<<< From Data to Dollars - Predicting Insurance Charges

Abstract:

The rising complexity and cost of healthcare highlight the need for data-driven strategies to enhance financial planning and service delivery in the insurance sector. Predictive analytics, empowered by machine learning, offers a valuable approach to forecasting healthcare expenses, enabling insurers to tailor services and assist customers in making informed decisions.

This study focuses on developing a predictive model for healthcare costs using the insurance.csv dataset, which contains demographic, lifestyle, and health-related attributes of insurance beneficiaries. Key features include age, gender, body mass index (BMI), number of dependents, smoking status, and residential region, with individual medical charges serving as the target variable. After necessary preprocessing and cleaning, the dataset is used to train machine learning models capable of identifying patterns and estimating healthcare costs. Model performance is further evaluated using a validation dataset (validation_dataset.csv), which omits the cost variable to simulate real-world prediction scenarios.

By leveraging predictive analytics, this work provides actionable insights for healthcare insurers, supporting personalized service offerings and proactive financial planning. The outcomes demonstrate the potential of machine learning to transform healthcare cost prediction, improving both customer experience and operational efficiency within the insurance industry.

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