Keywords
Machine Learning, Regression, Life Expectancy, Predictive Analysis
Abstract
This study presents a comprehensive analysis of three prominent machine learning regression models—Random Forest, XGBoost, and Support Vector Machine (SVM)—in the context of predictive analysis. Leveraging a carefully curated dataset, we explore the impact of various hyperparameters on model performance through an exhaustive tuning process. The Random Forest and XGBoost models exhibit robust predictive capabilities, with the former revealing notable insights through feature importance visualization. Additionally, SVM, optimized via GridSearchCV, demonstrates competitive performance. Evaluation metrics, including Mean Squared Error and R-squared, facilitate a thorough comparison of model efficacy. Results highlight nuanced strengths and weaknesses, informing practitioners on the suitability of each model for specific applications. This research contributes valuable insights to the ongoing discourse on machine learning regression, offering a practical guide for researchers and practitioners navigating the complex landscape of predictive analysis.
Semester
Fall 2023
Course Name
STA 6366 Data Science 1
Instructor Name
Dr. Rui Xie
College
College of Engineering and Computer Science
STARS Citation
Alinejad, Mahyar, "Analyzing the Impact of Health, Economic, and Demographic Factors on Life Expectancy: A Comparative Study of Developed and Developing Countries" (2023). Data Science and Data Mining. 14.
https://stars.library.ucf.edu/data-science-mining/14
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