HOTEL RESERVATION CANCELLATION PREDICTION USING GUEST BOOKING PATTERN AND LEAD TIME ANALYSIS

Abstract

Vehicle insurance claim prediction is a critical task for insurance companies to manage financial
risk and improve decision-making. With the increasing number of policyholders and claims,
traditional methods of risk assessment are becoming inefficient and less accurate. This project
presents a machine learning-based approach to predict the likelihood of vehicle insurance claims
using policyholder risk attributes. The system analyzes various features such as age, driving
history, vehicle type, location, past claims, and policy duration to identify patterns associated
with claim behavior.
The proposed model utilizes advanced machine learning algorithms like Logistic Regression,
Decision Trees, Random Forest, and Gradient Boosting to improve prediction accuracy. Data
preprocessing techniques such as handling missing values, encoding categorical variables, and
feature scaling are applied to enhance model performance. Feature selection methods are used to
identify the most influential factors affecting claim likelihood.
The model is trained and tested using historical insurance data, and its performance is evaluated
using metrics such as accuracy, precision, recall, and F1-score. The system is capable of
providing real-time predictions, helping insurance companies make informed decisions regarding
policy approval, premium pricing, and fraud detection.
By automating the claim prediction process, the system reduces manual effort and minimizes
errors. It also enhances scalability, allowing the handling of large datasets efficiently. Overall,
this project demonstrates how machine learning can transform traditional insurance systems into
intelligent and data-driven solutions, improving risk assessment and operational efficiency.