Affiliations: [a] Department of Computer Science and Engineering, GSFC University, Vadodara, Gujarat, India | [b] Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, India | [c] MIT School of Engineering, MIT ADT University, Rajbaug, Loni Kalbhor, Pune, India | [d] Visvesvaraya National Institute of Technology (VNIT), Nagpur, India
Correspondence:
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Corresponding author: Nilesh Bhaskarrao Bahadure, Department of Computer Science and Engineering, GSFC University, Vadodara, Gujarat, India. E-mails: [email protected], [email protected].
Abstract: This study performed a comparative analysis of various imputations for NULL values in the dataset, namely, mean, median, and mode. We implemented eleven regression models, including Linear and Support Vector Regression and tree-based regression models, such as decision tree, Surrogate tree, and random forest, with five different pre-processing techniques, providing different types of results. The core objective of this study is to compare these results and reach an interpretation as to why certain imputation technique produces a certain output. The interpretation of this result is helpful in the selection of the regression model. The experimental results of the proposed technique were evaluated and validated for the performance and quality analysis of life expectancy prediction using various quality parameters. Among the results, the highest accuracy was produced by random forest regression with an accuracy of 96.8%, which proves the significance of random forest in comparison to other state-of-the-art regression methods for life expectancy prediction.
Keywords: Life expectancy, random forest, decision tree, surrogate tree, support vector regression, regression methods
