Using machine learning to predict the target Re at different V ranging from 150-350 and droplet size d1 and d2. We need machine learning to overcome this problem and techniques to overcome small data set problems like data augmentation (CTGAN, VAE, SMOTE, Gaussian Noise, Mixing Data regression) to improve model accuracy and validate the final model after this.
We need to use different algorithms like linear regression, Decision tree-based regression, and Neural network-based models and compare the models to see which one outperforms and is validated.ImportantAll models must be improved to high performance tested and validated.All tables and related plots (Metrics, performance, loss function, Actual vs predicted, training, testing and validation)
