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种子名称： [DesireCourse.Net] Udemy - Credit Risk Modeling in Python 2020
文件类型： 视频
文件数目： 62个文件
文件大小： 3.16 GB
收录时间： 2021-10-14 02:29
已经下载： 3次
资源热度： 259
最近下载： 2024-11-14 03:18

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

1. Introduction/1. What does the course cover.mp472.92MB

1. Introduction/10. Different facility types (asset classes) and credit risk modeling approaches.mp4104.45MB

1. Introduction/2. What is credit risk and why is it important.mp458.17MB

1. Introduction/4. Expected loss (EL) and its components PD, LGD and EAD.mp447.95MB

1. Introduction/6. Capital adequacy, regulations, and the Basel II accord.mp451.03MB

1. Introduction/8. Basel II approaches SA, F-IRB, and A-IRB.mp4102.44MB

10. LGD and EAD Models Preparing the data/1. LGD and EAD models independent variables..mp450.03MB

10. LGD and EAD Models Preparing the data/3. LGD and EAD models dependent variables.mp440.31MB

10. LGD and EAD Models Preparing the data/5. LGD and EAD models distribution of recovery rates and credit conversion factors.mp440.04MB

11. LGD model/1. LGD model preparing the inputs.mp424.24MB

11. LGD model/10. LGD model combining stage 1 and stage 2.mp423.97MB

11. LGD model/2. LGD model testing the model.mp442.67MB

11. LGD model/4. LGD model estimating the accuracy of the model.mp434.84MB

11. LGD model/5. LGD model saving the model.mp423.83MB

11. LGD model/6. LGD model stage 2 – linear regression.mp436.06MB

11. LGD model/8. LGD model stage 2 – linear regression evaluation.mp426.8MB

12. EAD model/1. EAD model estimation and interpretation.mp448.01MB

12. EAD model/3. EAD model validation.mp429.89MB

13. Calculating expected loss/1. Calculating expected loss.mp4126.7MB

2. Setting up the working environment/1. Setting up the environment - Do not skip, please!.mp45.99MB

2. Setting up the working environment/2. Why Python and why Jupyter.mp429.24MB

2. Setting up the working environment/3. Installing Anaconda.mp429.26MB

2. Setting up the working environment/4. Jupyter Dashboard - Part 1.mp411.57MB

2. Setting up the working environment/5. Jupyter Dashboard - Part 2.mp423.92MB

2. Setting up the working environment/6. Installing the sklearn package.mp49.65MB

3. Dataset description/1. Our example consumer loans. A first look at the dataset.mp436.7MB

3. Dataset description/3. Dependent variables and independent variables.mp465.88MB

4. General preprocessing/1. Importing the data into Python.mp432.86MB

4. General preprocessing/3. Preprocessing few continuous variables.mp483.7MB

4. General preprocessing/6. Preprocessing few discrete variables.mp446.3MB

4. General preprocessing/8. Check for missing values and clean.mp425.08MB

5. PD Model Data Preparation/1. How is the PD model going to look like.mp437.59MB

5. PD Model Data Preparation/11. Data preparation. An example.mp449.9MB

5. PD Model Data Preparation/13. Data preparation. Preprocessing discrete variables automating calculations.mp443.72MB

5. PD Model Data Preparation/15. Data preparation. Preprocessing discrete variables visualizing results.mp466.35MB

5. PD Model Data Preparation/16. Data preparation. Preprocessing discrete variables creating dummies (Part 1).mp449.71MB

5. PD Model Data Preparation/18. Data preparation. Preprocessing discrete variables creating dummies (Part 2).mp493.29MB

5. PD Model Data Preparation/21. Data preparation. Preprocessing continuous variables Automating calculations.mp445.06MB

5. PD Model Data Preparation/23. Data preparation. Preprocessing continuous variables creating dummies (Part 1).mp444.03MB

5. PD Model Data Preparation/25. Data preparation. Preprocessing continuous variables creating dummies (Part 2).mp4111.79MB

5. PD Model Data Preparation/28. Data preparation. Preprocessing continuous variables creating dummies (Part 3).mp4100.95MB

5. PD Model Data Preparation/3. Dependent variable Good Bad (default) definition.mp438.97MB

5. PD Model Data Preparation/31. Data preparation. Preprocessing the test dataset.mp429.95MB

5. PD Model Data Preparation/5. Fine classing, weight of evidence, and coarse classing.mp455.34MB

5. PD Model Data Preparation/7. Information value.mp444.7MB

5. PD Model Data Preparation/9. Data preparation. Splitting data.mp459.38MB

6. PD model estimation/1. The PD model. Logistic regression with dummy variables.mp460.51MB

6. PD model estimation/3. Loading the data and selecting the features.mp443.26MB

6. PD model estimation/4. PD model estimation.mp424.92MB

6. PD model estimation/5. Build a logistic regression model with p-values.mp4102.45MB

6. PD model estimation/7. Interpreting the coefficients in the PD model.mp435.23MB

7. PD model validation/1. Out-of-sample validation (test).mp452.43MB

7. PD model validation/3. Evaluation of model performance accuracy and area under the curve (AUC).mp475.9MB

7. PD model validation/5. Evaluation of model performance Gini and Kolmogorov-Smirnov.mp469.86MB

8. Applying the PD Model for decision making/1. Calculating probability of default for a single customer.mp439.75MB

8. Applying the PD Model for decision making/2. Creating a scorecard.mp497.45MB

8. Applying the PD Model for decision making/4. Calculating credit score.mp441.14MB

8. Applying the PD Model for decision making/6. From credit score to PD.mp423.2MB

8. Applying the PD Model for decision making/8. Setting cut-offs.mp476.02MB

9. PD model monitoring/1. PD model monitoring via assessing population stability.mp439.03MB

9. PD model monitoring/3. Population stability index preprocessing.mp4105.25MB

9. PD model monitoring/4. Population stability index calculation and interpretation.mp491.64MB