Credit Risk Modelling - Case Studies- In this tutorial, we will learn credit risk modeling in R using case studies. Specifically, we will ...

## COURSE

## Credit Risk Modelling in R

- While building any predictive model, it is important to first understand whether it is a classificat...
- We will preform various steps in building our predictive model. These steps are explained below: ##...
- We are using the German Credit Scoring Data Set in numeric format which contains information about 2...
- The purpose of preprocessing is to make your raw data suitable for the data science algorithms. For ...
- For building the model, we will divide our data into two different data sets, namely training and te...
- We have now gathered our data and cleansed/transformed it to suit our modeling needs. The next step ...
- Logistic regression aims to model the probability of an event occurring depending on the values of i...
- R’s ROCR package can be used for evaluating and visualizing the performance of classifiers / fitted ...
- A confusion matrix is a tabular representation of Actual vs Predicted values. ![](https://financetr...
- To build a good model, it is important to use high quality data. For the purpose of this course, we ...
- There is no set path to how one would go about analyzing a data set. Typically, a data scientist wou...
- During our analysis, we will make use of various R packages. So, let’s look at what these packages a...
- For building the model, we will divide our data into two different data sets, namely training and te...
- ### Discarding Attributes LendingClub also provides a data dictionary that contains details of all ...
- ### Attributes with Zero Variance Datasets can sometimes contain attributes (predictors) that have ...
- ### Default by States We take a look at default rate for each state. We filter out states that have...
- ### Numeric Features Let’s look at all numeric features we have left. ``` > str(data_train[getNume...
- We will use the `preProcess` function from the `caret` package to center and scale (Normalize) the d...
- When we build the model, we will need the same set of columns in the test data also and will also ne...
- The loan data typically have a higher proportion of good loans. We can achieve high accuracy just by...
- To build our first model, we will tune Logistic Regression to our training dataset. First we set th...
- A support vector machine (SVM) is a supervised learning technique that analyzes data and isolates pa...
- Now, we will tune RandomForest model. Like [SVM](https://financetrain.com/support-vector-machine-svm...
- Extreme Gradient Boosting has a very efficient implementation. Unlike [SVM](https://financetrain.com...
- Our final model is to combine the result of previous machine learning models and provide a single pr...
- AUC for each model and their performance when we set probability cutoff at 50% is summarised below: ...
- People who are good at calculating probability and risk are few and far between. That is why underst...

## LESSONS

Classification vs. Regression Models

Case Study - German Credit - Steps to Build a Pred...

Import Credit Data Set in R

German Credit Data : Data Preprocessing and Featur...

Credit Modelling: Training and Test Data Sets

Build the Predictive Model

Logistic Regression Model in R

Measure Model Performance in R Using ROCR Package

Create a Confusion Matrix in R

Credit Risk Modelling - Case Study- Lending Club D...

Explore Loan Data in R - Loan Grade and Interest ...

Credit Risk Modelling - Required R Packages

Loan Data - Training and Test Data Sets

Data Cleaning in R - Part 1

Data Cleaning in R - Part 2

Data Cleaning in R - Part 3

Data Cleaning in R - Part 5

Remove Dimensions By Fitting Logistic Regression

Create a Function and Prepare Test Data in R

Building Credit Risk Model

Credit Risk - Logistic Regression Model in R

Support Vector Machine (SVM) Model in R

Random Forest Model in R

Extreme Gradient Boosting in R

Predictive Modelling: Averaging Results from Multi...

Predictive Modelling: Comparing Model Results

How Insurance Companies Calculate Risk