Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.
Dataset Used: Glass Identification Data Set
Data Set ML Model: Classification with real number attributes
Dataset Reference: https://archive.ics.uci.edu/ml/datasets/Glass+Identification
For more information on this case study project and performance benchmarks, please consult: https://www.kaggle.com/uciml/glass
The glass identification dataset involves predicting the six types of glass, defined by their oxide content (i.e., Na, Fe, K, .and so forth). The criminological investigation was the motivation for the study of the classification of types of glass. At the scene of the crime, the glass left can be used as evidence, if it is correctly identified!
CONCLUSION: The baseline performance of predicting the class variable achieved an average accuracy of 71.45%. The top accuracy result achieved via RandomForest was 80.11% after a series of tuning trials. The ensemble algorithm, in this case, yielded a better result than the non-ensemble algorithms to justify the additional processing and tuning.
The HTML formatted report can be found here on GitHub.