Python-Regression-Solutions-Health-Insurance-Cost-Prediction

The Python code for solving a Regression problem for predicting optimum Health Insurance Cost for the Individual. Has through EDA and various Regressor models employed in the prediction

Problem: All are aware that health care is a critical market segment. It is inextricably tied to the individual's existence; as a result, we must always be proactive in this area. Monetary aspect is important in this arena since therapy may be quite expensive at times, and if an individual is not covered by insurance, he or she will be in a very difficult financial situation. Medical insurance firms also aim to limit their risk by lowering insurance costs, because we all know that a healthy body is solely in the hands of the individual. Individuals who eat well and exercise regularly minimize their chances of being unwell.

Solution: The goal of this exercise is to create a model based on data that provides the best insurance pricing for a specific individual. For the projected insurance cost, you must use health and habit-related criteria.

Models:

1. Linear Regression
2. Lasso Regression
3. Elastic Net Regression
4. sklearn.tree.DecisionTreeRegressor
5. sklearn.ensemble.RandomForestRegressor
6. sklearn.neighbors.KNeighborsRegressor