There are several ways to split data into training and testing sets in Python, but one popular method is to use the train_test_split() function from the Scikit-learn library. Here’s an example of how to use train_test_split() to split a dataset into training and testing sets:
from sklearn.model_selection import train_test_split
import pandas as pd
# Load the dataset into a pandas dataframe
df = pd.read_csv('data.csv')
# Split the dataset into features (X) and labels (y)
X = df.drop('label', axis=1)
y = df['label']
# Split the dataset into training and testing sets, with 80% of the data for training and 20% for testing
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
In this example, train_test_split() is used to split the dataset into training and testing sets, with 80% of the data for training and 20% for testing. The feature data (X) and label data (y) are passed as arguments to the function, and the resulting training and testing sets are stored in separate variables (X_train, X_test, y_train, and y_test). The random_state argument is used to ensure that the split is reproducible.