optimize a parameter in a data set in python

main.py
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import GridSearchCV
from sklearn.datasets import load_boston

# load boston housing data
boston = load_boston()

# create linear regression model
model = LinearRegression()

# define parameter grid for alpha
param_grid = {'alpha': [0.1, 0.5, 1, 5]}

# perform grid search using cross-validation
grid_search = GridSearchCV(model, param_grid, cv=5)

# fit the model to the data
grid_search.fit(boston.data, boston.target)

# print the best alpha value and corresponding score
print("Best alpha:", grid_search.best_params_['alpha'])
print("Score:", grid_search.best_score_)
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