Predicting Wine Quality by comparing Linear Regression with Machine Learning techniques.

Comparing Linear Regression with kNN, Decision Tree and Random Forest with Bayesian Inference to Predict Wine Quality in Python.

We use python and Jupyter Notebook to download, extract, transform and analyze data about the physicochemical properties which make up wine, and use them to predict quality.

wine_quality.db is the database file which can be recreated with the wine_quality.sql scheme by running the wine_quality_ETL.py script.

Use the “Wine Quality.ipynd” to see the indepth analysis yourself.

Wine_Quality-Presentation.tex contains the code for the presentation, made in LaTeX.

models.py contains the the linear regression models which we use for our analysis.

A video recording of me going through the presentation can be viewed at: https://youtu.be/QpNIjdjWO0Q

The code is available on github, at: https://github.com/JoshuaPaulBarnard/Predicting-Wine-Quality

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Credits :

• P. Cortez, A. Cerdeira, F. Almeida, T. Matos and J. Reis. Modeling wine preferences by data mining from physicochemical properties. In Decision Support Systems, Elsevier, 47(4):547-553, 2009. Available at: http://www3.dsi.uminho.pt/pcortez/wine/

-Charters, S. and Pettigrew, S., 2007. The dimensions of wine quality. Food Quality and Preference, [online] 18(7), pp.997-1007. Available at: https://doi.org/10.1016/j.foodqual.2007.04.003

MIT License Copyright (c) 2022 Joshua Paul Barnard.