kripanshsharma / premium_prediction

Premium_Prediction

Aim of the Project

The aim of this project is to design and optimize a regression model to predict health insurance premiums with high accuracy.

Technologies Used

Python, pandas, matplotlib, Seaborn, Scikit-learn.

Working in Brief

The project involves designing a regression model to predict health insurance premiums based on various factors. Data is cleaned and preprocessed, followed by exploratory data analysis to identify key factors impacting premiums. The model's performance is enhanced through cross-validation and feature scaling.

Results and Future Improvement

The model achieved an 83% accuracy in predicting health insurance premiums. Future improvements could focus on incorporating more data points, experimenting with different machine learning algorithms, and fine-tuning the model for even better accuracy.