Implementing XGBoost in Python.py

import xgboost as xgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# load the Boston housing dataset
boston = load_boston()
X, y = boston.data, boston.target

# split the data 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)

# define the model parameters
params = {
    'objective': 'reg:squarederror',
    'learning_rate': 0.1,
    'max_depth': 3,
    'n_estimators': 100,
    'subsample': 0.8,
    'colsample_bytree': 0.8,
    'reg_alpha': 0.1,
    'reg_lambda': 0.1
}

# train the model
model = xgb.XGBRegressor(**params)
model.fit(X_train, y_train)

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

# calculate the mean squared error
mse = mean_squared_error(y_test, y_pred)
print("Mean squared error: %.2f" % mse)

Great example of xgboost on the boston housing dataset!