Travel insurance is a vital aspect of travel planning that provides travelers with financial protection against unforeseen events that may occur during a trip. These events could include medical emergencies, flight cancellations, lost or stolen luggage, and other travel-related mishaps. With the rise in global travel, the demand for travel insurance has grown exponentially, with travellers looking for the most cost-effective and comprehensive coverage.

This predictive model will utilize machine learning, data mining, and statistical analysis techniques to identify patterns and trends in the data. The model will examine historical data on past travel insurance purchases, demographic information, and travel itinerary to predict the likelihood of an individual purchasing travel insurance. In this project, we have some database history.of the customer as a dataset. The target variable of this dataset is the customer will buy travel insurance or not. The goal of this project is to create a predictive model that can accurately predict the likelihood of an individual purchasing travel insurance based on various factors like Age, Income, Number of Family members etc.