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

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视频 2021-10-14 02:29 2024-12-18 10:00 269 3.16 GB 62
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文件列表
  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
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