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# Model Card: Gradient Boosting
## Model Description
This model is a Gradient Boosting Classifier trained to predict tourism package purchases.
It has been tuned using GridSearchCV on a SMOTE-resampled training dataset to address class imbalance.
## Tuned Parameters
- **Parameters**: {'learning_rate': 0.2, 'max_depth': 7, 'n_estimators': 300}
## Evaluation Metrics (on Test Set)
* Accuracy: 0.9467
* Precision: 0.9389
* Recall: 0.7736
* F1-Score: 0.8483
* ROC AUC: 0.9771
## Model Purpose
This model aims to accurately predict whether a customer will take a tourism package, aiding in targeted marketing efforts and improving sales efficiency.
## Usage
To use this model, load the `gradient_boosting_model.pkl` file and apply it to preprocessed data. Ensure the data undergoes the same preprocessing steps (column removal, outlier handling, one-hot encoding, and StandardScaler scaling) as the training data.
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