Abstract:
The average longevity of a person is measured by life expectancy. The length of one's life is determined
by a number of factors. We utilized GDP, rural population growth, urban population growth, services
value, industry value, food production, permanent cropland, cereal production, agriculture, forestry, and
fisheries value as indicators of life expectancy. We may examine all of the factors that influence life
expectancy, such as the negative link between life expectancy and rural population. We can observe how
personality traits are linked to life expectancy and impact how we spend our lives. To evaluate which
regression models are the most accurate, we use eight different regression models. The Extreme Gradient
Boosting Regressor has the greatest accuracy and the least error of all the models. It was 99 percent
correct. K-Neighbors, Random Forest, and Stacking Regressor were all 94 percent accurate. Slightly
Stacking was the most accurate of the bunch. We used K-Neighbors, Gradient Boosting, and Random
Forest Regressor for the Stacking Regressor, and Random Forest for the meta regressor. Decision Tree
has the lowest accuracy of all the models, at 79 percent. The Gradient Boosting Regressor comes in
second with 96 percent accuracy. Multiple Linear Regression and Light Gradient Boosting Machine
Regressor scored 88 percent and 87 percent, respectively. This study assists a country in enhancing the
value of its characteristics in terms of life expectancy.
