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Task-2.lyx
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#LyX 2.3 created this file. For more info see http://www.lyx.org/
\lyxformat 544
\begin_document
\begin_header
\save_transient_properties true
\origin unavailable
\textclass article
\use_default_options true
\begin_modules
knitr
\end_modules
\maintain_unincluded_children false
\language english
\language_package default
\inputencoding auto
\fontencoding global
\font_roman "default" "default"
\font_sans "default" "default"
\font_typewriter "default" "default"
\font_math "auto" "auto"
\font_default_family default
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\font_sf_scale 100 100
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\use_microtype false
\use_dash_ligatures true
\graphics default
\default_output_format default
\output_sync 0
\bibtex_command default
\index_command default
\paperfontsize default
\spacing single
\use_hyperref false
\papersize default
\use_geometry false
\use_package amsmath 1
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\use_package cancel 1
\use_package esint 1
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\use_package mathtools 1
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\cite_engine basic
\cite_engine_type default
\biblio_style plain
\use_bibtopic false
\use_indices false
\paperorientation portrait
\suppress_date false
\justification true
\use_refstyle 1
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\index Index
\shortcut idx
\color #008000
\end_index
\secnumdepth 3
\tocdepth 3
\paragraph_separation indent
\paragraph_indentation default
\is_math_indent 0
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\quotes_style english
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\end_header
\begin_body
\begin_layout Title
GRIP Task 2: Prediction using Unsupervised ML
\end_layout
\begin_layout Author
Name: Debartha Paul
\end_layout
\begin_layout Section*
Importing libraries and visualising the data
\end_layout
\begin_layout Standard
We first load the libraries required for our work and then we read the dataset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#Luckily, we do not need any other library for this work.
\end_layout
\begin_layout Plain Layout
#Reading the dataset
\end_layout
\begin_layout Plain Layout
df<-read.csv('D:
\backslash
\backslash
Important Documents
\backslash
\backslash
Internship
\backslash
\backslash
Task-2
\backslash
\backslash
Iris.csv',header=T)
\end_layout
\begin_layout Plain Layout
dim(df)#dimensions of the dataset
\end_layout
\begin_layout Plain Layout
names(df)#column names of the dataset
\end_layout
\begin_layout Plain Layout
head(df)#a brief preview of the dataset
\end_layout
\begin_layout Plain Layout
summary(df)#a brief summary of the dataset
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Section*
Determining the value of k
\end_layout
\begin_layout Standard
We then determine the value of
\family typewriter
k
\family default
(optimum number of clusters) for the KMeans clustering.
We us the Elbow Method for determining the value of
\family typewriter
k
\family default
.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#Finding the optimum value of clusters
\end_layout
\begin_layout Plain Layout
twss<-sapply(1:10,function(k){kmeans(df[,-c(1,6)],k,iter.max=300,nstart=20)$tot.wi
thinss})
\end_layout
\begin_layout Plain Layout
plot(c(1:10),twss,type='b',lwd=2,col='dark blue',pch=16,
\end_layout
\begin_layout Plain Layout
ylab='Total within Sum of Squares',
\end_layout
\begin_layout Plain Layout
xlab='No.
of clusters',
\end_layout
\begin_layout Plain Layout
main='The Elbow Method')
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
From the above graph, we note that the the elbow occurs at 3.
\end_layout
\begin_layout Section*
Applying to the dataset
\end_layout
\begin_layout Standard
We now apply the kmeans method to our dataset, with the number of clusters
as 3.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#Applying the kmeans to our dataset
\end_layout
\begin_layout Plain Layout
KMeans<-kmeans(df[,-c(1,6)],3,iter.max=300,nstart=20)
\end_layout
\begin_layout Plain Layout
cluster<-fitted(KMeans,'classes')
\end_layout
\begin_layout Plain Layout
centroids<-as.data.frame(unique(fitted(KMeans,'centers')))
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Section*
Visualising the clusters
\end_layout
\begin_layout Standard
We visualise the clusters in the form of two scatterplots.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#1st scatterplot
\end_layout
\begin_layout Plain Layout
par(bg='light blue')
\end_layout
\begin_layout Plain Layout
plot(df[cluster==1,]$SepalLengthCm,
\end_layout
\begin_layout Plain Layout
df[cluster==1,]$SepalWidthCm,
\end_layout
\begin_layout Plain Layout
xlim=c(min(df$SepalLengthCm),max(df$SepalLengthCm)),
\end_layout
\begin_layout Plain Layout
ylim=c(min(df$SepalWidthCm),max(df$SepalWidthCm)),
\end_layout
\begin_layout Plain Layout
col='red',pch=16,main='Sepal Width vs.
Sepal Length',
\end_layout
\begin_layout Plain Layout
xlab='Sepal Length',ylab='Sepal Width')
\end_layout
\begin_layout Plain Layout
par(new=T)
\end_layout
\begin_layout Plain Layout
plot(df[cluster==2,]$SepalLengthCm,
\end_layout
\begin_layout Plain Layout
df[cluster==2,]$SepalWidthCm,
\end_layout
\begin_layout Plain Layout
xlim=c(min(df$SepalLengthCm),max(df$SepalLengthCm)),
\end_layout
\begin_layout Plain Layout
ylim=c(min(df$SepalWidthCm),max(df$SepalWidthCm)),
\end_layout
\begin_layout Plain Layout
col='blue',pch=16,xaxt='n',yaxt='n',xlab=NA,ylab=NA)
\end_layout
\begin_layout Plain Layout
par(new=T)
\end_layout
\begin_layout Plain Layout
plot(df[cluster==3,]$SepalLengthCm,
\end_layout
\begin_layout Plain Layout
df[cluster==3,]$SepalWidthCm,
\end_layout
\begin_layout Plain Layout
xlim=c(min(df$SepalLengthCm),max(df$SepalLengthCm)),
\end_layout
\begin_layout Plain Layout
ylim=c(min(df$SepalWidthCm),max(df$SepalWidthCm)),
\end_layout
\begin_layout Plain Layout
col='dark green',pch=16,xaxt='n',yaxt='n',xlab=NA,ylab=NA)
\end_layout
\begin_layout Plain Layout
par(new=T)
\end_layout
\begin_layout Plain Layout
plot(centroids$SepalLengthCm,centroids$SepalWidthCm,
\end_layout
\begin_layout Plain Layout
xlim=c(min(df$SepalLengthCm),max(df$SepalLengthCm)),
\end_layout
\begin_layout Plain Layout
ylim=c(min(df$SepalWidthCm),max(df$SepalWidthCm)),
\end_layout
\begin_layout Plain Layout
col='yellow',pch=16,cex=2,xaxt='n',yaxt='n',xlab=NA,ylab=NA)
\end_layout
\begin_layout Plain Layout
legend(x=7.1,y=4.4,c('Cluster 1','Cluster 2','Cluster 3','Centroids'),
\end_layout
\begin_layout Plain Layout
col=c('red','blue','dark green','yellow'),pch=16)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#2nd scatterplot
\end_layout
\begin_layout Plain Layout
par(bg='light green')
\end_layout
\begin_layout Plain Layout
plot(df[cluster==1,]$PetalLengthCm,
\end_layout
\begin_layout Plain Layout
df[cluster==1,]$PetalWidthCm,
\end_layout
\begin_layout Plain Layout
xlim=c(min(df$PetalLengthCm),max(df$PetalLengthCm)),
\end_layout
\begin_layout Plain Layout
ylim=c(min(df$PetalWidthCm),max(df$PetalWidthCm)),
\end_layout
\begin_layout Plain Layout
col='red',pch=16,main='Petal Width vs.
Petal Length',
\end_layout
\begin_layout Plain Layout
xlab='Petal Length',ylab='Petal Width')
\end_layout
\begin_layout Plain Layout
par(new=T)
\end_layout
\begin_layout Plain Layout
plot(df[cluster==2,]$PetalLengthCm,
\end_layout
\begin_layout Plain Layout
df[cluster==2,]$PetalWidthCm,
\end_layout
\begin_layout Plain Layout
xlim=c(min(df$PetalLengthCm),max(df$PetalLengthCm)),
\end_layout
\begin_layout Plain Layout
ylim=c(min(df$PetalWidthCm),max(df$PetalWidthCm)),
\end_layout
\begin_layout Plain Layout
col='blue',pch=16,xaxt='n',yaxt='n',xlab=NA,ylab=NA)
\end_layout
\begin_layout Plain Layout
par(new=T)
\end_layout
\begin_layout Plain Layout
plot(df[cluster==3,]$PetalLengthCm,
\end_layout
\begin_layout Plain Layout
df[cluster==3,]$PetalWidthCm,
\end_layout
\begin_layout Plain Layout
xlim=c(min(df$PetalLengthCm),max(df$PetalLengthCm)),
\end_layout
\begin_layout Plain Layout
ylim=c(min(df$PetalWidthCm),max(df$PetalWidthCm)),
\end_layout
\begin_layout Plain Layout
col='dark green',pch=16,xaxt='n',yaxt='n',xlab=NA,ylab=NA)
\end_layout
\begin_layout Plain Layout
par(new=T)
\end_layout
\begin_layout Plain Layout
plot(centroids$PetalLengthCm,centroids$PetalWidthCm,
\end_layout
\begin_layout Plain Layout
xlim=c(min(df$PetalLengthCm),max(df$PetalLengthCm)),
\end_layout
\begin_layout Plain Layout
ylim=c(min(df$PetalWidthCm),max(df$PetalWidthCm)),
\end_layout
\begin_layout Plain Layout
col='yellow',pch=16,cex=2,xaxt='n',yaxt='n',xlab=NA,ylab=NA)
\end_layout
\begin_layout Plain Layout
legend(x=1,y=2.5,c('Cluster 1','Cluster 2','Cluster 3','Centroids'),
\end_layout
\begin_layout Plain Layout
col=c('red','blue','dark green','yellow'),pch=16)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
Finally, we create a table summarising the clusters.
\end_layout
\begin_layout Standard
\begin_inset ERT
status open
\begin_layout Plain Layout
\end_layout
\begin_layout Plain Layout
<<>>=
\end_layout
\begin_layout Plain Layout
#Summary Table
\end_layout
\begin_layout Plain Layout
table(KMeans$cluster,df$Species)
\end_layout
\begin_layout Plain Layout
@
\end_layout
\begin_layout Plain Layout
\end_layout
\end_inset
\end_layout
\begin_layout Standard
From the table above, we find that the members of Iris-setosa were clustered
correctly.
However, there is some discrepancy regarding the clusters of the other
two species: Iris-versicolor and Iris-virginica
\end_layout
\end_body
\end_document