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

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28-06-2021 12:20 (edited 28-06-2021 12:20) by moviesbazar89

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File	Size
!! IMPORTANT Note !!.txt	298.00 B
!!! Please Support !!! [CoursesGhar.Com].txt	197.00 B
00. Websites You May Like/A1movies.com.pk.url	116.00 B
00. Websites You May Like/CoursesGhar.com.url	114.00 B
1. Introduction/1. What is credit risk and why is it important.mp4	55.10 MB
1. Introduction/2. Expected loss (EL) and its components - PD, LGD and EAD.mp4	46.02 MB
1. Introduction/3. Capital adequacy, regulations, and the Basel II accord.mp4	15.45 MB
1. Introduction/4. Basel II approaches - SA, F-IRB, and A-IRB.mp4	29.70 MB
1. Introduction/5. Different facility types (asset classes) and credit risk modeling approaches.mp4	32.21 MB
10. LGD and EAD models/1. LGD and EAD models - independent variables.mp4	17.98 MB
10. LGD and EAD models/2. LGD and EAD models - dependent variables.mp4	13.95 MB
10. LGD and EAD models/3. LGD and EAD models - distribution of recovery rates and credit conversion factors.mp4	14.00 MB
11. LGD model/1. LGD model - preparing the inputs.mp4	8.77 MB
11. LGD model/2. LGD model - testing the model.mp4	16.87 MB
11. LGD model/3. LGD model - estimating the accuracy of the model.mp4	12.33 MB
11. LGD model/4. LGD model - saving the model.mp4	7.86 MB
11. LGD model/5. LGD model - stage 2 – linear regression.mp4	14.70 MB
11. LGD model/6. LGD model - stage 2 – linear regression evaluation.mp4	9.64 MB
11. LGD model/7. LGD model - combining stage 1 and stage 2.mp4	9.25 MB
12. EAD model/1. EAD model estimation and interpretation.mp4	17.64 MB
12. EAD model/2. EAD model validation.mp4	10.37 MB
13. Calculating expected loss/1. Calculating expected loss.mp4	45.58 MB
2. Setting up the working environment/1. Setting up the environment - Do not skip, please!.mp4	1.88 MB
2. Setting up the working environment/2. Why Python and why Jupyter.mp4	9.38 MB
2. Setting up the working environment/3. Installing Anaconda.mp4	7.83 MB
2. Setting up the working environment/4. Jupyter Dashboard - Part 1.mp4	4.11 MB
2. Setting up the working environment/5. Jupyter Dashboard - Part 2.mp4	8.84 MB
2. Setting up the working environment/6. Installing the sklearn package.mp4	3.35 MB
3. Dataset description/1. Our example - consumer loans. A first look at the dataset.mp4	14.03 MB
3. Dataset description/2. Dependent variables and independent variables.mp4	19.14 MB
4. General preprocessing/1. Importing the data into Python.mp4	16.10 MB
4. General preprocessing/2. Preprocessing few continuous variables.mp4	29.68 MB
4. General preprocessing/3. Preprocessing few discrete variables.mp4	18.84 MB
4. General preprocessing/4. Check for missing values and clean.mp4	9.38 MB
5. PD model_ data preparation/1. How is the PD model going to look like.mp4	11.96 MB
5. PD model_ data preparation/10. Data preparation. Preprocessing discrete variables - creating dummies (Part 2).mp4	34.83 MB
5. PD model_ data preparation/11. Data preparation. Preprocessing continuous variables - automating calculations.mp4	19.45 MB
5. PD model_ data preparation/12. Data preparation. Preprocessing continuous variables - creating dummies (Part 1).mp4	14.48 MB
5. PD model_ data preparation/13. Data preparation. Preprocessing continuous variables - creating dummies (Part 2).mp4	38.02 MB
5. PD model_ data preparation/14. Data preparation. Preprocessing continuous variables - creating dummies (Part 3).mp4	36.09 MB
5. PD model_ data preparation/15. Data preparation. Preprocessing the test dataset.mp4	11.72 MB
5. PD model_ data preparation/2. Dependent variable - Good_ Bad (default) definition.mp4	12.31 MB
5. PD model_ data preparation/3. Fine classing, weight of evidence, and coarse classing.mp4	15.69 MB
5. PD model_ data preparation/4. Information value.mp4	12.84 MB
5. PD model_ data preparation/5. Data preparation. Splitting data.mp4	20.32 MB
5. PD model_ data preparation/6. Data preparation. An example.mp4	17.53 MB
5. PD model_ data preparation/7. Data preparation. Preprocessing discrete variables - automating calculations.mp4	16.23 MB
5. PD model_ data preparation/8. Data preparation. Preprocessing discrete variables - visualizing results.mp4	22.73 MB
5. PD model_ data preparation/9. Data Preparation. Preprocessing Discrete Variables - Creating Dummies (Part 1).mp4	17.78 MB
6. PD model estimation/1. The PD model. Logistic regression with dummy variables.mp4	20.34 MB
6. PD model estimation/2. Loading the data and selecting the features.mp4	15.65 MB
6. PD model estimation/3. PD model estimation.mp4	9.02 MB
6. PD model estimation/4. Build a logistic regression model with p-values..mp4	41.76 MB
6. PD model estimation/5. Interpreting the coefficients in the PD model.mp4	11.31 MB
7. PD model validation (test)/1. Out-of-sample validation (test)..mp4	20.09 MB
7. PD model validation (test)/2. Evaluation of model performance - accuracy and area under the curve (AUC).mp4	25.15 MB
7. PD model validation (test)/3. Evaluation of model performance - Gini and Kolmogorov-Smirnov..mp4	23.26 MB
8. Applying the PD model for decision making/1. Calculating probability of default for a single customer.mp4	13.28 MB
8. Applying the PD model for decision making/2. Creating a scorecard.mp4	40.16 MB
8. Applying the PD model for decision making/3. Calculating credit score.mp4	14.72 MB
8. Applying the PD model for decision making/4. From credit score to PD.mp4	7.31 MB
8. Applying the PD model for decision making/5. Setting cut-offs.mp4	29.69 MB
9. PD model monitoring/1. PD model monitoring via assessing population stability.mp4	10.60 MB
9. PD model monitoring/2. Population stability index - preprocessing.mp4	52.55 MB
9. PD model monitoring/3. Population stability index - calculation and interpretation.mp4	32.86 MB
Join Our Telegram Group For More Updates !!!.url	138.00 B
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Visit coursesghar.com for more awesome tutorials.url	114.00 B

Description

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

Credit risk modeling is the place where data science and fintech meet. It is one of the most important activities conducted in a bank and the one with the most attention since the recession. This course is the only comprehensive credit risk modeling course in Python available right now. It shows the complete credit risk modeling picture, from preprocessing, through probability of default (PD), loss given default (LGD) and exposure at default (EAD) modeling, and finally finishing off with calculating expected loss (EL).