Instructions to use Satyanjay/Tourism_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Scikit-learn
How to use Satyanjay/Tourism_Model with Scikit-learn:
from huggingface_hub import hf_hub_download import joblib model = joblib.load( hf_hub_download("Satyanjay/Tourism_Model", "sklearn_model.joblib") ) # only load pickle files from sources you trust # read more about it here https://skops.readthedocs.io/en/stable/persistence.html - Notebooks
- Google Colab
- Kaggle
| language: en | |
| tags: | |
| - machine-learning | |
| - classification | |
| - sklearn | |
| - random-forest | |
| - mlops | |
| license: mit | |
| datasets: | |
| - Satyanjay/Tourism_Project_Processed | |
| metrics: | |
| - accuracy | |
| # Tourism Purchase Prediction Model | |
| ## Overview | |
| This model predicts whether a customer will purchase the Wellness Tourism Package. | |
| ## Model Details | |
| - Algorithm: Random Forest Classifier | |
| - Framework: Scikit-learn | |
| - Experiment Tracking: MLflow | |
| ## Performance | |
| - Accuracy: 0.897 | |
| - Recall (Class 1): 0.52 | |
| ## Dataset | |
| - Satyanjay/Tourism_Project_Processed | |
| ## Usage | |
| Use huggingface_hub to download and load the model. | |