DesireCourse Net Udemy - Credit Risk Modeling in Python 2020

1. 1. Introduction/1. What does the course cover.mp472.92MB
2. 1. Introduction/10. Different facility types (asset classes) and credit risk modeling approaches.mp4104.45MB
3. 1. Introduction/2. What is credit risk and why is it important.mp458.17MB
4. 1. Introduction/4. Expected loss (EL) and its components PD, LGD and EAD.mp447.95MB
5. 1. Introduction/6. Capital adequacy, regulations, and the Basel II accord.mp451.03MB
6. 1. Introduction/8. Basel II approaches SA, F-IRB, and A-IRB.mp4102.44MB
7. 10. LGD and EAD Models Preparing the data/1. LGD and EAD models independent variables..mp450.03MB
8. 10. LGD and EAD Models Preparing the data/3. LGD and EAD models dependent variables.mp440.31MB
9. 10. LGD and EAD Models Preparing the data/5. LGD and EAD models distribution of recovery rates and credit conversion factors.mp440.04MB
10. 11. LGD model/1. LGD model preparing the inputs.mp424.24MB
11. 11. LGD model/10. LGD model combining stage 1 and stage 2.mp423.97MB
12. 11. LGD model/2. LGD model testing the model.mp442.67MB
13. 11. LGD model/4. LGD model estimating the accuracy of the model.mp434.84MB
14. 11. LGD model/5. LGD model saving the model.mp423.83MB
15. 11. LGD model/6. LGD model stage 2 – linear regression.mp436.06MB
16. 11. LGD model/8. LGD model stage 2 – linear regression evaluation.mp426.8MB
17. 12. EAD model/1. EAD model estimation and interpretation.mp448.01MB
18. 12. EAD model/3. EAD model validation.mp429.89MB
19. 13. Calculating expected loss/1. Calculating expected loss.mp4126.7MB
20. 2. Setting up the working environment/1. Setting up the environment - Do not skip, please!.mp45.99MB
21. 2. Setting up the working environment/2. Why Python and why Jupyter.mp429.24MB
22. 2. Setting up the working environment/3. Installing Anaconda.mp429.26MB
23. 2. Setting up the working environment/4. Jupyter Dashboard - Part 1.mp411.57MB
24. 2. Setting up the working environment/5. Jupyter Dashboard - Part 2.mp423.92MB
25. 2. Setting up the working environment/6. Installing the sklearn package.mp49.65MB
26. 3. Dataset description/1. Our example consumer loans. A first look at the dataset.mp436.7MB
27. 3. Dataset description/3. Dependent variables and independent variables.mp465.88MB
28. 4. General preprocessing/1. Importing the data into Python.mp432.86MB
29. 4. General preprocessing/3. Preprocessing few continuous variables.mp483.7MB
30. 4. General preprocessing/6. Preprocessing few discrete variables.mp446.3MB
31. 4. General preprocessing/8. Check for missing values and clean.mp425.08MB
32. 5. PD Model Data Preparation/1. How is the PD model going to look like.mp437.59MB
33. 5. PD Model Data Preparation/11. Data preparation. An example.mp449.9MB
34. 5. PD Model Data Preparation/13. Data preparation. Preprocessing discrete variables automating calculations.mp443.72MB
35. 5. PD Model Data Preparation/15. Data preparation. Preprocessing discrete variables visualizing results.mp466.35MB
36. 5. PD Model Data Preparation/16. Data preparation. Preprocessing discrete variables creating dummies (Part 1).mp449.71MB
37. 5. PD Model Data Preparation/18. Data preparation. Preprocessing discrete variables creating dummies (Part 2).mp493.29MB
38. 5. PD Model Data Preparation/21. Data preparation. Preprocessing continuous variables Automating calculations.mp445.06MB
39. 5. PD Model Data Preparation/23. Data preparation. Preprocessing continuous variables creating dummies (Part 1).mp444.03MB
40. 5. PD Model Data Preparation/25. Data preparation. Preprocessing continuous variables creating dummies (Part 2).mp4111.79MB
41. 5. PD Model Data Preparation/28. Data preparation. Preprocessing continuous variables creating dummies (Part 3).mp4100.95MB
42. 5. PD Model Data Preparation/3. Dependent variable Good Bad (default) definition.mp438.97MB
43. 5. PD Model Data Preparation/31. Data preparation. Preprocessing the test dataset.mp429.95MB
44. 5. PD Model Data Preparation/5. Fine classing, weight of evidence, and coarse classing.mp455.34MB
45. 5. PD Model Data Preparation/7. Information value.mp444.7MB
46. 5. PD Model Data Preparation/9. Data preparation. Splitting data.mp459.38MB
47. 6. PD model estimation/1. The PD model. Logistic regression with dummy variables.mp460.51MB
48. 6. PD model estimation/3. Loading the data and selecting the features.mp443.26MB
49. 6. PD model estimation/4. PD model estimation.mp424.92MB
50. 6. PD model estimation/5. Build a logistic regression model with p-values.mp4102.45MB
51. 6. PD model estimation/7. Interpreting the coefficients in the PD model.mp435.23MB
52. 7. PD model validation/1. Out-of-sample validation (test).mp452.43MB
53. 7. PD model validation/3. Evaluation of model performance accuracy and area under the curve (AUC).mp475.9MB
54. 7. PD model validation/5. Evaluation of model performance Gini and Kolmogorov-Smirnov.mp469.86MB
55. 8. Applying the PD Model for decision making/1. Calculating probability of default for a single customer.mp439.75MB
56. 8. Applying the PD Model for decision making/2. Creating a scorecard.mp497.45MB
57. 8. Applying the PD Model for decision making/4. Calculating credit score.mp441.14MB
58. 8. Applying the PD Model for decision making/6. From credit score to PD.mp423.2MB
59. 8. Applying the PD Model for decision making/8. Setting cut-offs.mp476.02MB
60. 9. PD model monitoring/1. PD model monitoring via assessing population stability.mp439.03MB
61. 9. PD model monitoring/3. Population stability index preprocessing.mp4105.25MB
62. 9. PD model monitoring/4. Population stability index calculation and interpretation.mp491.64MB