To build a model for Medical Insurance Cost Prediction Using Machine Learning. In this project machine learning algorithms such as linear regression, ridge regression, lasso regression and random forest regressor have been used.
Tools & Libraries - ● Numpy:- Used to support Panda frameworks. ● Pandas:- To Create Dataframe of the Image Pixel Values. ● Sklearn:- Python Library used for machine learning and statistical modelling including classification. ● Matplotlib:-Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python. Matplotlib makes easy things easy and hard things possible. Create publication quality plots. ● Seaborn:- Data visualization library that is commonly used for data science and machine learning tasks. It is used to create interactive plots to answer questions about data.
● Linear Regression ● Rigde Regression ● Lasso Regression ● Random Forest Regression
An investigation into individual health insurance data is conducted using two regression models.Out of the four models used above, Random forest regressor has better accuracy. Any unneeded attribute was removed from each of the features. Premiums are determined by a person's health rather than the terms and conditions of another insurance provider. Some other algorithms can be employed to predict premiums based on data and improve accuracy. People and insurance companies can work together to deliver better and more health-focused coverage as a result of this.