evaluate.py

import pandas as pd
import joblib
import os
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, roc_auc_score

# Define paths for model and data
MODEL_PATH = 'tourism_project/best_random_forest_model.joblib'
X_TEST_PATH = 'tourism_project/data/X_test.csv'
Y_TEST_PATH = 'tourism_project/data/y_test.csv'

def evaluate_model():
    print("Loading model and test data...")
    model = joblib.load(MODEL_PATH)
    X_test = pd.read_csv(X_TEST_PATH)
    y_test = pd.read_csv(Y_TEST_PATH)

    # Make predictions
    print("Making predictions on test data...")
    y_pred = model.predict(X_test)

    # Calculate metrics
    accuracy = accuracy_score(y_test, y_pred)
    precision = precision_score(y_test, y_pred)
    recall = recall_score(y_test, y_pred)
    f1 = f1_score(y_test, y_pred)
    roc_auc = roc_auc_score(y_test, y_pred)

    print(f"
Model Evaluation Results:")
    print(f"Accuracy: {accuracy:.4f}")
    print(f"Precision: {precision:.4f}")
    print(f"Recall: {recall:.4f}")
    print(f"F1-Score: {f1:.4f}")
    print(f"ROC AUC Score: {roc_auc:.4f}")

if __name__ == '__main__':
    evaluate_model()