An Accurate Prediction of Medical Insurance Cost Using Forest Regression Algorithms

The project is a health insurance forecasting system powered by artificial intelligence. Health insurance has become one of the most important areas of study since the COVID-19 outbreak, and there have recently been various attempts to discover solutions to this problem. To predict insurance prices, the dataset includes age, gender, BMI, smoking habits, the number of children, and other characteristics. Medical insurance cost prediction is a crucial task that can assist people and insurance companies plan their finances and premiums. The model provided more accuracy for predicting medical insurance with the help of regression techniques. For classification and regression, rather than being a boosting approach, random forest is a bagging technique. In random forests, the trees are arranged in parallel. When these trees are being built, there is no contact between them. Combining multiple decision trees also helps random forests handle noisy data and outliers effectively, as the averaging process mitigates their impact on the final prediction. Additionally, random forests provide a feature importance score, indicating each feature's contribution in making accurate predictions. This can be particularly valuable in understanding the underlying relationships within the data and identifying key factors driving the outcomes. As a result, random forests are widely used in various fields such as finance, healthcare, and marketing for their ability to deliver reliable and interpretable predictions.