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predict.py

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+import tensorflow as tf
+import pandas as pd
+import numpy as np
+import sklearn
+import sklearn.model_selection
+from tensorflow.keras.models import load_model
+from sklearn.metrics import accuracy_score, precision_score, f1_score
+
+feature_cols = ['year', 'mileage', 'vol_engine']
+
+model = load_model('model.h5')
+test_data = pd.read_csv('test.csv')
+
+predictions = model.predict(test_data[feature_cols])
+predicted_prices = [p[0] for p in predictions]
+
+
+results = pd.DataFrame({'id': test_data['id'], 'year': test_data['year'], 'mileage': test_data['mileage'], 'vol_engine': test_data['vol_engine'], 'predicted_price': predicted_prices})
+results.to_csv('predictions.csv', index=False)
+
+y_true = test_data['price']
+y_pred = y_pred = [round(p[0]) for p in predictions]
+
+accuracy = accuracy_score(y_true, y_pred)
+precision = precision_score(y_true, y_pred, average='micro')
+f1 = f1_score(y_true, y_pred, average='micro')
+
+with open('metrics.txt', 'w') as f:
+    f.write(f"Accuracy: {accuracy:.4f}\n")
+    f.write(f"Micro-average Precision: {precision:.4f}\n")
+    f.write(f"Micro-average F1-score: {f1:.4f}\n")