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
finding statistical significance for the hypothesis raised for medical insurance costs.
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.
Analysis of Pathway Health’s marketing campaigns (2019–2023) to evaluate awareness, acquisition, and claim risk. Includes a Tableau dashboard and slide deck showing insights and recommendations to guide 2024 marketing budget allocation.
Predict annual medical insurance costs instantly using Machine Learning! This AI system analyzes age, BMI, smoking habits, and demographics to estimate healthcare expenses with 90% accuracy. Built with Linear Regression, featuring 8+ visualizations and comprehensive data analysis.
Predicting medical insurance costs using machine learning in Python.
Project to analyze and forecast medical insurance costs of patients using data science framework.
🏥 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.
AI-powered medical insurance cost prediction using advanced regression models. Features a comprehensive ML pipeline and an interactive Jupyter Notebook dashboard.
A Streamlit web app that predicts medical insurance costs using Machine Learning models like Linear Regression, Random Forest, and XGBoost.
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.
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