from sklearn.model_selection import RandomizedSearchCV
from sklearn.ensemble import RandomForestClassifier
from scipy.stats import randint

param_dist = {
    'n_estimators': randint(100, 500),
    'max_depth': randint(10, 50),
    'min_samples_split': randint(2, 11)
}

random_search = RandomizedSearchCV(estimator=RandomForestClassifier(), param_distributions=param_dist, n_iter=100, cv=3, n_jobs=-1, verbose=2)
random_search.fit(X_train, y_train)

print("Best Hyperparameters:", random_search.best_params_)

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