Regression Learning Model for Biomaterial Design
Description
The development and validation of a regression learning model for biomaterial design. Data is split 75:25 into training and test sets, followed by cleaning and feature engineering. Parameters are estimated using OLS regression and optimized with Nelder-Mead error minimization until convergence. The calibrated model is validated against the test set using MAPE and RMSE metrics. This validated model identifies the optimal biomaterial composition, leading to new design insights.
Acknowledgements
References
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