Includes:
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Clustering - K-means, DBSCAN, GMM --- scripts to choose close materials for better models.
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Regression Models - Gaussian process, Kernel ridge, XGBoost, neural net (Keras Tensorflow), random forest, etc. --- scripts to use better kernel or hyperparameters.
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Random sample generation - Brute force, and Gaussian mixture model --- with specific restrictions.
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Backward prediction and screening - Combine all process to find solution --- allows multiple targets and show solution reliability as circle sized.
Verify if standard deviation truely reflects dot size (large means small deviation)
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Automatic outlier detection - Powerful tool to improve performance of regression models. Thank you again Kaneko sensei Meiji University. ( https://datachemeng.com/outlier_samples_detectionc_python/ )