Predicting Life Expectancy: A Machine Learning Approach For Improved Accuracy

The number of years people survive functions as a vital marker to assess both public health state and economic standards of a community. The prediction system evaluates life expectancy through multiple analysis of GDP per capita and healthcare spending and literacy rates and deathratesalongwithvariablesthatreflectlifestylechoices. The dataset includes many records collected from internationally respected sources to ensure trustworthiness. The relationship between life expectancy and its influencing factors becomes discernible using XGBoost alongside RF and MLR as machine learning algorithms. The assessmentof predictive ability for each model depends on Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) using training and testing sets. XGBoost achieves superior accuracy than other models due to its strong capability in processing non-linear relationships. Feature importance analysis helps medical practitioners and policymakers acquire vital data about the determinants which affect life expectancy.The research contributes to life expectancy predictive modeling while helping data-based decisions for resource planning in healthcare worldwide.