365 Data Science - Credit Risk Modeling in Python [CoursesGhar]

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365 Data Science - Credit Risk Modeling in Python [CoursesGhar]

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种子哈希：bfca304c75dd5568c55fa46060b2f55f83907aff
文件大小： 1.16G
已经下载：2085次
下载速度：极快
收录时间：2021-11-03
最近下载：2025-10-10

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文件列表

  • 1. Introduction/1. What is credit risk and why is it important.mp4 57.8 MB
  • 9. PD model monitoring/2. Population stability index - preprocessing.mp4 55.1 MB
  • 1. Introduction/2. Expected loss (EL) and its components - PD, LGD and EAD.mp4 48.3 MB
  • 13. Calculating expected loss/1. Calculating expected loss.mp4 47.8 MB
  • 6. PD model estimation/4. Build a logistic regression model with p-values..mp4 43.8 MB
  • 8. Applying the PD model for decision making/2. Creating a scorecard.mp4 42.1 MB
  • 5. PD model_ data preparation/13. Data preparation. Preprocessing continuous variables - creating dummies (Part 2).mp4 39.9 MB
  • 5. PD model_ data preparation/14. Data preparation. Preprocessing continuous variables - creating dummies (Part 3).mp4 37.8 MB
  • 5. PD model_ data preparation/10. Data preparation. Preprocessing discrete variables - creating dummies (Part 2).mp4 36.5 MB
  • 9. PD model monitoring/3. Population stability index - calculation and interpretation.mp4 34.5 MB
  • 1. Introduction/5. Different facility types (asset classes) and credit risk modeling approaches.mp4 33.8 MB
  • 1. Introduction/4. Basel II approaches - SA, F-IRB, and A-IRB.mp4 31.1 MB
  • 8. Applying the PD model for decision making/5. Setting cut-offs.mp4 31.1 MB
  • 4. General preprocessing/2. Preprocessing few continuous variables.mp4 31.1 MB
  • 7. PD model validation (test)/2. Evaluation of model performance - accuracy and area under the curve (AUC).mp4 26.4 MB
  • 7. PD model validation (test)/3. Evaluation of model performance - Gini and Kolmogorov-Smirnov..mp4 24.4 MB
  • 5. PD model_ data preparation/8. Data preparation. Preprocessing discrete variables - visualizing results.mp4 23.8 MB
  • 6. PD model estimation/1. The PD model. Logistic regression with dummy variables.mp4 21.3 MB
  • 5. PD model_ data preparation/5. Data preparation. Splitting data.mp4 21.3 MB
  • 7. PD model validation (test)/1. Out-of-sample validation (test)..mp4 21.1 MB
  • 5. PD model_ data preparation/11. Data preparation. Preprocessing continuous variables - automating calculations.mp4 20.4 MB
  • 3. Dataset description/2. Dependent variables and independent variables.mp4 20.1 MB
  • 4. General preprocessing/3. Preprocessing few discrete variables.mp4 19.8 MB
  • 10. LGD and EAD models/1. LGD and EAD models - independent variables.mp4 18.9 MB
  • 5. PD model_ data preparation/9. Data Preparation. Preprocessing Discrete Variables - Creating Dummies (Part 1).mp4 18.6 MB
  • 12. EAD model/1. EAD model estimation and interpretation.mp4 18.5 MB
  • 5. PD model_ data preparation/6. Data preparation. An example.mp4 18.4 MB
  • 11. LGD model/2. LGD model - testing the model.mp4 17.7 MB
  • 5. PD model_ data preparation/7. Data preparation. Preprocessing discrete variables - automating calculations.mp4 17.0 MB
  • 4. General preprocessing/1. Importing the data into Python.mp4 16.9 MB
  • 5. PD model_ data preparation/3. Fine classing, weight of evidence, and coarse classing.mp4 16.5 MB
  • 6. PD model estimation/2. Loading the data and selecting the features.mp4 16.4 MB
  • 1. Introduction/3. Capital adequacy, regulations, and the Basel II accord.mp4 16.2 MB
  • 8. Applying the PD model for decision making/3. Calculating credit score.mp4 15.4 MB
  • 11. LGD model/5. LGD model - stage 2 – linear regression.mp4 15.4 MB
  • 5. PD model_ data preparation/12. Data preparation. Preprocessing continuous variables - creating dummies (Part 1).mp4 15.2 MB
  • 3. Dataset description/1. Our example - consumer loans. A first look at the dataset.mp4 14.7 MB
  • 10. LGD and EAD models/3. LGD and EAD models - distribution of recovery rates and credit conversion factors.mp4 14.7 MB
  • 10. LGD and EAD models/2. LGD and EAD models - dependent variables.mp4 14.6 MB
  • 8. Applying the PD model for decision making/1. Calculating probability of default for a single customer.mp4 13.9 MB
  • 5. PD model_ data preparation/4. Information value.mp4 13.5 MB
  • 11. LGD model/3. LGD model - estimating the accuracy of the model.mp4 12.9 MB
  • 5. PD model_ data preparation/2. Dependent variable - Good_ Bad (default) definition.mp4 12.9 MB
  • 5. PD model_ data preparation/1. How is the PD model going to look like.mp4 12.5 MB
  • 5. PD model_ data preparation/15. Data preparation. Preprocessing the test dataset.mp4 12.3 MB
  • 6. PD model estimation/5. Interpreting the coefficients in the PD model.mp4 11.9 MB
  • 9. PD model monitoring/1. PD model monitoring via assessing population stability.mp4 11.1 MB
  • 12. EAD model/2. EAD model validation.mp4 10.9 MB
  • 11. LGD model/6. LGD model - stage 2 – linear regression evaluation.mp4 10.1 MB
  • 4. General preprocessing/4. Check for missing values and clean.mp4 9.8 MB
  • 2. Setting up the working environment/2. Why Python and why Jupyter.mp4 9.8 MB
  • 11. LGD model/7. LGD model - combining stage 1 and stage 2.mp4 9.7 MB
  • 6. PD model estimation/3. PD model estimation.mp4 9.5 MB
  • 2. Setting up the working environment/5. Jupyter Dashboard - Part 2.mp4 9.3 MB
  • 11. LGD model/1. LGD model - preparing the inputs.mp4 9.2 MB
  • 11. LGD model/4. LGD model - saving the model.mp4 8.2 MB
  • 2. Setting up the working environment/3. Installing Anaconda.mp4 8.2 MB
  • 8. Applying the PD model for decision making/4. From credit score to PD.mp4 7.7 MB
  • 2. Setting up the working environment/4. Jupyter Dashboard - Part 1.mp4 4.3 MB
  • 2. Setting up the working environment/6. Installing the sklearn package.mp4 3.5 MB
  • 2. Setting up the working environment/1. Setting up the environment - Do not skip, please!.mp4 2.0 MB
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