This project explains on how to build a machine learning algorithm for calculating the medical insurance costs. Check out my video on this topic for the complete video explanation.
Predicting the medical costs charged by health insurers
Testing Hypothesis on Medical Insurance dataset to analyze & gain insights using Normality Test, T-Test, Chi-Square Test etc. Along with Power Analysis to check the statistical significance.
The goal of this project is to develop a predictive model that can accurately estimate the medical insurance premium for potential policyholders. By accurately predicting premiums, the insurance company can provide more precise quotes to customers, leading to better customer satisfaction and improved financial planning for the company.
🏥 Health Insurance Cost Prediction – A Machine Learning model that predicts health insurance costs based on factors like age, BMI, smoking habits, and pre-existing conditions. Built with Regression models, Scikit-Learn, Pandas, and Python.
Predicting medical insurance costs using machine learning in Python.
Medical Insurance Payout
In this project, I embarked on an exploratory journey through a dataset of U.S. medical insurance costs, aiming to uncover insights into the factors that influence these costs.
Project to analyze and forecast medical insurance costs of patients using data science framework.
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