Loan Data - Training and Test Data Sets

We can use the "sample" command to randomly select certain index number 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.

1# Sample Indexes
2> indexes = sample(1:nrow(loandata), size=0.3*nrow(loandata))
3>
4# Split data
5> data_test = loandata[indexes,]
6> dim(data_test)
7[1] 17960   145
8 
9> data_train = loandata[-indexes,]
10> dim(data_train)
11[1] 41909   145
12>
13

We can now remove the original loandata dataset from R to free up memory.

While building the model, we will emphasize that we avoid picking loans that can default as we don’t want to spoil our ROI. At the same time, we don’t want to pick up just a number of loans as we want to make a sizeable investment.

In the next few lessons we will focus on cleaning the dataset and then training the model.