train and test xgboost in python

main.py
import xgboost as xgb
from sklearn.model_selection import train_test_split
from sklearn.datasets import load_breast_cancer

# Load the breast cancer dataset
data = load_breast_cancer()
X, y = data.data, data.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create an XGBoost model
model = xgb.XGBClassifier()

# Train the model on the training set
model.fit(X_train, y_train)

# Test the model on the testing set
y_pred = model.predict(X_test)

# Evaluate the model's accuracy
accuracy = sum(y_test == y_pred) / len(y_test)
print("Accuracy:", accuracy)
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