Imported dataset containing transactions in grocery store, created Sparse Matrix and visualised Frequency Plot

Author: anastazijaverovic

Part 4 - Clustering/Section 25 - Hierarchical Clustering/.Rhistory

@@ -1,21 +1,4 @@
 }
-}
-for(r in regressor$coefficients)
-{
-print(r)
-else{
-print(regressor$coefficients)
-}
-}
-for(r in regressor$coefficients)
-{
-print(r)
-}
-summary(regressor)
-for(r in regressor$coefficients)
-{
-print(r[2])
-}
 for(r in regressor$coefficients)
 {
 print(r[3])
@@ -493,20 +476,37 @@ SL = 0.05
 dataset = dataset[, c(1,2,3,4,5)]
 backwardElimination(training_set, SL)
 setwd("~/Desktop/Machine_Learning_Algorithms_R/Part 4 - Clustering/Section 25 - Hierarchical Clustering")
+library(cluster)
+clusplot (dataset,
+y_hc,
+lines = 0,
+shade = TRUE,
+color = TRUE,
+labels = 2,
+plotchar = FALSE,
+span = TRUE,
+main = paste('Clusters of customers'),
+xlab = "Annual Income",
+ylab = "Spending Score")
 dataset = read.csv("Mall_Customers.csv")
-dataset = read.csv("Mall_Customers.csv")
-View(dataset)
 dataset = dataset[4:5]
-View(dataset)
 dendrogram = hclust(d = dist(dataset,method = "euclidean"),
 method = "ward.D")
-View(dataset)
 plot(dendrogram,
 main = paste("Dendrogram"),
 xlab = "Customers",
 ylab = "Euclidean distances")
 hc = hclust(d = dist(dataset,method = "euclidean"),
 method = "ward.D")
-y_hc = cutree(ttree = hc, k = 5)
 y_hc = cutree(tree = hc, k = 5)
-y_hc
+clusplot (dataset,
+y_hc,
+lines = 0,
+shade = TRUE,
+color = TRUE,
+labels = 2,
+plotchar = FALSE,
+span = TRUE,
+main = paste('Clusters of customers'),
+xlab = "Annual Income",
+ylab = "Spending Score")