Wines K-means clustering
Jose L. Santana
2022-06-14
K Means Clustering Project
Usually when dealing with an unsupervised learning problem, its difficult to get a good measure of how well the model performed. For this project, we will use data from the UCI archive based off of red and white wines (this is a very commonly used data set in ML).
We will then add a label to the a combined data set, we’ll bring this label back later to see how well we can cluster the wine into groups. Get the Data
Libraries
library(ggplot2)Data
df1 <- read.csv("winequality-red.csv", sep = ";")
df2 <- read.csv("winequality-white.csv", sep = ";")
df1$wine.type <- "red"
df2$wine.type <- "white"Check heads of dfs
head(df1, 10)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.4 0.70 0.00 1.9 0.076
## 2 7.8 0.88 0.00 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.70 0.00 1.9 0.076
## 6 7.4 0.66 0.00 1.8 0.075
## 7 7.9 0.60 0.06 1.6 0.069
## 8 7.3 0.65 0.00 1.2 0.065
## 9 7.8 0.58 0.02 2.0 0.073
## 10 7.5 0.50 0.36 6.1 0.071
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## 7 15 59 0.9964 3.30 0.46 9.4
## 8 15 21 0.9946 3.39 0.47 10.0
## 9 9 18 0.9968 3.36 0.57 9.5
## 10 17 102 0.9978 3.35 0.80 10.5
## quality wine.type
## 1 5 red
## 2 5 red
## 3 5 red
## 4 6 red
## 5 5 red
## 6 5 red
## 7 5 red
## 8 7 red
## 9 7 red
## 10 5 redhead(df2, 10)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.0 0.27 0.36 20.7 0.045
## 2 6.3 0.30 0.34 1.6 0.049
## 3 8.1 0.28 0.40 6.9 0.050
## 4 7.2 0.23 0.32 8.5 0.058
## 5 7.2 0.23 0.32 8.5 0.058
## 6 8.1 0.28 0.40 6.9 0.050
## 7 6.2 0.32 0.16 7.0 0.045
## 8 7.0 0.27 0.36 20.7 0.045
## 9 6.3 0.30 0.34 1.6 0.049
## 10 8.1 0.22 0.43 1.5 0.044
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 45 170 1.0010 3.00 0.45 8.8
## 2 14 132 0.9940 3.30 0.49 9.5
## 3 30 97 0.9951 3.26 0.44 10.1
## 4 47 186 0.9956 3.19 0.40 9.9
## 5 47 186 0.9956 3.19 0.40 9.9
## 6 30 97 0.9951 3.26 0.44 10.1
## 7 30 136 0.9949 3.18 0.47 9.6
## 8 45 170 1.0010 3.00 0.45 8.8
## 9 14 132 0.9940 3.30 0.49 9.5
## 10 28 129 0.9938 3.22 0.45 11.0
## quality wine.type
## 1 6 white
## 2 6 white
## 3 6 white
## 4 6 white
## 5 6 white
## 6 6 white
## 7 6 white
## 8 6 white
## 9 6 white
## 10 6 whiteCombine dataframes
wine <- rbind(df1, df2)
str(wine)## 'data.frame': 6497 obs. of 13 variables:
## $ fixed.acidity : num 7.4 7.8 7.8 11.2 7.4 7.4 7.9 7.3 7.8 7.5 ...
## $ volatile.acidity : num 0.7 0.88 0.76 0.28 0.7 0.66 0.6 0.65 0.58 0.5 ...
## $ citric.acid : num 0 0 0.04 0.56 0 0 0.06 0 0.02 0.36 ...
## $ residual.sugar : num 1.9 2.6 2.3 1.9 1.9 1.8 1.6 1.2 2 6.1 ...
## $ chlorides : num 0.076 0.098 0.092 0.075 0.076 0.075 0.069 0.065 0.073 0.071 ...
## $ free.sulfur.dioxide : num 11 25 15 17 11 13 15 15 9 17 ...
## $ total.sulfur.dioxide: num 34 67 54 60 34 40 59 21 18 102 ...
## $ density : num 0.998 0.997 0.997 0.998 0.998 ...
## $ pH : num 3.51 3.2 3.26 3.16 3.51 3.51 3.3 3.39 3.36 3.35 ...
## $ sulphates : num 0.56 0.68 0.65 0.58 0.56 0.56 0.46 0.47 0.57 0.8 ...
## $ alcohol : num 9.4 9.8 9.8 9.8 9.4 9.4 9.4 10 9.5 10.5 ...
## $ quality : int 5 5 5 6 5 5 5 7 7 5 ...
## $ wine.type : chr "red" "red" "red" "red" ...EDA
Exploring data
Histrogram of the residual sugar by wine
ggplot(wine, aes(residual.sugar)) + geom_histogram(aes(fill=wine.type), color = "black", alpha = .7)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Histogram of citric.acid from wine data. Color by wine.
ggplot(wine, aes(citric.acid)) + geom_histogram(aes(fill=wine.type), color = "black", alpha = .7)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Histogram of alcohol from the wine data. Color by red and white
wines.
ggplot(wine, aes(alcohol)) + geom_histogram(aes(fill=wine.type), color = "black", alpha = .7)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Scatterplot of residual.sugar versus citric.acid, color by red and white wine.
ggplot(wine, aes(citric.acid, residual.sugar))+geom_point(aes(color=wine.type), alpha=.2, size = 2)
Scatterplot of volatile.acidity versus residual.sugar, color by red and white wine.
ggplot(wine, aes(volatile.acidity, residual.sugar))+geom_point(aes(color=wine.type),
alpha=.2, size = 2)
Subset dataframe without label
clus.data <- wine[, 1:12]
head(clus.data, 10)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 7.4 0.70 0.00 1.9 0.076
## 2 7.8 0.88 0.00 2.6 0.098
## 3 7.8 0.76 0.04 2.3 0.092
## 4 11.2 0.28 0.56 1.9 0.075
## 5 7.4 0.70 0.00 1.9 0.076
## 6 7.4 0.66 0.00 1.8 0.075
## 7 7.9 0.60 0.06 1.6 0.069
## 8 7.3 0.65 0.00 1.2 0.065
## 9 7.8 0.58 0.02 2.0 0.073
## 10 7.5 0.50 0.36 6.1 0.071
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates alcohol
## 1 11 34 0.9978 3.51 0.56 9.4
## 2 25 67 0.9968 3.20 0.68 9.8
## 3 15 54 0.9970 3.26 0.65 9.8
## 4 17 60 0.9980 3.16 0.58 9.8
## 5 11 34 0.9978 3.51 0.56 9.4
## 6 13 40 0.9978 3.51 0.56 9.4
## 7 15 59 0.9964 3.30 0.46 9.4
## 8 15 21 0.9946 3.39 0.47 10.0
## 9 9 18 0.9968 3.36 0.57 9.5
## 10 17 102 0.9978 3.35 0.80 10.5
## quality
## 1 5
## 2 5
## 3 5
## 4 6
## 5 5
## 6 5
## 7 5
## 8 7
## 9 7
## 10 5Building the clusters
Call the kmeans function on clus.data and assign the results to wine.cluster.
wine.cluster <- kmeans(wine[1:12], 2)
print(wine.cluster$centers)## fixed.acidity volatile.acidity citric.acid residual.sugar chlorides
## 1 6.904812 0.2871659 0.3397642 7.244809 0.04859257
## 2 7.623219 0.4086378 0.2908725 3.076425 0.06580983
## free.sulfur.dioxide total.sulfur.dioxide density pH sulphates
## 1 39.75590 155.69246 0.9947903 3.190808 0.4999485
## 2 18.39868 63.26318 0.9945736 3.254882 0.5724145
## alcohol quality
## 1 10.25932 5.824343
## 2 10.79722 5.810541Evaluating the clusters
table(wine$wine.type, wine.cluster$cluster)##
## 1 2
## red 85 1514
## white 3604 1294