```html Travel Insurance Claim Prediction | Mutiara Ayu

Travel Insurance Claim Prediction

Machine learning classification project focused on predicting travel insurance claims using ensemble models and imbalance handling techniques.

Machine Learning Project

Travel Insurance Claim Prediction Using Machine Learning

Developed an end-to-end machine learning classification model to predict travel insurance claims using Python, XGBoost, and LightGBM with preprocessing, feature engineering, and imbalance handling techniques.

Python Pandas Scikit-learn XGBoost LightGBM SMOTE Machine Learning

Key Features

  • Classification Modeling
  • XGBoost & LightGBM Modeling
  • Feature Engineering
  • Hyperparameter Tuning
  • SMOTE & Class Imbalance Handling
  • Recall Optimization

Project Overview

This project focuses on predicting whether a customer will submit a travel insurance claim based on demographic, travel, and product-related features.

Multiple machine learning models were evaluated using imbalance handling techniques such as SMOTE, NearMiss, and class weighting to improve minority class prediction performance.

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