English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 75 lectures (6h 51m) | 2.19 GB

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

Hi! Welcome to Credit Risk Modeling in Python. The only online course that teaches you how banks use data science modeling in Python to improve their performance and comply with regulatory requirements. This is the perfect course for you, if you are interested in a data science career. Here’s why:

The instructor is a proven expert (PhD from the Norwegian Business school, who has taught in world renowned universities such as HEC, the University of Texas, and the Norwegian Business school).

The course is suitable for beginners. We start with theory and initial data pre-processing and gradually solve a complete exercise in front of you

Everything we cover is up-to-date and relevant in today’s development of Python models for the banking industry

This is the only online course that shows the complete picture in credit risk in Python (using state of the art techniques to model all three aspects of the expected loss equation – PD, LGD, and EAD) including creating a scorecard from scratch

Here we show you how to create models that are compliant with Basel II and Basel III regulations that other courses rarely touch upon

We are not going to work with fake data. The dataset used in this course is an actual real-world example

You get to differentiate your data science portfolio by showing skills that are highly demanded in the job marketplace

What is most important – you get to see first-hand how a data science task is solved in the real-world

Most data science courses cover several frameworks, but skip the pre-processing and theoretical part. This is like learning how to taste wine before being able to open a bottle of wine.

We don’t do that. Our goal is to help you build a solid foundation. We want you to study the theory, learn how to pre-process data that does not necessarily come in the ‘’friendliest’’ format, and of course, only then we will show you how to build a state of the art model and how to evaluate its effectiveness.

Throughout the course, we will cover several important data science techniques.

- Weight of evidence
- Information value
- Fine classing
- Coarse classing
- Linear regression
- Logistic regression
- Area Under the Curve
- Receiver Operating Characteristic Curve
- Gini Coefficient
- Kolmogorov-Smirnov
- Assessing Population Stability
- Maintaining a model

What you’ll learn

- Improve your Python modeling skills
- 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

## Table of Contents

**Introduction**

What does the course cover

What is credit risk and why is it important

Expected loss EL and its components PD LGD and EAD

What is credit risk and why is it important

Capital adequacy regulations and the Basel II accord

Expected loss EL and its components PD LGD and EAD

Basel II approaches SA FIRB and AIRB

Capital adequacy regulations and the Basel II accord

Basel II approaches SA F

Different facility types asset classes and credit risk modeling approaches

Different facility types asset classes and credit risk modeling approaches

**Setting up the working environment**

Setting up the environment

Why Python and why Jupyter

Installing Anaconda

Jupyter Dashboard

Jupyter Dashboard

Installing the sklearn package

**Dataset description**

Our example consumer loans A first look at the dataset

Dependent variables and independent variables

Our example consumer loans A first look at the dataset

Dependent variables and independent variables

**General preprocessing**

Importing the data into Python

Preprocessing few continuous variables

Preprocessing few discrete variables

Check for missing values and clean

Importing the data into Python

Preprocessing few continuous variables

Preprocessing few continuous variables Homework

Preprocessing few discrete variables

Check for missing values and clean

Check for missing values and clean Homework

**PD Model Data Preparation**

How is the PD model going to look like

Dependent variable Good Bad default definition

Fine classing weight of evidence and coarse classing

Information value

Data preparation Splitting data

Data preparation An example

Data preparation Preprocessing discrete variables automating calculations

Data preparation Preprocessing discrete variables creating dummies Part 1

Data preparation Preprocessing discrete variables creating dummies Part 2

Data preparation Preprocessing continuous variables Automating calculations

How is the PD model going to look like

Data preparation Preprocessing continuous variables creating dummies Part 1

Dependent variable Good Bad default definition

Data preparation Preprocessing continuous variables creating dummies Part 2

Fine classing weight of evidence and coarse classing

Data preparation Preprocessing continuous variables creating dummies Part 3

Information value

Data preparation Splitting data

Data preparation An example

Data preparation Preprocessing discrete variables automating calculations

Data preparation Preprocessing discrete variables visualizing results

Data preparation Preprocessing discrete variables creating dummies Part 1

Data preparation Preprocessing discrete variables creating dummies Part 2

Data preparation Preprocessing discrete variables Homework

Data preparation Preprocessing continuous variables Automating calculations

Data preparation Preprocessing continuous variables creating dummies Part 1

Data preparation Preprocessing continuous variables creating dummies Part 2

Data preparation Preprocessing continuous variables creating dummies Homework

Data preparation Preprocessing continuous variables creating dummies Part 3

Data preparation Preprocessing continuous variables creating dummies Homework

Data preparation Preprocessing the test dataset

PD model data preparation notebooks

**PD model estimation**

The PD model Logistic regression with dummy variables

Build a logistic regression model with pvalues

Interpreting the coefficients in the PD model

The PD model Logistic regression with dummy variables

Loading the data and selecting the features

PD model estimation

Build a logistic regression model with p

Interpreting the coefficients in the PD model

**PD model validation**

Outofsample validation test

Evaluation of model performance accuracy and area under the curve AUC

Evaluation of model performance Gini and KolmogorovSmirnov

Out

Evaluation of model performance accuracy and area under the curve AUC

Evaluation of model performance Gini and Kolmogorov

**Applying the PD Model for decision making**

Creating a scorecard

Calculating credit score

From credit score to PD

Setting cutoffs

Calculating probability of default for a single customer

Creating a scorecard

Calculating credit score

From credit score to PD

Setting cut

Setting cutoffs Homework

PD model logistic regression notebooks

**PD model monitoring**

PD model monitoring via assessing population stability

Population stability index calculation and interpretation

PD model monitoring via assessing population stability

Population stability index preprocessing

Population stability index calculation and interpretation

Homework building an updated PD model

**LGD and EAD Models Preparing the data**

LGD and EAD models independent variables

LGD and EAD models dependent variables

LGD and EAD models distribution of recovery rates and credit conversion factors

LGD and EAD models independent variables

LGD and EAD models dependent variables

LGD and EAD models distribution of recovery rates and credit conversion factors

**LGD model**

LGD model testing the model

LGD model stage 2 linear regression with comments

LGD model stage 2 linear regression evaluation

LGD model combining stage 1 and stage 2

LGD model preparing the inputs

LGD model testing the model

LGD model estimating the accuracy of the model

LGD model saving the model

LGD model stage 2 linear regression

LGD model stage 2 linear regression evaluation

LGD model combining stage 1 and stage 2

Homework building an updated LGD model

**EAD model**

EAD model estimation and interpretation

EAD model validation

EAD model estimation and interpretation

EAD model validation

Homework building an updated EAD model

**Calculating expected loss**

Calculating expected loss

Calculating expected loss

Homework calculate expected loss on more recent data

Completing 100

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