Facial Beauty Standards Predictions Based on Machine Learning: A Comparative Analysis
DOI:
https://doi.org/10.33022/ijcs.v13i1.3709Keywords:
facial beauty standards Prediction, Random Forest, Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors Algorithm (KNN)Decision TreeAbstract
This study uses a variety of machine learning and classification methods to anticipate the Facial Beauty Standards. The Accuracy of five different models—Random Forest, Logistic Regression, Support Vector Machine (SVM), KNN, and decision tree—were used to analyses each one. There were noticeable differences in the models' performances. In particular, the Logistic Regression and SVM methods demonstrate almost perfect accuracy, followed closely by random forest and KNN. This study gives insight into how well different models perform in comparison and emphasizes the benefits and drawbacks of each in terms of predicting face beauty standards.
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Copyright (c) 2024 bareen haval sadiq, Adnan M. Abdulazeez
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.