This project predicts health insurance premiums based on personal attributes such as age, gender, BMI, family size, and smoking habits. Using a dataset that captures the relationship between these features and medical insurance charges, the model provides insights into how these factors influence healthcare costs.
Features:
Data Analysis: Explored and visualized relationships between attributes and insurance charges.
Feature Engineering: Cleaned and processed data for optimal model performance.
Machine Learning: Built and trained regression models to predict insurance premiums.
Interpretability: Analyzed the importance of each attribute in determining costs.
Tech Stack:
Programming Language: Python
Libraries/Frameworks: NumPy, Pandas, Scikit-learn, Matplotlib, Seaborn
Highlights:
Demonstrates the impact of key factors like smoking habits and BMI on premium costs. Offers a practical tool for insurers to estimate premiums and for users to understand cost determinants.