 1.1 Importing the data into Python with comments.html | 204 B | ||
| 1.1 LCDataDictionary.xlsx | 19.6 KB | ||
| 1.1 LGD and EAD models independent variables with comments.html | 204 B | ||
| 1.1 LGD model preparing the inputs with comments.html | 204 B | ||
| 1.1 Out-of-sample validation (test).html | 204 B | ||
| 1.2 Calculating expected loss.html | 204 B | ||
| 1.2 Calculating probability of default for a single customer.html | 204 B | ||
| 1.2 Data preparation with comments.html | 204 B | ||
| 1.2 EAD model estimation and interpretation.html | 204 B | ||
| 1.2 Importing the data into Python.html | 204 B | ||
| 1.2 LGD and EAD models independent variables..html | 204 B | ||
| 1.2 Out-of-sample validation (test) with comments.html | 204 B | ||
| 1.2 loan_data_2007_2014_preprocessed.csv.html | 102 B | ||
| 1.3 Data Preparation.html | 204 B | ||
| 1.3 LGD model preparing the inputs.html | 204 B | ||
| 1.3 loan_data_2007_2014_preprocessed.csv.html | 102 B | ||
| 1.4 Dataset for the course.html | 102 B | ||
| 10. Check for missing values and clean Homework.html | 716 B | ||
| 10. Data preparation. Splitting data.html | 102 B | ||
| 10. Different facility types (asset classes) and credit risk modeling approaches.mp4 | 104.4 MB | ||
| 10. Different facility types (asset classes) and credit risk modeling approaches.srt | 12 KB | ||
| 10. LGD model combining stage 1 and stage 2.mp4 | 24 MB | ||
| 10. LGD model combining stage 1 and stage 2.srt | 4.2 KB | ||
| 10. Setting cut-offs. Homework.html | 921 B | ||
| 10.1 Check for missing values and clean the data Homework - Solution.html | 204 B | ||
| 10.1 LGD model combining stage 1 and stage 2.html | 204 B | ||
| 10.2 Check for missing values and clean the data Homework - Solution with comments.html | 204 B | ||
| 10.2 LGD model combining stage 1 and stage 2 with comments.html | 204 B | ||
| 11. Data preparation. An example.mp4 | 49.9 MB | ||
| 11. Data preparation. An example.srt | 11.1 KB | ||
| 11. Different facility types (asset classes) and credit risk modeling approaches.html | 102 B | ||
| 11. LGD model combining stage 1 and stage 2.html | 102 B | ||
| 11. PD model logistic regression notebooks.html | 102 B | ||
| 11.1 Data preparation. An example with comments.html | 204 B | ||
| 11.1 PD model complete with comments.html | 204 B | ||
| 11.2 Data preparation. An example.html | 204 B | ||
| 11.2 PD model complete.html | 204 B | ||
| 12. Data preparation. An example.html | 102 B | ||
| 12. Homework building an updated LGD model.html | 1.4 KB | ||
| 12.1 Dataset with new data (loan_data_2015.csv).html | 102 B | ||
| 13. Data preparation. Preprocessing discrete variables automating calculations.mp4 | 43.7 MB | ||
| 13. Data preparation. Preprocessing discrete variables automating calculations.srt | 7.8 KB | ||
| 13.1 Data preparation. Preprocessing discrete variables automating calculations.html | 204 B | ||
| 13.2 Data preparation. Preprocessing discrete variables automating calculations with comments.html | 204 B | ||
| 14. Data preparation. Preprocessing discrete variables automating calculations.html | 102 B | ||
| 15. Data preparation. Preprocessing discrete variables visualizing results.mp4 | 66.3 MB | ||
| 15. Data preparation. Preprocessing discrete variables visualizing results.srt | 12.9 KB | ||
| 15.1 Data preparation. Preprocessing discrete variables visualizing results with comments.html | 204 B | ||
| 15.2 Data preparation. Preprocessing discrete variables visualizing results.html | 204 B | ||
| 16. Data preparation. Preprocessing discrete variables creating dummies (Part 1).mp4 | 49.7 MB | ||
| 16. Data preparation. Preprocessing discrete variables creating dummies (Part 1).srt | 9.5 KB | ||
| 16.1 Data preparation. Preprocessing discrete variables creating dummies (Part 1) with comments.html | 204 B | ||
| 16.2 Data preparation. Preprocessing discrete variables creating dummies (Part 1).html | 204 B | ||
| 17. Data preparation. Preprocessing discrete variables creating dummies (Part 1).html | 102 B | ||
| 18. Data preparation. Preprocessing discrete variables creating dummies (Part 2).mp4 | 93.3 MB | ||
| 18. Data preparation. Preprocessing discrete variables creating dummies (Part 2).srt | 15.1 KB | ||
| 18.1 Data preparation. Preprocessing discrete variables creating dummies (Part 2).html | 204 B | ||
| 18.2 Data preparation. Preprocessing discrete variables creating dummies (Part 2) with comments.html | 204 B | ||
| 19. Data preparation. Preprocessing discrete variables creating dummies (Part 2).html | 102 B | ||
| 2. Calculating expected loss.html | 102 B | ||
| 2. Creating a scorecard.mp4 | 97.4 MB | ||
| 2. Creating a scorecard.srt | 16.8 KB | ||
| 2. EAD model estimation and interpretation.html | 102 B | ||
| 2. How is the PD model going to look like.html | 102 B | ||
| 2. Importing the data into Python.html | 102 B | ||
| 2. LGD and EAD models independent variables.html | 102 B | ||
| 2. LGD model testing the model.mp4 | 42.7 MB | ||
| 2. LGD model testing the model.srt | 6.8 KB | ||
| 2. Our example consumer loans. A first look at the dataset.html | 102 B | ||
| 2. Out-of-sample validation (test).html | 102 B | ||
| 2. PD model monitoring via assessing population stability.html | 102 B | ||
| 2. The PD model. Logistic regression with dummy variables.html | 102 B | ||
| 2. What is credit risk and why is it important.mp4 | 58.2 MB | ||
| 2. What is credit risk and why is it important.srt | 6.1 KB | ||
| 2. Why Python and why Jupyter.mp4 | 29.2 MB | ||
| 2. Why Python and why Jupyter.srt | 6.4 KB | ||
| 2.1 Creating a scorecard with comments.html | 204 B | ||
| 2.1 LGD model testing the model with comments.html | 204 B | ||
| 2.2 Creating a scorecard.html | 204 B | ||
| 2.2 LGD model testing the model.html | 204 B | ||
| 20. Data preparation. Preprocessing discrete variables. Homework..html | 1.2 KB | ||
| 20.1 Data preparation. Preprocessing discrete variables. Homework with comments.html | 204 B | ||
| 20.2 Data preparation. Preprocessing discrete variables Homework - Soluton.html | 204 B | ||
| 21. Data preparation. Preprocessing continuous variables Automating calculations.mp4 | 45.1 MB | ||
| 21. Data preparation. Preprocessing continuous variables Automating calculations.srt | 6.6 KB | ||
| 21.1 Data preparation. Preprocessing continuous variables Automating calculations with comments.html | 204 B | ||
| 21.2 Data preparation. Preprocessing continuous variables Automating calculations.html | 204 B | ||
| 22. Data preparation. Preprocessing continuous variables Automating calculations.html | 102 B | ||
| 23. Data preparation. Preprocessing continuous variables creating dummies (Part 1).mp4 | 44 MB | ||
| 23. Data preparation. Preprocessing continuous variables creating dummies (Part 1).srt | 9.8 KB | ||
| 23.1 Data preparation. Preprocessing continuous variables creating dummies (Part 1).html | 204 B | ||
| 23.2 Data preparation. Preprocessing continuous variables creating dummies (Part 1) with comments.html | 204 B | ||
| 24. Data preparation. Preprocessing continuous variables creating dummies (Part 1).html | 102 B | ||
| 25. Data preparation. Preprocessing continuous variables creating dummies (Part 2).mp4 | 111.8 MB | ||
| 25. Data preparation. Preprocessing continuous variables creating dummies (Part 2).srt | 19.4 KB | ||
| 25.1 Data preparation. Preprocessing continuous variables creating dummies (Part 2).html | 204 B | ||
| 25.2 Data preparation. Preprocessing continuous variables creating dummies (Part 2) with comments.html | 204 B | ||
| 26. Data preparation. Preprocessing continuous variables creating dummies (Part 2).html | 102 B | ||
| 27. Data preparation. Preprocessing continuous variables creating dummies. Homework.html | 1.9 KB | ||
| 27.1 Data preparation. Preprocessing continuous variables creating dummies. Homework with comments.html | 204 B | ||
| 27.2 Data preparation. Preprocessing continuous variables creating dummies. Homework.html | 204 B | ||
| 28. Data preparation. Preprocessing continuous variables creating dummies (Part 3).mp4 | 101 MB | ||
| 28. Data preparation. Preprocessing continuous variables creating dummies (Part 3).srt | 16.9 KB | ||
| 28.1 Data preparation. Preprocessing continuous variables creating dummies (Part 3).html | 204 B | ||
| 28.2 Data preparation. Preprocessing continuous variables creating dummies (Part 3) with comments.html | 204 B | ||
| 29. Data preparation. Preprocessing continuous variables creating dummies (Part 3).html | 102 B | ||
| 3. Creating a scorecard.html | 102 B | ||
| 3. Dependent variable Good Bad (default) definition.mp4 | 39 MB | ||
| 3. Dependent variable Good Bad (default) definition.srt | 7.1 KB | ||
| 3. Dependent variables and independent variables.mp4 | 65.9 MB | ||
| 3. Dependent variables and independent variables.srt | 8 KB | ||
| 3. EAD model validation.mp4 | 29.9 MB | ||
| 3. EAD model validation.srt | 5.6 KB | ||
| 3. Evaluation of model performance accuracy and area under the curve (AUC).mp4 | 75.9 MB | ||
| 3. Evaluation of model performance accuracy and area under the curve (AUC).srt | 14.4 KB | ||
| 3. Homework calculate expected loss on more recent data.html | 1 KB | ||
| 3. Installing Anaconda.mp4 | 29.3 MB | ||
| 3. Installing Anaconda.srt | 4.5 KB | ||
| 3. LGD and EAD models dependent variables.mp4 | 40.3 MB | ||
| 3. LGD and EAD models dependent variables.srt | 6.9 KB | ||
| 3. LGD model testing the model.html | 102 B | ||
| 3. Loading the data and selecting the features.mp4 | 43.3 MB | ||
| 3. Loading the data and selecting the features.srt | 7.4 KB | ||
| 3. Population stability index preprocessing.mp4 | 105.2 MB | ||
| 3. Population stability index preprocessing.srt | 14.8 KB | ||
| 3. Preprocessing few continuous variables.mp4 | 83.7 MB | ||
| 3. Preprocessing few continuous variables.srt | 17.3 KB | ||
| 3. What is credit risk and why is it important.html | 102 B | ||
| 3.1 Calculating expected loss complete notebook with comments.html | 204 B | ||
| 3.1 Dataset for the course.html | 102 B | ||
| 3.1 Dependent variable GoodBad.html | 204 B | ||
| 3.1 EAD model validation.html | 204 B | ||
| 3.1 Evaluation of model performance accuracy and area under the curve (AUC) with comments.html | 204 B | ||
| 3.1 LGD and EAD models dependent variables.html | 204 B | ||
| 3.1 Loading the data and selecting the features.html | 204 B | ||
| 3.1 Preprocessing few continuous variables with comments.html | 204 B | ||
| 3.2 Calculating expected loss complete notebook.html | 204 B | ||
| 3.2 Dependent variable GoodBad with comments.html | 204 B | ||
| 3.2 EAD model validation with comments.html | 204 B | ||
| 3.2 Evaluation of model performance accuracy and area under the curve (AUC).html | 204 B | ||
| 3.2 LGD and EAD models dependent variables with comments.html | 204 B | ||
| 3.2 Loading the data and selecting the features with comments.html | 204 B | ||
| 3.2 Preprocessing few continuous variables.html | 204 B | ||
| 30. Data preparation. Preprocessing continuous variables creating dummies. Homework.html | 1.4 KB | ||
| 30.1 Data preparation. Preprocessing continuous variables creating dummies Homework - Solution.html | 204 B | ||
| 30.2 Data preparation. Preprocessing continuous variables creating dummies. Homework with comments.html | 204 B | ||
| 31. Data preparation. Preprocessing the test dataset.mp4 | 30 MB | ||
| 31. Data preparation. Preprocessing the test dataset.srt | 5.5 KB | ||
| 31.1 Data preparation. Preprocessing the test dataset with comments.html | 204 B | ||
| 31.2 Data preparation. Preprocessing the test dataset.html | 204 B | ||
| 32. PD model data preparation notebooks.html | 102 B | ||
| 32.1 PD model data preparation.html | 204 B | ||
| 32.2 PD model data preparation with comments.html | 204 B | ||
| 4. Calculating credit score.mp4 | 41.1 MB | ||
| 4. Calculating credit score.srt | 7.5 KB | ||
| 4. Completing 100%.html | 1.9 KB | ||
| 4. Dependent variable Good Bad (default) definition.html | 102 B | ||
| 4. Dependent variables and independent variables.html | 102 B | ||
| 4. EAD model validation.html | 102 B | ||
| 4. Evaluation of model performance accuracy and area under the curve (AUC).html | 102 B | ||
| 4. Expected loss (EL) and its components PD, LGD and EAD.mp4 | 47.9 MB | ||
| 4. Expected loss (EL) and its components PD, LGD and EAD.srt | 5.2 KB | ||
| 4. Jupyter Dashboard - Part 1.mp4 | 11.6 MB | ||
| 4. Jupyter Dashboard - Part 1.srt | 3.2 KB | ||
| 4. LGD and EAD models dependent variables.html | 102 B | ||
| 4. LGD model estimating the accuracy of the model.mp4 | 34.8 MB | ||
| 4. LGD model estimating the accuracy of the model.srt | 5.9 KB | ||
| 4. PD model estimation.mp4 | 24.9 MB | ||
| 4. PD model estimation.srt | 4.9 KB | ||
| 4. Population stability index calculation and interpretation.mp4 | 91.6 MB | ||
| 4. Population stability index calculation and interpretation.srt | 14.3 KB | ||
| 4. Preprocessing few continuous variables.html | 102 B | ||
| 4.1 Calculating credit score.html | 204 B | ||
| 4.1 LGD model estimating the accuracy of the model with comments.html | 204 B | ||
| 4.1 Monitoring.html | 204 B | ||
| 4.1 PD model estimation.html | 204 B | ||
| 4.2 Calculating credit score with comments.html | 204 B | ||
| 4.2 LGD model estimating the accuracy of the model.html | 204 B | ||
| 4.2 Monitoring with comments.html | 204 B | ||
| 4.2 PD model estimation with comments.html | 204 B | ||
| 5. Build a logistic regression model with p-values.mp4 | 102.5 MB | ||
| 5. Build a logistic regression model with p-values.srt | 14.5 KB | ||
| 5. Calculating credit score.html | 102 B | ||
| 5. Evaluation of model performance Gini and Kolmogorov-Smirnov.mp4 | 69.9 MB | ||
| 5. Evaluation of model performance Gini and Kolmogorov-Smirnov.srt | 13.5 KB | ||
| 5. Expected loss (EL) and its components PD, LGD and EAD.html | 102 B | ||
| 5. Fine classing, weight of evidence, and coarse classing.mp4 | 55.3 MB | ||
| 5. Fine classing, weight of evidence, and coarse classing.srt | 8.7 KB | ||
| 5. Homework building an updated EAD model.html | 921 B | ||
| 5. Jupyter Dashboard - Part 2.mp4 | 23.9 MB | ||
| 5. Jupyter Dashboard - Part 2.srt | 6.6 KB | ||
| 5. LGD and EAD models distribution of recovery rates and credit conversion factors.mp4 | 40 MB | ||
| 5. LGD and EAD models distribution of recovery rates and credit conversion factors.srt | 7.7 KB | ||
| 5. LGD model saving the model.mp4 | 23.8 MB | ||
| 5. LGD model saving the model.srt | 4 KB | ||
| 5. Population stability index calculation and interpretation.html | 102 B | ||
| 5. Preprocessing few continuous variables Homework.html | 921 B | ||
| 5.1 Build a logistic regression model with p-values.html | 204 B | ||
| 5.1 Evaluation of model performance Gini and Kolmogorov-Smirnov with comments.html | 204 B | ||
| 5.1 LGD and EAD models distribution of recovery rates and credit conversion factors with comments.html | 204 B | ||
| 5.1 LGD model saving the model with comments.html | 204 B | ||
| 5.1 Preprocessing few continuous variables Homework - Solution.html | 204 B | ||
| 5.1 Shortcuts-for-Jupyter.pdf | 629.2 KB | ||
| 5.2 Build a logistic regression model with p-values with comments.html | 204 B | ||
| 5.2 Evaluation of model performance Gini and Kolmogorov-Smirnov.html | 204 B | ||
| 5.2 LGD and EAD models distribution of recovery rates and credit conversion factors.html | 204 B | ||
| 5.2 LGD model saving the model.html | 204 B | ||
| 5.2 Preprocessing few continuous variables Homework - Solution with comments.html | 204 B | ||
| 6. Build a logistic regression model with p-values.html | 102 B | ||
| 6. Capital adequacy, regulations, and the Basel II accord.mp4 | 51 MB | ||
| 6. Capital adequacy, regulations, and the Basel II accord.srt | 5.8 KB | ||
| 6. Evaluation of model performance Gini and Kolmogorov-Smirnov.html | 102 B | ||
| 6. Fine classing, weight of evidence, and coarse classing.html | 102 B | ||
| 6. From credit score to PD.mp4 | 23.2 MB | ||
| 6. From credit score to PD.srt | 4.1 KB | ||
| 6. Homework building an updated PD model.html | 819 B | ||
| 6. Installing the sklearn package.mp4 | 9.7 MB | ||
| 6. Installing the sklearn package.srt | 1.9 KB | ||
| 6. LGD and EAD models distribution of recovery rates and credit conversion factors.html | 102 B | ||
| 6. LGD model stage 2 – linear regression.mp4 | 36.1 MB | ||
| 6. LGD model stage 2 – linear regression.srt | 5.3 KB | ||
| 6. Preprocessing few discrete variables.mp4 | 46.3 MB | ||
| 6. Preprocessing few discrete variables.srt | 8.9 KB | ||
| 6.1 Dataset with new data (loan_data_2015.csv).html | 102 B | ||
| 6.1 From credit score to PD.html | 204 B | ||
| 6.1 LGD model stage 2 – linear regression.html | 204 B | ||
| 6.1 Preprocessing few discrete variables with comments.html | 204 B | ||
| 6.2 From credit score to PD with comments.html | 204 B | ||
| 6.2 LGD model stage 2 – linear regression with comments.html | 204 B | ||
| 6.2 Preprocessing few discrete variables.html | 204 B | ||
| 7. Capital adequacy, regulations, and the Basel II accord.html | 102 B | ||
| 7. From credit score to PD.html | 102 B | ||
| 7. Information value.mp4 | 44.7 MB | ||
| 7. Information value.srt | 6.9 KB | ||
| 7. Interpreting the coefficients in the PD model.mp4 | 35.2 MB | ||
| 7. Interpreting the coefficients in the PD model.srt | 8 KB | ||
| 7. LGD model stage 2 – linear regression with comments.html | 102 B | ||
| 7. Preprocessing few discrete variables.html | 102 B | ||
| 8. Basel II approaches SA, F-IRB, and A-IRB.mp4 | 102.4 MB | ||
| 8. Basel II approaches SA, F-IRB, and A-IRB.srt | 12.6 KB | ||
| 8. Check for missing values and clean.mp4 | 25.1 MB | ||
| 8. Check for missing values and clean.srt | 4.6 KB | ||
| 8. Information value.html | 102 B | ||
| 8. Interpreting the coefficients in the PD model.html | 102 B | ||
| 8. LGD model stage 2 – linear regression evaluation.mp4 | 26.8 MB | ||
| 8. LGD model stage 2 – linear regression evaluation.srt | 4.6 KB | ||
| 8. Setting cut-offs.mp4 | 76 MB | ||
| 8. Setting cut-offs.srt | 11.4 KB | ||
| 8.1 Check for missing values and clean.html | 204 B | ||
| 8.1 LGD model stage 2 – linear regression evaluation.html | 204 B | ||
| 8.1 Setting cut-offs.html | 204 B | ||
| 8.2 Check for missing values and clean with comments.html | 204 B | ||
| 8.2 LGD model stage 2 – linear regression evaluation with comments.html | 204 B | ||
| 8.2 Setting cut-offs with comments.html | 204 B | ||
| 9. Basel II approaches SA, F-IRB, and A-IRB.html | 102 B | ||
| 9. Check for missing values and clean.html | 102 B | ||
| 9. Data preparation. Splitting data.mp4 | 59.4 MB | ||
| 9. Data preparation. Splitting data.srt | 11.5 KB | ||
| 9. LGD model stage 2 – linear regression evaluation.html | 102 B | ||
| 9. Setting cut-offs.html | 102 B | ||
| 9.1 Data preparation. Splitting data.html | 204 B | ||
| 9.2 Data preparation. Splitting data with comments.html | 204 B | ||
| [CourseClub.Me].url | 0 B | ||
| [DesireCourse.Net].url | 0 B | ||
| ▲ 301 total files | |||
Credit Risk Modeling in Python 2020
A complete data science case study: preprocessing, modeling, model validation and maintenance in Python
Created by 365 Careers
Last updated 6/2020
English
English [Auto-generated]
For More Courses Visit: https://desirecourse.net
| torrent name | size | uploader | age | seed | leech |
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| 1009.3 MB | freecoursewb | 5 months | 2 | 6 | |
| 3.1 GB | freecoursewb | 8 months | 0 | 0 | |
| 816.9 MB | freecoursewb | 10 months | 0 | 0 | |
| 926.5 MB | freecoursewb | 10 months | 0 | 0 | |
| 2 GB | freecoursewb | 1 year | 0 | 0 |
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