• Skip to primary navigation
  • Skip to main content
  • Skip to primary sidebar
  • Skip to footer
Finance Train

Finance Train

High Quality tutorials for finance, risk, data science

  • Home
  • Data Science
  • CFA® Exam
  • PRM Exam
  • Tutorials
  • Careers
  • Products
  • Login

Loan Data – Training and Test Data Sets

Data Science, Risk Management

This lesson is part 14 of 28 in the course Credit Risk Modelling in R

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. If we had multi-year data, we could have used data for some years as training data and other years as testing data. Our data is for the same period (2016 Q1). We will use a simple approach to randomly divide the dataset into training and test set.

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.

# Sample Indexes
> indexes = sample(1:nrow(loandata), size=0.3*nrow(loandata))
>
# Split data
> data_test = loandata[indexes,]
> dim(data_test)
[1] 17960   145
 
> data_train = loandata[-indexes,]
> dim(data_train)
[1] 41909   145
>

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

> rm(loandata)

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.

Previous Lesson

‹ Credit Risk Modelling – Required R Packages

Next Lesson

Data Cleaning in R – Part 1 ›

Join Our Facebook Group - Finance, Risk and Data Science

Posts You May Like

How to Improve your Financial Health

CFA® Exam Overview and Guidelines (Updated for 2021)

Changing Themes (Look and Feel) in ggplot2 in R

Coordinates in ggplot2 in R

Facets for ggplot2 Charts in R (Faceting Layer)

Reader Interactions

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Primary Sidebar

In this Course

  • 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

Latest Tutorials

    • Data Visualization with R
    • Derivatives with R
    • Machine Learning in Finance Using Python
    • Credit Risk Modelling in R
    • Quantitative Trading Strategies in R
    • Financial Time Series Analysis in R
    • VaR Mapping
    • Option Valuation
    • Financial Reporting Standards
    • Fraud
Facebook Group

Membership

Unlock full access to Finance Train and see the entire library of member-only content and resources.

Subscribe

Footer

Recent Posts

  • How to Improve your Financial Health
  • CFA® Exam Overview and Guidelines (Updated for 2021)
  • Changing Themes (Look and Feel) in ggplot2 in R
  • Coordinates in ggplot2 in R
  • Facets for ggplot2 Charts in R (Faceting Layer)

Products

  • Level I Authority for CFA® Exam
  • CFA Level I Practice Questions
  • CFA Level I Mock Exam
  • Level II Question Bank for CFA® Exam
  • PRM Exam 1 Practice Question Bank
  • All Products

Quick Links

  • Privacy Policy
  • Contact Us

CFA Institute does not endorse, promote or warrant the accuracy or quality of Finance Train. CFA® and Chartered Financial Analyst® are registered trademarks owned by CFA Institute.

Copyright © 2021 Finance Train. All rights reserved.

  • About Us
  • Privacy Policy
  • Contact Us