# Credit Risk Modeling in Python

A complete data science case study: preprocessing, modeling, model validation and maintenance in Python

4.5/5

## Credit Risk Modeling in Python

### What you’ll learn

Differentiate your data science portfolio with a hot topic
Fill up your resume with in demand data science skills
Build a complete credit risk model in Python
Impress interviewers by showing practical knowledge
How to preprocess real data in Python
Learn credit risk modeling theory
Apply state of the art data science techniques
Solve a real-life data science task
Be able to evaluate the effectiveness of your model
Perform linear and logistic regressions in Python

### Requirements

No prior experience is required. We will start from the very basics
You’ll need to install Anaconda and Python. We will show you how to do that step by step

### Overview

Section 1: Introduction

Lecture 1 What does the course cover

Lecture 2 What is credit risk and why is it important?

Lecture 3 Expected loss (EL) and its components: PD, LGD and EAD

Lecture 4 Capital adequacy, regulations, and the Basel II accord

Lecture 5 Basel II approaches: SA, F-IRB, and A-IRB

Lecture 6 Different facility types (asset classes) and credit risk modeling approaches

Section 2: Setting up the working environment

Lecture 7 Setting up the environment – Do not skip, please!

Lecture 8 Why Python and why Jupyter

Lecture 9 Installing Anaconda

Lecture 10 Jupyter Dashboard – Part 1

Lecture 11 Jupyter Dashboard – Part 2

Lecture 12 Installing the sklearn package

Section 3: Dataset description

Lecture 13 Our example: consumer loans. A first look at the dataset

Lecture 14 Dependent variables and independent variables

Section 4: General preprocessing

Lecture 15 Importing the data into Python

Lecture 16 Preprocessing few continuous variables

Lecture 17 Preprocessing few continuous variables: Homework

Lecture 18 Preprocessing few discrete variables

Lecture 19 Check for missing values and clean

Lecture 20 Check for missing values and clean: Homework

Section 5: PD Model: Data Preparation

Lecture 21 How is the PD model going to look like?

Lecture 22 Dependent variable: Good/ Bad (default) definition

Lecture 23 Fine classing, weight of evidence, and coarse classing

Lecture 24 Information value

Lecture 25 Data preparation. Splitting data

Lecture 26 Data preparation. An example

Lecture 27 Data preparation. Preprocessing discrete variables: automating calculations

Lecture 28 Data preparation. Preprocessing discrete variables: visualizing results

Lecture 29 Data preparation. Preprocessing discrete variables: creating dummies (Part 1)

Lecture 30 Data preparation. Preprocessing discrete variables: creating dummies (Part 2)

Lecture 31 Data preparation. Preprocessing discrete variables. Homework.

Lecture 32 Data preparation. Preprocessing continuous variables: Automating calculations

Lecture 33 Data preparation. Preprocessing continuous variables: creating dummies (Part 1)

Lecture 34 Data preparation. Preprocessing continuous variables: creating dummies (Part 2)

Lecture 35 Data preparation. Preprocessing continuous variables: creating dummies. Homework

Lecture 36 Data preparation. Preprocessing continuous variables: creating dummies (Part 3)

Lecture 37 Data preparation. Preprocessing continuous variables: creating dummies. Homework

Lecture 38 Data preparation. Preprocessing the test dataset

Lecture 39 PD model: data preparation notebooks

Section 6: PD model estimation

Lecture 40 The PD model. Logistic regression with dummy variables

Lecture 42 PD model estimation

Lecture 43 Build a logistic regression model with p-values

Lecture 44 Interpreting the coefficients in the PD model

Section 7: PD model validation

Lecture 45 Out-of-sample validation (test)

Lecture 46 Evaluation of model performance: accuracy and area under the curve (AUC)

Lecture 47 Evaluation of model performance: Gini and Kolmogorov-Smirnov

Section 8: Applying the PD Model for decision making

Lecture 48 Calculating probability of default for a single customer

Lecture 49 Creating a scorecard

Lecture 50 Calculating credit score

Lecture 51 From credit score to PD

Lecture 52 Setting cut-offs

Lecture 53 Setting cut-offs. Homework

Lecture 54 PD model: logistic regression notebooks

Section 9: PD model monitoring

Lecture 55 PD model monitoring via assessing population stability

Lecture 56 Population stability index: preprocessing

Lecture 57 Population stability index: calculation and interpretation

Lecture 58 Homework: building an updated PD model

Section 10: LGD and EAD Models: Preparing the data

Lecture 59 LGD and EAD models: independent variables.

Lecture 60 LGD and EAD models: dependent variables

Lecture 61 LGD and EAD models: distribution of recovery rates and credit conversion factors

Section 11: LGD model

Lecture 62 LGD model: preparing the inputs

Lecture 63 LGD model: testing the model

Lecture 64 LGD model: estimating the accuracy of the model

Lecture 65 LGD model: saving the model

Lecture 66 LGD model: stage 2 – linear regression

Lecture 67 LGD model: stage 2 – linear regression evaluation

Lecture 68 LGD model: combining stage 1 and stage 2

Lecture 69 Homework: building an updated LGD model

Lecture 70 EAD model estimation and interpretation

Lecture 72 Homework: building an updated EAD model

Section 13: Calculating expected loss

Lecture 73 Calculating expected loss

Lecture 74 Homework: calculate expected loss on more recent data

Lecture 75 Completing 100%

You should take this course if you are a data science student interested in improving their skills,You should take this course if you want to specialize in credit risk modeling,The course is also ideal for beginners, as it starts from the fundamentals and gradually builds up your skills,This course is for you if you want a great career

#### Course Information:

Udemy | English | 6h 51m | 3.50 GB
Created by: 365 Careers

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