Template Credit: Adapted from a template made available by Dr. Jason Brownlee of Machine Learning Mastery.

SUMMARY: The purpose of this project is to construct a predictive model using various machine learning algorithms and to document the end-to-end steps using a template. The Wine Quality dataset is a regression situation where we are trying to predict the value of a continuous variable.

INTRODUCTION: The dataset is related to the white variants of the Portuguese “Vinho Verde” wine. The problem is to predict the wine quality using the chemical characteristics of the wine. Due to privacy and logistic issues, only physicochemical (inputs) and sensory (the output) variables are available (e.g., there is no data about grape types, wine brand, wine selling price).

ANALYSIS: In another iteration of modeling with TensorFlow, the performance of the preliminary model achieved an RMSE of 0.663. After tuning the hyperparameters, the best model processed the training dataset with an RMSE of 0.643. Furthermore, the final model processed the test dataset with an RMSE of 0.679.

After a series of modeling trials, the AutoKeras system processed the validation dataset with a minimum RMSE score of 0.386. When we applied the best AutoKeras model to the previously unseen test dataset, we obtained an RMSE score of 0.602.

CONCLUSION: In this iteration, the best TensorFlow model generated by AutoKeras appeared to be suitable for modeling this dataset. We should consider experimenting with AutoKeras for further modeling.

Dataset Used: Wine Quality Data Set

Dataset ML Model: Regression with numerical attributes

Dataset Reference: https://archive.ics.uci.edu/ml/datasets/wine+quality

The HTML formatted report can be found here on GitHub.