- Credit Risk Modelling - Case Studies
- Classification vs. Regression Models
- Case Study - German Credit - Steps to Build a Predictive Model
- Import Credit Data Set in R
- German Credit Data : Data Preprocessing and Feature Selection in R
- 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 Data
- Explore Loan Data in R - Loan Grade and Interest Rate
- 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 Multiple Models
- Predictive Modelling: Comparing Model Results
- How Insurance Companies Calculate Risk

R Programming

## Credit Modelling: Training and Test Data Sets

For building the model, we will divide our data into two different data sets, namely training and testing datasets. The model will be built using the training set and then we will test it on the testing set to evaluate how our model is performing.

There are many ways in which we can split the data.

We can use the “sample” command to randomly select certain index numbers and then use the selected index numbers to divide the dataset into training and testing dataset. Below is the code for doing this. In the code below we use 30% of the data for testing and rest of the 70% for training.

```
# Sample Indexes
> indexes = sample(1:nrow(creditdata), size=0.3*nrow(creditdata))
# Split data
> credit_test = creditdata_new[indexes,]
> credit_train = creditdata_new[-indexes,]
> dim(credit_test)
[1] 300 18
> dim(credit_train)
[1] 700 18
>
```

### Other Ways to Split Data

- We can use the rpart function of the rpart package to split the data. RPART stands for Recursive Partitioning And Regression Trees. The
*rpart*algorithm works by splitting the dataset recursively, which means that the subsets that arise from a split are further split until a predetermined termination criterion is reached. It allows you to construct splitting rules in many different ways. - We can also use the createDataPartition function of the caret package to split the data set

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