deepakdm411/wellness-tourism-prediction-model

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']