Update README.md

README.md

@@ -9,6 +9,16 @@ The base model consists of four classifiers: RandomForestClassifier, LogisticReg
 The StackedLogisticSolversMaxIter5000SolverSagaRandomState42 model has been trained and fine-tuned to achieve an 89% accuracy rate in predicting hotel booking cancellations. Its primary goal is to provide data-driven insights to empower hotel management, optimize reservation systems, and enhance overall service quality.
 
 This model is designed for use in predictive analytics applications within the hotel industry and can serve as a valuable resource for making informed decisions and reducing losses due to cancellations.
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+                accuracy    recall  f1-score   support
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+           0       0.86      0.79      0.83      3607
+           1       0.90      0.94      0.92      7276
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+    accuracy                           0.89     10883
+   macro avg       0.88      0.86      0.87     10883
+weighted avg       0.89      0.89      0.89     10883
+
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 license: mit
 language: