Create model_evaluation.py

src/ml/model_evaluation.py

@@ -0,0 +1,44 @@
+# src/ml/model_evaluation.py
+
+import joblib
+import pandas as pd
+from sklearn.metrics import mean_squared_error, r2_score
+
+class ModelEvaluator:
+    """Class to evaluate machine learning models."""
+
+    def __init__(self, model_path='models'):
+        self.model_path = model_path
+        self.model = joblib.load(f'{self.model_path}/model.pkl')
+        self.scaler = joblib.load(f'{self.model_path}/scaler.pkl')
+
+    def load_data(self, file_path):
+        """Load dataset from a CSV file."""
+        data = pd.read_csv(file_path)
+        return data
+
+    def preprocess_data(self, data):
+        """Preprocess the data for evaluation."""
+        X = data.drop('target', axis=1)  # Assuming 'target' is the label column
+        y = data['target']
+        X_scaled = self.scaler.transform(X)
+        return X_scaled, y
+
+    def evaluate_model(self, X_test, y_test):
+        """Evaluate the model and return metrics."""
+        predictions = self.model.predict(X_test)
+        mse = mean_squared_error(y_test, predictions)
+        r2 = r2_score(y_test, predictions)
+        return mse, r2
+
+    def run(self, data_file):
+        """Load data, preprocess, and evaluate the model."""
+        data = self.load_data(data_file)
+        X_test, y_test = self.preprocess_data(data)
+        mse, r2 = self .evaluate_model(X_test, y_test)
+        print(f'Mean Squared Error: {mse:.2f}')
+        print(f'R2 Score: {r2:.2f}')
+
+if __name__ == "__main__":
+    evaluator = ModelEvaluator()
+    evaluator.run('data/testing_data.csv')  # Path to your testing data