Mastering Python for Finance - Second Edition

Splitting and scaling the data

Before feeding the dataset into our model, we have to prepare it in a proper format. The following steps guide you through the process:

1. Split the dataset into independent and target variables:

In [ ]:    feature_columns= df.columns[:-1]    features = df.loc[:, feature_columns]    target = df.loc[:, 'default payment next month']

Our target values in the last column of the dataset are assigned to the target variable, while remaining values are feature values and are assigned to the features variable.

2. Split the dataset into training data and testing data:

In [ ]:    from sklearn.model_selection import train_test_split     train_features, test_features, train_target, test_target = \ train_test_split(features, target, test_size=0.20, ...