hyperparametertuning.py

# Import necessary libraries
from sklearn.model_selection import GridSearchCV
from sklearn.ensemble import RandomForestClassifier
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Create a dataset
X, y = make_classification(n_samples=500, n_features=10, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Define a model and hyperparameter grid
model = RandomForestClassifier(random_state=42)
param_grid = {
    'n_estimators': [10, 50, 100],
    'max_depth': [5, 10, 20],
    'min_samples_split': [2, 5, 10]
}

# Perform Grid Search
grid_search = GridSearchCV(estimator=model, param_grid=param_grid, cv=5, scoring='accuracy')
grid_search.fit(X_train, y_train)

# Print best parameters and accuracy
print("Best Parameters:", grid_search.best_params_)
print("Best Score:", grid_search.best_score_)

# Evaluate the model on the test set
best_model = grid_search.best_estimator_
test_accuracy = best_model.score(X_test, y_test)
print("Test Accuracy:", test_accuracy)