jskswamy/wellness-tourism-model

Wellness Tourism Package Prediction Model

This model predicts whether a customer will purchase the Wellness Tourism Package offered by "Visit with Us" travel company.

Model Description

• Model Type: GradientBoosting
• Task: Binary Classification
• Framework: scikit-learn

Intended Use

This model is designed to help travel companies identify potential customers for wellness tourism packages based on customer demographics and travel history.

Training Data

The model was trained on the Wellness Tourism Dataset containing customer information including:

• Demographics (Age, Gender, Marital Status)
• Professional details (Occupation, Designation, Monthly Income)
• Travel history (Number of Trips, Passport status)
• Pitch information (Duration, Property Star preference, Satisfaction Score)

Evaluation Results

Metric	Value
Accuracy	0.9346
F1 Score	0.8163
Precision	0.8911
Recall	0.7531
ROC AUC	0.9670

Usage

import joblib
from huggingface_hub import hf_hub_download

# Download model
model_path = hf_hub_download(
    repo_id="jskswamy/wellness-tourism-model",
    filename="wellness_tourism_model.joblib"
)

# Load and use
model = joblib.load(model_path)
predictions = model.predict(your_data)

Limitations

• Model performance may vary on data significantly different from the training distribution
• Predictions should be used as one factor among many in business decisions