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 solely. 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, etc.).

For the red wine…

ANALYSIS: The baseline performance of the 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, which was slightly worse than the baseline result from the training dataset.

For the white wine…

ANALYSIS: The baseline performance of the model achieved an RMSE of 0.726. After tuning the hyperparameters, the best model processed the training dataset with an RMSE of 0.714. Furthermore, the final model processed the test dataset with an RMSE of 0.693, which was slightly worse than the baseline result from the training dataset.

CONCLUSION: For this dataset, the model built using Keras and TensorFlow achieved a satisfactory result and should be considered for future modeling activities.

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.