updated everything

output/Gradient Boosting_classification_report.txt

@@ -2,9 +2,9 @@ Gradient Boosting Classification Report:
               precision    recall  f1-score   support
 
            0       0.93      0.91      0.92      1152
-           1       0.78      0.82      0.80       432
+           1       0.77      0.81      0.79       432
 
-    accuracy                           0.89      1584
+    accuracy                           0.88      1584
    macro avg       0.85      0.86      0.86      1584
-weighted avg       0.89      0.89      0.89      1584
+weighted avg       0.89      0.88      0.89      1584
 

output/Random Forest_classification_report.txt

@@ -1,10 +1,10 @@
 Random Forest Classification Report:
               precision    recall  f1-score   support
 
-           0       0.93      0.93      0.93      1152
-           1       0.81      0.82      0.81       432
+           0       0.93      0.92      0.92      1152
+           1       0.79      0.81      0.80       432
 
-    accuracy                           0.90      1584
-   macro avg       0.87      0.87      0.87      1584
-weighted avg       0.90      0.90      0.90      1584
+    accuracy                           0.89      1584
+   macro avg       0.86      0.87      0.86      1584
+weighted avg       0.89      0.89      0.89      1584
 

sentiment_analysis.py

@@ -74,5 +74,6 @@
 best_pipeline.fit(df['tweet'], df['label'])
 
 
-joblib.dump(best_pipeline, os.path.join(output_dir, 'best_model.pkl'))
+
+joblib.dump(best_pipeline, 'best_model.pkl')
 print(f'The best model is {best_model_name} with accuracy {results[best_model_name]}')