Update README.md

README.md

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+Description of StackedLogisticSolversMaxIter5000SolverSagaRandomState42 Model
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/659512081adf6d577e6b0c7a/Qm9bGKeU0_Ri94flBNsMA.png)
+
+This model, named "StackedLogisticSolversMaxIter5000SolverSagaRandomState42," is a stacked classifier that combines various machine learning techniques to predict hotel booking cancellations in the hospitality industry. The model utilizes an advanced "Stacking Model" technique, integrating different base algorithms to improve prediction accuracy.
+
+The base model consists of four classifiers: RandomForestClassifier, LogisticRegression with custom parameters (max_iter=5000, solver='saga', random_state=42), DecisionTreeClassifier, and Support Vector Classifier (SVC) with data scaling. These base models are combined into a meta-model that also uses LogisticRegression with the same parameter configuration. Additionally, data scaling is performed using StandardScaler on models that require it.
+
+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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 license: mit
 language:
@@ -11,13 +22,3 @@ tags:
 pipeline_tag: tabular-regression
 ---
 
-Description of StackedLogisticSolversMaxIter5000SolverSagaRandomState42 Model
-
-This model, named "StackedLogisticSolversMaxIter5000SolverSagaRandomState42," is a stacked classifier that combines various machine learning techniques to predict hotel booking cancellations in the hospitality industry. The model utilizes an advanced "Stacking Model" technique, integrating different base algorithms to improve prediction accuracy.
-
-The base model consists of four classifiers: RandomForestClassifier, LogisticRegression with custom parameters (max_iter=5000, solver='saga', random_state=42), DecisionTreeClassifier, and Support Vector Classifier (SVC) with data scaling. These base models are combined into a meta-model that also uses LogisticRegression with the same parameter configuration. Additionally, data scaling is performed using StandardScaler on models that require it.
-
-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.
-