589689.xyz

[] Udemy - Credit Risk Modeling in Python 2020

  • 收录时间:2020-04-09 21:44:26
  • 文件大小:3GB
  • 下载次数:50
  • 最近下载:2021-01-16 23:18:52
  • 磁力链接:
立即下载 复制链接

文件列表

  1. 13. Calculating expected loss/1. Calculating expected loss.mp4 127MB
  2. 5. PD Model Data Preparation/25. Data preparation. Preprocessing continuous variables creating dummies (Part 2).mp4 112MB
  3. 9. PD model monitoring/3. Population stability index preprocessing.mp4 105MB
  4. 1. Introduction/10. Different facility types (asset classes) and credit risk modeling approaches.mp4 104MB
  5. 6. PD model estimation/5. Build a logistic regression model with p-values.mp4 102MB
  6. 1. Introduction/8. Basel II approaches SA, F-IRB, and A-IRB.mp4 102MB
  7. 5. PD Model Data Preparation/28. Data preparation. Preprocessing continuous variables creating dummies (Part 3).mp4 101MB
  8. 8. Applying the PD Model for decision making/2. Creating a scorecard.mp4 97MB
  9. 5. PD Model Data Preparation/18. Data preparation. Preprocessing discrete variables creating dummies (Part 2).mp4 93MB
  10. 9. PD model monitoring/4. Population stability index calculation and interpretation.mp4 92MB
  11. 4. General preprocessing/3. Preprocessing few continuous variables.mp4 84MB
  12. 8. Applying the PD Model for decision making/8. Setting cut-offs.mp4 76MB
  13. 7. PD model validation/3. Evaluation of model performance accuracy and area under the curve (AUC).mp4 76MB
  14. 1. Introduction/1. What does the course cover.mp4 73MB
  15. 7. PD model validation/5. Evaluation of model performance Gini and Kolmogorov-Smirnov.mp4 70MB
  16. 5. PD Model Data Preparation/15. Data preparation. Preprocessing discrete variables visualizing results.mp4 66MB
  17. 3. Dataset description/3. Dependent variables and independent variables.mp4 66MB
  18. 6. PD model estimation/1. The PD model. Logistic regression with dummy variables.mp4 61MB
  19. 5. PD Model Data Preparation/9. Data preparation. Splitting data.mp4 59MB
  20. 1. Introduction/2. What is credit risk and why is it important.mp4 58MB
  21. 5. PD Model Data Preparation/5. Fine classing, weight of evidence, and coarse classing.mp4 55MB
  22. 7. PD model validation/1. Out-of-sample validation (test).mp4 52MB
  23. 1. Introduction/6. Capital adequacy, regulations, and the Basel II accord.mp4 51MB
  24. 10. LGD and EAD Models Preparing the data/1. LGD and EAD models independent variables..mp4 50MB
  25. 5. PD Model Data Preparation/11. Data preparation. An example.mp4 50MB
  26. 5. PD Model Data Preparation/16. Data preparation. Preprocessing discrete variables creating dummies (Part 1).mp4 50MB
  27. 12. EAD model/1. EAD model estimation and interpretation.mp4 48MB
  28. 1. Introduction/4. Expected loss (EL) and its components PD, LGD and EAD.mp4 48MB
  29. 4. General preprocessing/6. Preprocessing few discrete variables.mp4 46MB
  30. 5. PD Model Data Preparation/21. Data preparation. Preprocessing continuous variables Automating calculations.mp4 45MB
  31. 5. PD Model Data Preparation/7. Information value.mp4 45MB
  32. 5. PD Model Data Preparation/23. Data preparation. Preprocessing continuous variables creating dummies (Part 1).mp4 44MB
  33. 5. PD Model Data Preparation/13. Data preparation. Preprocessing discrete variables automating calculations.mp4 44MB
  34. 6. PD model estimation/3. Loading the data and selecting the features.mp4 43MB
  35. 11. LGD model/2. LGD model testing the model.mp4 43MB
  36. 8. Applying the PD Model for decision making/4. Calculating credit score.mp4 41MB
  37. 10. LGD and EAD Models Preparing the data/3. LGD and EAD models dependent variables.mp4 40MB
  38. 10. LGD and EAD Models Preparing the data/5. LGD and EAD models distribution of recovery rates and credit conversion factors.mp4 40MB
  39. 8. Applying the PD Model for decision making/1. Calculating probability of default for a single customer.mp4 40MB
  40. 9. PD model monitoring/1. PD model monitoring via assessing population stability.mp4 39MB
  41. 5. PD Model Data Preparation/3. Dependent variable Good Bad (default) definition.mp4 39MB
  42. 5. PD Model Data Preparation/1. How is the PD model going to look like.mp4 38MB
  43. 3. Dataset description/1. Our example consumer loans. A first look at the dataset.mp4 37MB
  44. 11. LGD model/6. LGD model stage 2 – linear regression.mp4 36MB
  45. 6. PD model estimation/7. Interpreting the coefficients in the PD model.mp4 35MB
  46. 11. LGD model/4. LGD model estimating the accuracy of the model.mp4 35MB
  47. 4. General preprocessing/1. Importing the data into Python.mp4 33MB
  48. 5. PD Model Data Preparation/31. Data preparation. Preprocessing the test dataset.mp4 30MB
  49. 12. EAD model/3. EAD model validation.mp4 30MB
  50. 2. Setting up the working environment/3. Installing Anaconda.mp4 29MB
  51. 2. Setting up the working environment/2. Why Python and why Jupyter.mp4 29MB
  52. 11. LGD model/8. LGD model stage 2 – linear regression evaluation.mp4 27MB
  53. 4. General preprocessing/8. Check for missing values and clean.mp4 25MB
  54. 6. PD model estimation/4. PD model estimation.mp4 25MB
  55. 11. LGD model/1. LGD model preparing the inputs.mp4 24MB
  56. 11. LGD model/10. LGD model combining stage 1 and stage 2.mp4 24MB
  57. 2. Setting up the working environment/5. Jupyter Dashboard - Part 2.mp4 24MB
  58. 11. LGD model/5. LGD model saving the model.mp4 24MB
  59. 8. Applying the PD Model for decision making/6. From credit score to PD.mp4 23MB
  60. 2. Setting up the working environment/4. Jupyter Dashboard - Part 1.mp4 12MB
  61. 2. Setting up the working environment/6. Installing the sklearn package.mp4 10MB
  62. 2. Setting up the working environment/1. Setting up the environment - Do not skip, please!.mp4 6MB
  63. 2. Setting up the working environment/5.1 Shortcuts-for-Jupyter.pdf 619KB
  64. 13. Calculating expected loss/1. Calculating expected loss.srt 20KB
  65. 3. Dataset description/1.1 LCDataDictionary.xlsx 20KB
  66. 5. PD Model Data Preparation/25. Data preparation. Preprocessing continuous variables creating dummies (Part 2).srt 19KB
  67. 4. General preprocessing/3. Preprocessing few continuous variables.srt 17KB
  68. 5. PD Model Data Preparation/28. Data preparation. Preprocessing continuous variables creating dummies (Part 3).srt 17KB
  69. 8. Applying the PD Model for decision making/2. Creating a scorecard.srt 17KB
  70. 5. PD Model Data Preparation/18. Data preparation. Preprocessing discrete variables creating dummies (Part 2).srt 15KB
  71. 9. PD model monitoring/3. Population stability index preprocessing.srt 15KB
  72. 6. PD model estimation/5. Build a logistic regression model with p-values.srt 14KB
  73. 7. PD model validation/3. Evaluation of model performance accuracy and area under the curve (AUC).srt 14KB
  74. 9. PD model monitoring/4. Population stability index calculation and interpretation.srt 14KB
  75. 7. PD model validation/5. Evaluation of model performance Gini and Kolmogorov-Smirnov.srt 13KB
  76. 5. PD Model Data Preparation/15. Data preparation. Preprocessing discrete variables visualizing results.srt 13KB
  77. 1. Introduction/8. Basel II approaches SA, F-IRB, and A-IRB.srt 13KB
  78. 1. Introduction/10. Different facility types (asset classes) and credit risk modeling approaches.srt 12KB
  79. 5. PD Model Data Preparation/9. Data preparation. Splitting data.srt 12KB
  80. 8. Applying the PD Model for decision making/8. Setting cut-offs.srt 11KB
  81. 5. PD Model Data Preparation/11. Data preparation. An example.srt 11KB
  82. 6. PD model estimation/1. The PD model. Logistic regression with dummy variables.srt 11KB
  83. 5. PD Model Data Preparation/23. Data preparation. Preprocessing continuous variables creating dummies (Part 1).srt 10KB
  84. 5. PD Model Data Preparation/16. Data preparation. Preprocessing discrete variables creating dummies (Part 1).srt 10KB
  85. 4. General preprocessing/6. Preprocessing few discrete variables.srt 9KB
  86. 7. PD model validation/1. Out-of-sample validation (test).srt 9KB
  87. 5. PD Model Data Preparation/5. Fine classing, weight of evidence, and coarse classing.srt 9KB
  88. 10. LGD and EAD Models Preparing the data/1. LGD and EAD models independent variables..srt 8KB
  89. 12. EAD model/1. EAD model estimation and interpretation.srt 8KB
  90. 3. Dataset description/3. Dependent variables and independent variables.srt 8KB
  91. 6. PD model estimation/7. Interpreting the coefficients in the PD model.srt 8KB
  92. 1. Introduction/1. What does the course cover.srt 8KB
  93. 5. PD Model Data Preparation/13. Data preparation. Preprocessing discrete variables automating calculations.srt 8KB
  94. 10. LGD and EAD Models Preparing the data/5. LGD and EAD models distribution of recovery rates and credit conversion factors.srt 8KB
  95. 8. Applying the PD Model for decision making/4. Calculating credit score.srt 8KB
  96. 6. PD model estimation/3. Loading the data and selecting the features.srt 7KB
  97. 5. PD Model Data Preparation/3. Dependent variable Good Bad (default) definition.srt 7KB
  98. 10. LGD and EAD Models Preparing the data/3. LGD and EAD models dependent variables.srt 7KB
  99. 9. PD model monitoring/1. PD model monitoring via assessing population stability.srt 7KB
  100. 5. PD Model Data Preparation/7. Information value.srt 7KB
  101. 11. LGD model/2. LGD model testing the model.srt 7KB
  102. 2. Setting up the working environment/5. Jupyter Dashboard - Part 2.srt 7KB
  103. 5. PD Model Data Preparation/21. Data preparation. Preprocessing continuous variables Automating calculations.srt 7KB
  104. 2. Setting up the working environment/2. Why Python and why Jupyter.srt 6KB
  105. 1. Introduction/2. What is credit risk and why is it important.srt 6KB
  106. 11. LGD model/4. LGD model estimating the accuracy of the model.srt 6KB
  107. 1. Introduction/6. Capital adequacy, regulations, and the Basel II accord.srt 6KB
  108. 12. EAD model/3. EAD model validation.srt 6KB
  109. 4. General preprocessing/1. Importing the data into Python.srt 6KB
  110. 8. Applying the PD Model for decision making/1. Calculating probability of default for a single customer.srt 6KB
  111. 5. PD Model Data Preparation/31. Data preparation. Preprocessing the test dataset.srt 6KB
  112. 5. PD Model Data Preparation/1. How is the PD model going to look like.srt 5KB
  113. 11. LGD model/6. LGD model stage 2 – linear regression.srt 5KB
  114. 1. Introduction/4. Expected loss (EL) and its components PD, LGD and EAD.srt 5KB
  115. 6. PD model estimation/4. PD model estimation.srt 5KB
  116. 11. LGD model/8. LGD model stage 2 – linear regression evaluation.srt 5KB
  117. 4. General preprocessing/8. Check for missing values and clean.srt 5KB
  118. 2. Setting up the working environment/3. Installing Anaconda.srt 5KB
  119. 11. LGD model/1. LGD model preparing the inputs.srt 4KB
  120. 11. LGD model/10. LGD model combining stage 1 and stage 2.srt 4KB
  121. 8. Applying the PD Model for decision making/6. From credit score to PD.srt 4KB
  122. 11. LGD model/5. LGD model saving the model.srt 4KB
  123. 3. Dataset description/1. Our example consumer loans. A first look at the dataset.srt 4KB
  124. 2. Setting up the working environment/4. Jupyter Dashboard - Part 1.srt 3KB
  125. 2. Setting up the working environment/6. Installing the sklearn package.srt 2KB
  126. 5. PD Model Data Preparation/27. Data preparation. Preprocessing continuous variables creating dummies. Homework.html 2KB
  127. 11. LGD model/12. Homework building an updated LGD model.html 1KB
  128. 5. PD Model Data Preparation/30. Data preparation. Preprocessing continuous variables creating dummies. Homework.html 1KB
  129. 2. Setting up the working environment/1. Setting up the environment - Do not skip, please!.srt 1KB
  130. 5. PD Model Data Preparation/20. Data preparation. Preprocessing discrete variables. Homework..html 1KB
  131. 13. Calculating expected loss/3. Homework calculate expected loss on more recent data.html 974B
  132. 8. Applying the PD Model for decision making/10. Setting cut-offs. Homework.html 957B
  133. 4. General preprocessing/5. Preprocessing few continuous variables Homework.html 919B
  134. 12. EAD model/5. Homework building an updated EAD model.html 875B
  135. 9. PD model monitoring/6. Homework building an updated PD model.html 820B
  136. 4. General preprocessing/10. Check for missing values and clean Homework.html 668B
  137. 13. Calculating expected loss/1.1 Calculating expected loss with comments.html 207B
  138. 13. Calculating expected loss/3.2 Calculating expected loss complete notebook with comments.html 207B
  139. 10. LGD and EAD Models Preparing the data/1.3 LGD and EAD models independent variables with comments.html 202B
  140. 10. LGD and EAD Models Preparing the data/3.2 LGD and EAD models dependent variables with comments.html 202B
  141. 10. LGD and EAD Models Preparing the data/5.2 LGD and EAD models distribution of recovery rates and credit conversion factors with comments.html 202B
  142. 11. LGD model/1.2 LGD model preparing the inputs with comments.html 202B
  143. 11. LGD model/10.2 LGD model combining stage 1 and stage 2 with comments.html 202B
  144. 11. LGD model/2.1 LGD model testing the model with comments.html 202B
  145. 11. LGD model/4.1 LGD model estimating the accuracy of the model with comments.html 202B
  146. 11. LGD model/5.1 LGD model saving the model with comments.html 202B
  147. 11. LGD model/6.2 LGD model stage 2 – linear regression with comments.html 202B
  148. 11. LGD model/8.1 LGD model stage 2 – linear regression evaluation with comments.html 202B
  149. 12. EAD model/1.1 EAD model estimation and interpretation with comments.html 202B
  150. 12. EAD model/3.1 EAD model validation with comments.html 202B
  151. 5. PD Model Data Preparation/18.1 Data preparation. Preprocessing discrete variables creating dummies (Part 2) with comments.html 189B
  152. 5. PD Model Data Preparation/20.1 Data preparation. Preprocessing discrete variables. Homework with comments.html 189B
  153. 5. PD Model Data Preparation/21.1 Data preparation. Preprocessing continuous variables Automating calculations with comments.html 189B
  154. 5. PD Model Data Preparation/23.2 Data preparation. Preprocessing continuous variables creating dummies (Part 1) with comments.html 189B
  155. 5. PD Model Data Preparation/25.2 Data preparation. Preprocessing continuous variables creating dummies (Part 2) with comments.html 189B
  156. 5. PD Model Data Preparation/27.2 Data preparation. Preprocessing continuous variables creating dummies. Homework with comments.html 189B
  157. 5. PD Model Data Preparation/28.2 Data preparation. Preprocessing continuous variables creating dummies (Part 3) with comments.html 189B
  158. 5. PD Model Data Preparation/30.1 Data preparation. Preprocessing continuous variables creating dummies. Homework with comments.html 189B
  159. 5. PD Model Data Preparation/31.1 Data preparation. Preprocessing the test dataset with comments.html 189B
  160. 3. Dataset description/1.3 Data preparation with comments.html 188B
  161. 4. General preprocessing/1.1 Importing the data into Python with comments.html 188B
  162. 4. General preprocessing/10.1 Check for missing values and clean the data Homework - Solution with comments.html 188B
  163. 4. General preprocessing/3.2 Preprocessing few continuous variables with comments.html 188B
  164. 4. General preprocessing/5.1 Preprocessing few continuous variables Homework - Solution with comments.html 188B
  165. 4. General preprocessing/6.1 Preprocessing few discrete variables with comments.html 188B
  166. 4. General preprocessing/8.2 Check for missing values and clean with comments.html 188B
  167. 5. PD Model Data Preparation/11.1 Data preparation. An example with comments.html 188B
  168. 5. PD Model Data Preparation/13.1 Data preparation. Preprocessing discrete variables automating calculations with comments.html 188B
  169. 5. PD Model Data Preparation/15.1 Data preparation. Preprocessing discrete variables visualizing results with comments.html 188B
  170. 5. PD Model Data Preparation/16.1 Data preparation. Preprocessing discrete variables creating dummies (Part 1) with comments.html 188B
  171. 5. PD Model Data Preparation/3.1 Dependent variable GoodBad with comments.html 188B
  172. 5. PD Model Data Preparation/9.2 Data preparation. Splitting data with comments.html 188B
  173. 6. PD model estimation/3.1 Loading the data and selecting the features with comments.html 187B
  174. 6. PD model estimation/4.1 PD model estimation with comments.html 187B
  175. 6. PD model estimation/5.1 Build a logistic regression model with p-values with comments.html 187B
  176. 7. PD model validation/1.2 Out-of-sample validation (test) with comments.html 187B
  177. 7. PD model validation/3.2 Evaluation of model performance accuracy and area under the curve (AUC) with comments.html 187B
  178. 7. PD model validation/5.2 Evaluation of model performance Gini and Kolmogorov-Smirnov with comments.html 187B
  179. 8. Applying the PD Model for decision making/1.1 Calculating probability of default for a single customer with comments.html 187B
  180. 8. Applying the PD Model for decision making/2.2 Creating a scorecard with comments.html 187B
  181. 8. Applying the PD Model for decision making/4.2 Calculating credit score with comments.html 187B
  182. 8. Applying the PD Model for decision making/6.2 From credit score to PD with comments.html 187B
  183. 8. Applying the PD Model for decision making/8.1 Setting cut-offs with comments.html 187B
  184. 13. Calculating expected loss/1.2 Calculating expected loss.html 185B
  185. 13. Calculating expected loss/3.1 Calculating expected loss complete notebook.html 185B
  186. 10. LGD and EAD Models Preparing the data/1.2 LGD and EAD models independent variables..html 180B
  187. 10. LGD and EAD Models Preparing the data/3.1 LGD and EAD models dependent variables.html 180B
  188. 10. LGD and EAD Models Preparing the data/5.1 LGD and EAD models distribution of recovery rates and credit conversion factors.html 180B
  189. 11. LGD model/1.3 LGD model preparing the inputs.html 180B
  190. 11. LGD model/10.1 LGD model combining stage 1 and stage 2.html 180B
  191. 11. LGD model/2.2 LGD model testing the model.html 180B
  192. 11. LGD model/4.2 LGD model estimating the accuracy of the model.html 180B
  193. 11. LGD model/5.2 LGD model saving the model.html 180B
  194. 11. LGD model/6.1 LGD model stage 2 – linear regression.html 180B
  195. 11. LGD model/8.2 LGD model stage 2 – linear regression evaluation.html 180B
  196. 12. EAD model/1.2 EAD model estimation and interpretation.html 180B
  197. 12. EAD model/3.2 EAD model validation.html 180B
  198. 5. PD Model Data Preparation/32.1 PD model data preparation with comments.html 178B
  199. 8. Applying the PD Model for decision making/11.2 PD model complete with comments.html 177B
  200. 9. PD model monitoring/4.1 Monitoring with comments.html 177B
  201. 5. PD Model Data Preparation/18.2 Data preparation. Preprocessing discrete variables creating dummies (Part 2).html 167B
  202. 5. PD Model Data Preparation/20.2 Data preparation. Preprocessing discrete variables Homework - Soluton.html 167B
  203. 5. PD Model Data Preparation/21.2 Data preparation. Preprocessing continuous variables Automating calculations.html 167B
  204. 5. PD Model Data Preparation/23.1 Data preparation. Preprocessing continuous variables creating dummies (Part 1).html 167B
  205. 5. PD Model Data Preparation/25.1 Data preparation. Preprocessing continuous variables creating dummies (Part 2).html 167B
  206. 5. PD Model Data Preparation/27.1 Data preparation. Preprocessing continuous variables creating dummies. Homework.html 167B
  207. 5. PD Model Data Preparation/28.1 Data preparation. Preprocessing continuous variables creating dummies (Part 3).html 167B
  208. 5. PD Model Data Preparation/30.2 Data preparation. Preprocessing continuous variables creating dummies Homework - Solution.html 167B
  209. 5. PD Model Data Preparation/31.2 Data preparation. Preprocessing the test dataset.html 167B
  210. 3. Dataset description/1.2 Data Preparation.html 166B
  211. 4. General preprocessing/1.2 Importing the data into Python.html 166B
  212. 4. General preprocessing/10.2 Check for missing values and clean the data Homework - Solution.html 166B
  213. 4. General preprocessing/3.1 Preprocessing few continuous variables.html 166B
  214. 4. General preprocessing/5.2 Preprocessing few continuous variables Homework - Solution.html 166B
  215. 4. General preprocessing/6.2 Preprocessing few discrete variables.html 166B
  216. 4. General preprocessing/8.1 Check for missing values and clean.html 166B
  217. 5. PD Model Data Preparation/11.2 Data preparation. An example.html 166B
  218. 5. PD Model Data Preparation/13.2 Data preparation. Preprocessing discrete variables automating calculations.html 166B
  219. 5. PD Model Data Preparation/15.2 Data preparation. Preprocessing discrete variables visualizing results.html 166B
  220. 5. PD Model Data Preparation/16.2 Data preparation. Preprocessing discrete variables creating dummies (Part 1).html 166B
  221. 5. PD Model Data Preparation/3.2 Dependent variable GoodBad.html 166B
  222. 5. PD Model Data Preparation/9.1 Data preparation. Splitting data.html 166B
  223. 6. PD model estimation/3.2 Loading the data and selecting the features.html 165B
  224. 6. PD model estimation/4.2 PD model estimation.html 165B
  225. 6. PD model estimation/5.2 Build a logistic regression model with p-values.html 165B
  226. 7. PD model validation/1.1 Out-of-sample validation (test).html 165B
  227. 7. PD model validation/3.1 Evaluation of model performance accuracy and area under the curve (AUC).html 165B
  228. 7. PD model validation/5.1 Evaluation of model performance Gini and Kolmogorov-Smirnov.html 165B
  229. 8. Applying the PD Model for decision making/1.2 Calculating probability of default for a single customer.html 165B
  230. 8. Applying the PD Model for decision making/2.1 Creating a scorecard.html 165B
  231. 8. Applying the PD Model for decision making/4.1 Calculating credit score.html 165B
  232. 8. Applying the PD Model for decision making/6.1 From credit score to PD.html 165B
  233. 8. Applying the PD Model for decision making/8.2 Setting cut-offs.html 165B
  234. 5. PD Model Data Preparation/32.2 PD model data preparation.html 156B
  235. 8. Applying the PD Model for decision making/11.1 PD model complete.html 155B
  236. 9. PD model monitoring/4.2 Monitoring.html 155B
  237. 10. LGD and EAD Models Preparing the data/1.1 loan_data_2007_2014_preprocessed.csv.html 144B
  238. 11. LGD model/1.1 loan_data_2007_2014_preprocessed.csv.html 144B
  239. 1. Introduction/11. Different facility types (asset classes) and credit risk modeling approaches.html 141B
  240. 1. Introduction/3. What is credit risk and why is it important.html 141B
  241. 1. Introduction/5. Expected loss (EL) and its components PD, LGD and EAD.html 141B
  242. 1. Introduction/7. Capital adequacy, regulations, and the Basel II accord.html 141B
  243. 1. Introduction/9. Basel II approaches SA, F-IRB, and A-IRB.html 141B
  244. 10. LGD and EAD Models Preparing the data/2. LGD and EAD models independent variables.html 141B
  245. 10. LGD and EAD Models Preparing the data/4. LGD and EAD models dependent variables.html 141B
  246. 10. LGD and EAD Models Preparing the data/6. LGD and EAD models distribution of recovery rates and credit conversion factors.html 141B
  247. 11. LGD model/11. LGD model combining stage 1 and stage 2.html 141B
  248. 11. LGD model/3. LGD model testing the model.html 141B
  249. 11. LGD model/7. LGD model stage 2 – linear regression with comments.html 141B
  250. 11. LGD model/9. LGD model stage 2 – linear regression evaluation.html 141B
  251. 12. EAD model/2. EAD model estimation and interpretation.html 141B
  252. 12. EAD model/4. EAD model validation.html 141B
  253. 13. Calculating expected loss/2. Calculating expected loss.html 141B
  254. 3. Dataset description/2. Our example consumer loans. A first look at the dataset.html 141B
  255. 3. Dataset description/4. Dependent variables and independent variables.html 141B
  256. 4. General preprocessing/2. Importing the data into Python.html 141B
  257. 4. General preprocessing/4. Preprocessing few continuous variables.html 141B
  258. 4. General preprocessing/7. Preprocessing few discrete variables.html 141B
  259. 4. General preprocessing/9. Check for missing values and clean.html 141B
  260. 5. PD Model Data Preparation/10. Data preparation. Splitting data.html 141B
  261. 5. PD Model Data Preparation/12. Data preparation. An example.html 141B
  262. 5. PD Model Data Preparation/14. Data preparation. Preprocessing discrete variables automating calculations.html 141B
  263. 5. PD Model Data Preparation/17. Data preparation. Preprocessing discrete variables creating dummies (Part 1).html 141B
  264. 5. PD Model Data Preparation/19. Data preparation. Preprocessing discrete variables creating dummies (Part 2).html 141B
  265. 5. PD Model Data Preparation/2. How is the PD model going to look like.html 141B
  266. 5. PD Model Data Preparation/22. Data preparation. Preprocessing continuous variables Automating calculations.html 141B
  267. 5. PD Model Data Preparation/24. Data preparation. Preprocessing continuous variables creating dummies (Part 1).html 141B
  268. 5. PD Model Data Preparation/26. Data preparation. Preprocessing continuous variables creating dummies (Part 2).html 141B
  269. 5. PD Model Data Preparation/29. Data preparation. Preprocessing continuous variables creating dummies (Part 3).html 141B
  270. 5. PD Model Data Preparation/4. Dependent variable Good Bad (default) definition.html 141B
  271. 5. PD Model Data Preparation/6. Fine classing, weight of evidence, and coarse classing.html 141B
  272. 5. PD Model Data Preparation/8. Information value.html 141B
  273. 6. PD model estimation/2. The PD model. Logistic regression with dummy variables.html 141B
  274. 6. PD model estimation/6. Build a logistic regression model with p-values.html 141B
  275. 6. PD model estimation/8. Interpreting the coefficients in the PD model.html 141B
  276. 7. PD model validation/2. Out-of-sample validation (test).html 141B
  277. 7. PD model validation/4. Evaluation of model performance accuracy and area under the curve (AUC).html 141B
  278. 7. PD model validation/6. Evaluation of model performance Gini and Kolmogorov-Smirnov.html 141B
  279. 8. Applying the PD Model for decision making/3. Creating a scorecard.html 141B
  280. 8. Applying the PD Model for decision making/5. Calculating credit score.html 141B
  281. 8. Applying the PD Model for decision making/7. From credit score to PD.html 141B
  282. 8. Applying the PD Model for decision making/9. Setting cut-offs.html 141B
  283. 9. PD model monitoring/2. PD model monitoring via assessing population stability.html 141B
  284. 9. PD model monitoring/5. Population stability index calculation and interpretation.html 141B
  285. 3. Dataset description/1.4 Dataset for the course.html 131B
  286. 3. Dataset description/3.1 Dataset for the course.html 131B
  287. 11. LGD model/12.1 Dataset with new data (loan_data_2015.csv).html 126B
  288. 9. PD model monitoring/6.1 Dataset with new data (loan_data_2015.csv).html 126B
  289. 5. PD Model Data Preparation/32. PD model data preparation notebooks.html 85B
  290. 8. Applying the PD Model for decision making/11. PD model logistic regression notebooks.html 73B
  291. 0. Websites you may like/[FreeCourseWorld.Com].url 54B
  292. [FreeCourseWorld.Com].url 54B
  293. 0. Websites you may like/[DesireCourse.Net].url 51B
  294. [DesireCourse.Net].url 51B
  295. 0. Websites you may like/[CourseClub.Me].url 48B
  296. [CourseClub.Me].url 48B