Supervised Leaning Modelings

Wine Quality Data | Machine Learning Implementation in R

#K-nearest neighbor algorithm (KNN)   #Logistic regression  
#Linear Discriminant Analysis (LDA)   #Quadratic Discriminant Analysis(QDA)
#Naive Bayes   #Decision Tree (CART)   #Random Forest (Classification)
#Bagging   #Boosting   #Support Vector Machine (SVM)   #Neural Network

Preamble

Consider the wine quality dataset from UCI Machine Learning Respository ¹. We will focus only on the data concerning white wines (and not red wines). Dichotomize the quality variable as good, which takes the value 1 if quality ≥ 7 and the value 0, otherwise. We will take good as response and all the 11 physicochemical characteristics of the wines in the data as predictors. Develop a good classifier and justify your choice of that classifier.

File Tree

📦Wine-Quality-Modeling
 ┣ 📂_freeze
 ┣ 📂_site              // Repository Website
 ┣ 📂asset              // Website Assets
 ┃ ┣ 📂css
 ┃ ┗ 📂img
 ┣ 📂src                // Source Code
 ┃ ┣ 📂dataset
 ┃ ┃ ┣ 📂model
 ┃ ┃ ┣ 📂plot
 ┃ ┣ 📄analysis.qmd
 ┃ ┣ 📄bagging.qmd
 ┃ ┣ 📄boosting.qmd
 ┃ ┣ 📄decisionTree.qmd
 ┃ ┣ 📄knn.qmd
 ┃ ┣ 📄lda.qmd
 ┃ ┣ 📄logit.qmd
 ┃ ┣ 📄naiveBayes.qmd
 ┃ ┣ 📄nnet.qmd
 ┃ ┣ 📄qda.qmd
 ┃ ┣ 📄randomForest.qmd
 ┃ ┣ 📄summary.qmd
 ┃ ┣ 📄svm.qmd
 ┃ ┗ 📄xgboost.qmd 
 ┣ 📄.gitignore
 ┣ 📄LICENSE
 ┣ 📄README.md
 ┣ 📄_quarto.yml
 ┗ 📄index.qmd

Reference

Footnotes

1. 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. ↩