wellness-tourism-prediction-model

deepakdm411/wellness-tourism-prediction-model

wellness-tourism-prediction-model is a machine learning model from deepakdm411. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.

Model: Bagging ROC-AUC: 0.9823 Accuracy: 0.9334

Downloads · 30 days

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Public

Updated Dec 13, 2025

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32.5 MB

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Public

Hugging Face

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.pkl931 KB · 99%

At a glance

License: mit
Access: Public
Created: Dec 13, 2025
Updated: Dec 13, 2025
SHA: 70910781

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deepakdm411/wellness-tourism-predictor

Model card

From the Hugging Face model README

Wellness Tourism Prediction Model

Model: Bagging
ROC-AUC: 0.9823
Accuracy: 0.9334

Performance

Model	Test Accuracy	Precision	Recall	F1 Score	ROC-AUC	Training Time (s)
Decision Tree	0.883777	0.748031	0.597484	0.664336	0.837789	2.09308
Random Forest	0.918886	0.933962	0.622642	0.74717	0.97365	1.64203
AdaBoost	0.837772	0.658228	0.327044	0.436975	0.829821	0.648075
Gradient Boosting	0.935835	0.920635	0.72956	0.814035	0.954966	3.43388
XGBoost	0.917676	0.90991	0.63522	0.748148	0.94399	0.253888
Bagging	0.933414	0.933333	0.704403	0.802867	0.982301	1.63008

Usage

import joblib
import pandas as pd

model = joblib.load('best_model.pkl')
scaler = joblib.load('scaler.pkl')

# Load feature metadata
import json
with open('feature_metadata.json') as f:
    metadata = json.load(f)

# Your input must have these features in this exact order:
# ['Age', 'TypeofContact', 'CityTier', 'Occupation', 'Gender', 'NumberOfPersonVisiting', 'PreferredPropertyStar', 'MaritalStatus', 'NumberOfTrips', 'Passport', 'OwnCar', 'NumberOfChildrenVisiting', 'Designation', 'MonthlyIncome', 'PitchSatisfactionScore', 'ProductPitched', 'NumberOfFollowups', 'DurationOfPitch', 'AgeGroup', 'IncomeGroup']

License: mit
Languages: en
Created: Dec 13, 2025
Updated: Dec 13, 2025