How to do it...

The following steps demonstrate how to take a dataset, consisting of features X and labels y, and split these into a training and testing subset:

1. Start by importing the train_test_split module and the pandas library, and read your features into X and labels into y:

from sklearn.model_selection import train_test_split
import pandas as pd

df = pd.read_csv("north_korea_missile_test_database.csv")
y = df["Missile Name"]
X = df.drop("Missile Name", axis=1)

1. Next, randomly split the dataset and its labels into a training set consisting 80% of the size of the original dataset and a testing set 20% of the size:

X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.2, random_state=31
)

1.  We apply the train_test_split method once more, to obtain a validation set, X_val and y_val:

X_train, X_val, y_train, y_val = train_test_split(
    X_train, y_train, test_size=0.25, random_state=31
)

1. We end up with a training set that's 60% of the size of the original data, a validation set of 20%, and a testing set of 20%.

The following screenshot shows the output: