Having completed the EDA, the next step is to split the dataset into training and test sets. The idea is to have two separate datasets:

• Training set—On this part of the data, we train a machine learning model
• Test set—This part of the data was not seen by the model during training, and is used to evaluate the performance

What we want to achieve by splitting the data is preventing overfitting. Overfitting is a phenomenon whereby a model finds too many patterns in data used for training and performs well only on that particular data. In other words, it fails to generalize to unseen data.

This is a very important step in the analysis, as doing it incorrectly can introduce bias, for example, in the form of data leakage. Data leakage can occur when, during the training phase, a model observes information to which it should not have access. We follow up with an example. A common scenario is that of imputing missing values with the feature's average. If we had done this before splitting the data, we would have also used data from the test set to calculate the average, introducing data leakage. That is why the proper order would be to split the data into training and test sets first and then carry out the imputation, using the data observed in the training set.

Additionally, this approach ensures consistency, as unseen data in the future (new customers that will be scored by the model) will be treated in the same way as the ones data in the test set.