VishalLoharkar/wellness-tourism-predictor

🏖️ Wellness Tourism Package Predictor

Model Description

This is a machine learning model that predicts whether a customer will purchase the Wellness Tourism Package. The model helps "Visit with Us" travel company to identify potential customers for targeted marketing campaigns.

Model Type: XGBoost Task: Binary Classification (Purchase/No Purchase) Framework: Scikit-learn

Intended Use

• Predict customer purchase likelihood for Wellness Tourism Package
• Targeted marketing campaigns
• Customer segmentation and personalization
• Sales optimization

Training Data

• Dataset: VishalLoharkar/tourism-wellness-dataset
• Training Samples: Not specified')
• Target Variable: ProdTaken (0 = No Purchase, 1 = Purchase)

How to Use

Installation

pip install scikit-learn pandas joblib
import joblib
import pandas as pd

# Download and load the model
model = joblib.load('model.pkl')

# Prepare your data (should match training format)
# Example features dictionary
customer_data = {
    'Age': 35,
    'TypeofContact': 1,
    'CityTier': 1,
    'Occupation': 2,
    'Gender': 1,
    'NumberOfPersonVisiting': 2,
    'PreferredPropertyStar': 4,
    'MaritalStatus': 1,
    'NumberOfTrips': 3,
    'Passport': 1,
    'OwnCar': 1,
    'NumberOfChildrenVisiting': 0,
    'Designation': 2,
    'MonthlyIncome': 75000,
    'PitchSatisfactionScore': 4,
    'ProductPitched': 2,
    'NumberOfFollowups': 3,
    'DurationOfPitch': 30
}

# Convert to DataFrame
df = pd.DataFrame([customer_data])

# Make prediction
prediction = model.predict(df)
probability = model.predict_proba(df)

print(f"Will purchase: {'Yes' if prediction[0] == 1 else 'No'}")
print(f"Confidence: {probability[0][1]:.2%}")```


Built as part of the "Visit with Us" MLOps Project