This project will analyze health insurance data to evaluate the effectiveness of predictive models in aligning insurance costs with individual health risks. The goal is to refine risk assessments and premium strategies by examining demographic trends, health risk categories, and lifestyle impacts on policy costs. This effort aims to uncover key factors that affect insurance success and customer satisfaction, ultimately leading to more equitable health insurance practices.
.jpg)
This project contains a detailed analysis of a bank's marketing campaign data. The primary objective of this analysis is to understand the effectiveness of different marketing strategies and to provide data-driven recommendations for future campaigns. Through a comprehensive examination of demographic trends, response rates by job category, and the impact of marital status on conversion rates, I aim to identify key factors that influence the success of marketing efforts.
In the healthcare sector, particularly in oncology, there is significant concern over the effectiveness of cancer detection methods. Early and accurate cancer detection significantly improves treatment outcomes, yet many current models fail to reliably predict the presence of cancer, leading to delayed or incorrect treatments. This project aims to predict the likelihood of new patients developing cancer, based on their health features