We have used several well-known supervised regressor machine learning algorithms such as Gradient Boost regressor (GBR), Random Forest regressor (RF), Support Vector Regressor (SVR), and Knearest neighbors regressor (KNN) for this study. These models are trained using the training data, and their performance are evaluated using the test data. The hyperparameters of the models were tuned using grid search with five-fold cross-validation. In five-fold crossvalidation, the training data was divided into five smaller datasets known as folds, and the model was trained on four of the folds as training data and validated on the remaining part of the data. The hyperparameters were tuned based on the average value of the performance scores computed in five different loops. Here, we have used the python library scikit-learn GroupKFold function to ensure that the same alloy group does not repeat in different folds to avoid information leakage within folds. Once the hyperparameters of the model were tuned, performance evaluation of the model was carried out using the unseen test data. Mean absolute error (MAE), root mean square error (RMSE), and the correlation coefficient (R 2 ) were used as metrics to evaluate the performance of the machine learning models. In this study, models not only predicted the targeted property but also quantified the uncertainty of the predictions.