Build the Predictive Model

We create the model using one of the many algorithms that best describes the relationship between the predictors and the response variable. This is also called training the model. Once the model is ready, it can be used to make the prediction for creditability given all the other features of the loan applicant/borrower.

As we have established earlier, the problem we are looking at is a binary classification problem - Creditability as Bad Credit (0) or Good Credit (1).

Below is a list of the popular algorithms used for classification problems.

1. Linear Classifiers: Logistic Regression, Naive Bayes Classifier
2. Support Vector Machines
3. Decision Trees
4. Boosted Trees
5. Random Forest
6. Neural Networks
7. Nearest Neighbor

Most often a data scientist will create many models using different algorithms and then use the best or average of all the models. In this case study, we will build the model using just one algorithm, i.e., Logistic Regression.