This model utilizes a multivariate linear regression algorithm to predict the cost of medical insurance. The dataset consists of 1338 data points with 6 input features and 1 target variable. Prior to training the model, exploratory data analysis and visualization techniques were applied.
Unfortunately, the accuracy of the model was found to be unsatisfactory, measuring at 74.5%. In order to improve the accuracy, alternative regression algorithms can be further explored and applied.