BST260_WineAnalysis/
├── data
│ └── winequality_all.csv : dataset(red and white combined) downloaded from [`UCI Machine Learning Repository`](https://archive.ics.uci.edu/ml/datasets/Wine+Quality)
│ └── group_level_accuracy.csv : quality prediction accuracy of each group
│
├── html: html files for all analysis
├── rmd: rmd files for all analysis
└── app.R : Shiny App
└── README.md
└── ...
Video Link: BST260 Final Project Video
Website Link : Wine Analysis
Ling Feng
Yiting Han
Xingchen Hao
Zongjun Liu
Yuming Shi
Yichun Yao
Motivation, objectives, related work and initial questions of the project topic about Portuguese wine quality assessment.
Analysis of fixed acidity, citric acidity, volatile acidity and their associations with wine type and quality.
Analysis of sugar and its association with wine quality.
Analysis of chloride and its association with wine quality.
Analysis of free sulfur ioxide and total sulfur dioxide and their associations with wine quality.
Analysis of density and its association with wine quality.
Analysis of pH and its association with wine quality.
Analysis of sulphate and its association with wine quality.
Analysis of alcohol and its association with wine quality.
Analysis of correlations between all the continuous variables by constructing a correlation graph.
Analysis of determining the optimal number of clusters based on several methods and Constructing the cluster graph based on K-means clustering and PAM clustering.
Analysis of principal components and prediction of wine quality based on principal components.
Prediction of wine quality using linear regression and machine learning techniques.
Prediction of wine type(red/white) using machine learning techniques.