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dash-binayak92/tourism-propensity-best-model

sourceHugging Faceupdated 8mo agoView on Hugging Face

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tag: ['tabular-classification', 'scikit-learn'] something: else: 'with metadata' ---

Best Model for Tourism Propensity Prediction

This repository contains the best-performing model for predicting tourism propensity, selected based on its F1-score on the test set.

Model Details

—Model Name: Gradient Boosting Classifier
—Best Parameters: {'learningrate': 0.2, 'maxdepth': 7, 'n_estimators': 200}

Test Set Evaluation Metrics:

—Accuracy: 0.9492
—Precision: 0.9362
—Recall: 0.8000
—F1-Score: 0.8627

Usage (Example with Python):

python

import joblib
from huggingface_hub import HfApi

# Download the model (assuming you have the `hf_hub_download` utility)
# from huggingface_hub import hf_hub_download
# model_path = hf_hub_download(repo_id="dash-binayak92/tourism-propensity-best-model", filename="tourism_propensity_best_model.joblib")

# Load the model
best_model = joblib.load('tourism_propensity_best_model.joblib') # or use model_path if downloaded

# Make predictions (example X_test needs to be in the same format as training data)
# y_pred = best_model.predict(X_test)
# print(y_pred)