Predictive Analytics for Future Life Expectancy Using Machine Learning

Predictive Analytics for Future Life Expectancy Using Machine Learning

Mrs. V. Krishna Vijaya 1, Ms. Latchi Naga Mythili2, Ms. Manam Nandini3,
Ms. Kothuri Keerthi4, Ms. Manchala Priyanka5

1 Asst. Professor, Dept. Information Technology, KKR & KSR Institute of Technology and Sciences, Guntur, India

2-5Student, Dept. Information Technology, KKR & KSR Institute of Technology and Sciences, Guntur, India

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Abstract - Advanced machine learning is used in the project "Predictive Analytics for Future Life Expectancy Using Machine Learning." methods for forecasting forthcoming trends in lifespan. By examining a range of datasets, including historical lifetime records, healthcare data, economic features, demographics, and environmental information, the research develops accurate prediction models. To assure reliability, these models—which were constructed using a variety of machine learning algorithms, including ensemble approaches, decision trees, and regression—go through a thorough training and validation process. This resulting forecasting tool is able to be used to legislators, healthcare professionals, and academics to make informed decisions on public health, including resource allocation. Periodic updates and monitoring enable reaction to evolving trends all while maintaining relevance and effectiveness over time.

Key Words: Predictive Analytics, Future Life Expectancy, Linear regressor, Decision Tree regressor, Random forest regressor, Xgboost.