Tourism Package Prediction Model
Model Overview
This repository contains the best performing machine learning model for predicting customer purchases of the Wellness Tourism Package.
Best Model Details
- Model Type: XGBoost
- Best Hyperparameters:
1{
2 "xgboost__colsample_bytree": 0.8,
3 "xgboost__learning_rate": 0.2,
4 "xgboost__max_depth": 7,
5 "xgboost__n_estimators": 100
6}
Performance Metrics (Test Set)
- Accuracy: 0.9443
- Precision: 0.8742
- Recall: 0.8424
- F1-Score: 0.8580
Features Used
The model was trained on preprocessed customer and interaction data, including (but not limited to) features such as Age, TypeofContact, CityTier, Occupation, Gender, NumberOfPersonVisiting, PreferredPropertyStar, MaritalStatus, NumberOfTrips, Passport, OwnCar, NumberOfChildrenVisiting, Designation, MonthlyIncome, PitchSatisfactionScore, ProductPitched, NumberOfFollowups, and DurationOfPitch.
Usage
To use this model for inference:
1import joblib
2import pandas as pd
3
4# Load the model
5model = joblib.load('best_tourism_package_model.joblib')
6
7# Prepare your input data (ensure it matches the training data format)
8# Example (replace with actual data structure after preprocessing):
9# new_data = pd.DataFrame(...)
10
11# Make a prediction
12# prediction = model.predict(new_data)
13# print(prediction)
