/wine_quality_prediction

This project aims to build machine learning models that predict a given wine’s quality based on its physicochemical characters.

Primary LanguagePythonMIT LicenseMIT

README

Prediction of Wine Quality based on Physicochemical Tests

  • author: JiaJie (Joshua) Lim, Ling (Elina) Lin, Jiacheng Wang, Kangyu (Mark) Wang

This project aims to build machine learning models that predict a given wine’s quality based on its physicochemical characters. It uses classification and regression techniques k-nearest neighbors (k-NN) and support vector machines (SVMs) with RBF kernel.

Data

The data sets used in this project are of the prediction of wine quality based on physicochemical tests, related to red and white vinho verde wine samples, from the north of Portugal. The data were created by Paulo Cortez et al at the University of Minho, Guimarães, Portugal (Cortez et al. 2009). It was sourced from the UCI Machine Learning Repository (Dua and Graff 2017) and can be found here, specifically this file. The data provided only have physicochemical (inputs) and sensory (the output) variables available (e.g. there is no data about grape types, wine brand, wine selling price, etc.). There are a total of 4898 instances in our combined dataset of red wine and white wine.

Research Question

From the data set provided, we are interested in predicting the wine quality (white wine and red wine) based on the physicochemical tests. We are engaged in predicting the quality score (score between 0 and 10) of a wine based on variables including:

  • fixed acidity: most acids involved with wine or fixed or nonvolatile (do not evaporate readily).
  • volatile acidity: the amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste.
  • citric acid: found in small quantities, citric acid can add ‘freshness’ and flavor to wines.
  • residual sugar: the amount of sugar remaining after fermentation stops, it’s rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet.
  • chlorides: the amount of salt in the wine.
  • free sulfur dioxide: the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine.
  • total sulfur dioxide: amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine.
  • density: the density of wine is close to that of water depending on the percent alcohol and sugar content.
  • pH: describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale.
  • sulphates: a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant.
  • alcohol: the percent alcohol content of the wine.

Moreover, we will also attempt to look for the correlation between the explanatory variables, to see if there are any multicollinearity occurs. Assuming all variables are independent, if the variables are correlated and the degree of correlation between variables is high enough, it can be a problem when fitting the model and the result may not be accurate.

Analysis

The k-nearest neighbors (k-nn) algorithm will be used to build a classification model to predict the quality score of the wine, including white and red wine. All variables included in the original dataset will be used in fitting our model. Also, the support vector machines (SVMs) with RBF kernel will be our second model in predicting the quality score of wine. We are hoping to see improvement in our model, given the support vectors. Most of our analysis will be done using Python and R programming languages (R Core Team 2020; Van Rossum and Drake 2009).

Exploratory Data Analysis (EDA)

EDA table:

  • We will be using python data.info() to check for information on our data. From this table, we can check for the data type of each column and look for missing values contained in every column. If there are any missing values, we would want to clean the data first before proceeding with any analysis. Also, data.describe is also useful in identifying the minimum, maximum, and mean of each numerical columns. We would want to double-check if the values returned from the table are ‘acceptable.’

EDA figure:

  • We will be plotting a histogram on all the variables to gain insight into the probability distribution that the dataset follows. With a histogram, it is easy for us to see how the data is distributed. Also, scatterplot will be used to show the relationship between two variables. If the points between two variables resemble a straight line, this indicates that the relationship between the two variables we have is approximately linear.

Sharing Results of Analysis

We would compare the results of our cross-validation analysis to test analysis and see whether there exists any overfitting or underfitting in our model. Also, we would return the confusion matrix of model performance on the test data to check for misclassification errors.

Report

The final report can be found here.

Usage

1. Using Docker

note - the instructions in this section also depends on running this in a unix shell (e.g., terminal or Git Bash)

To replicate the analysis, install Docker. Then clone this GitHub repository and run the following command at the command line/terminal from the root directory of this project:

docker run --rm -v /$(pwd):/home/rstudio/project dsci522_t23 make -C /home/rstudio/project all

To reset the repo to a clean state, with no intermediate or results files, run the following command at the command line/terminal from the root directory of this project:

docker run --rm -v /$(pwd):/home/rstudio/project dsci522_t23 make -C /home/rstudio/project clean

2. Without Using Docker

To replicate the analysis, clone this GitHub repository, install the dependencies listed below, and run the following command at the command line/terminal from the root directory of this project:

make all

To reset the repo to a clean state, with no intermediate or results files, run the following command at the command line/terminal from the root directory of this project:

make clean

Dependencies

  • Python 3.8.3 and Python packages:

    • ipykernel==5.1.2
    • docopt==0.6.2
    • pandas==0.25.1
    • altair==4.1.0
    • scikit-learn>=0.21.3

  • R version 4.0.3 and R packages:

    • knitr==1.30
    • tidyverse==1.3.0
    • dplyr==2.0.0
    • readr==1.4.0

  • GNU make 4.2.1

References

Cortez, Paulo, António Cerdeira, Fernando Almeida, Telmo Matos, and José Reis. 2009. “Modeling Wine Preferences by Data Mining from Physicochemical Properties.” Decision Support Systems 47 (4): 547–53.

Dua, Dheeru, and Casey Graff. 2017. “UCI Machine Learning Repository.” University of California, Irvine, School of Information; Computer Sciences. http://archive.ics.uci.edu/ml.

R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.

Van Rossum, Guido, and Fred L. Drake. 2009. Python 3 Reference Manual. Scotts Valley, CA: CreateSpace.