Analysis of Iris dataset using KNN
Menali
February 25, 2019
Introduction
This is a famous dataset that has measurements (in centimeters) of the sepal and petal for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica. It has four variable and hundred fifty rows.
Libraries used
library(tidyverse)
library(GGally) # for GGPairs
library(gridExtra) # for grid arrange
library(class) #for KNN
library(gmodels) #for Cross Table Exploratory Data Analysis
About the Dataset
head(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1 5.1 3.5 1.4 0.2 setosa
## 2 4.9 3.0 1.4 0.2 setosa
## 3 4.7 3.2 1.3 0.2 setosa
## 4 4.6 3.1 1.5 0.2 setosa
## 5 5.0 3.6 1.4 0.2 setosa
## 6 5.4 3.9 1.7 0.4 setosa#summary
summary(iris)## Sepal.Length Sepal.Width Petal.Length Petal.Width
## Min. :4.300 Min. :2.000 Min. :1.000 Min. :0.100
## 1st Qu.:5.100 1st Qu.:2.800 1st Qu.:1.600 1st Qu.:0.300
## Median :5.800 Median :3.000 Median :4.350 Median :1.300
## Mean :5.843 Mean :3.057 Mean :3.758 Mean :1.199
## 3rd Qu.:6.400 3rd Qu.:3.300 3rd Qu.:5.100 3rd Qu.:1.800
## Max. :7.900 Max. :4.400 Max. :6.900 Max. :2.500
## Species
## setosa :50
## versicolor:50
## virginica :50
##
##
## #plot
ggpairs(iris)
Plot of Sepal Length - Width and Petal Length- Width
g1 <- ggplot(iris , aes(x= Sepal.Length, y=Sepal.Width, col= Species))+
geom_point()
g2 <- ggplot(iris , aes(x= Petal.Length, y=Petal.Width, col= Species))+
geom_point()
grid.arrange(g1, g2)
Analysis
Step I: Scaling the data.
final_iris <- iris
final_iris[,1:4] <- scale(final_iris[,1:4])Step II: SPlitting the data in training and testing data
set.seed(1234)
sample_index <- sample(nrow(final_iris), nrow(final_iris)*0.80)
iris_train <- final_iris[sample_index,]
iris_test <- final_iris[-sample_index,]Step III: Building the model for k = 10,15,20
iris_knn_test_1 <- knn(train = iris_train[,1:4], test = iris_test[,1:4], cl= as.factor(iris_train$Species), k=10)
iris_knn_test_2 <- knn(train = iris_train[,1:4], test = iris_test[,1:4], cl= as.factor(iris_train$Species), k=15)
iris_knn_test_3 <- knn(train = iris_train[,1:4], test = iris_test[,1:4], cl= as.factor(iris_train$Species), k=20)Step IV: Checking the results
test_1 <- CrossTable(x= iris_test$Species, y= iris_knn_test_1, prop.chisq = FALSE )##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 30
##
##
## | iris_knn_test_1
## iris_test$Species | setosa | versicolor | virginica | Row Total |
## ------------------|------------|------------|------------|------------|
## setosa | 10 | 0 | 0 | 10 |
## | 1.000 | 0.000 | 0.000 | 0.333 |
## | 1.000 | 0.000 | 0.000 | |
## | 0.333 | 0.000 | 0.000 | |
## ------------------|------------|------------|------------|------------|
## versicolor | 0 | 8 | 2 | 10 |
## | 0.000 | 0.800 | 0.200 | 0.333 |
## | 0.000 | 0.889 | 0.182 | |
## | 0.000 | 0.267 | 0.067 | |
## ------------------|------------|------------|------------|------------|
## virginica | 0 | 1 | 9 | 10 |
## | 0.000 | 0.100 | 0.900 | 0.333 |
## | 0.000 | 0.111 | 0.818 | |
## | 0.000 | 0.033 | 0.300 | |
## ------------------|------------|------------|------------|------------|
## Column Total | 10 | 9 | 11 | 30 |
## | 0.333 | 0.300 | 0.367 | |
## ------------------|------------|------------|------------|------------|
##
## test_2 <- CrossTable(x= iris_test$Species, y= iris_knn_test_2, prop.chisq = FALSE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 30
##
##
## | iris_knn_test_2
## iris_test$Species | setosa | versicolor | virginica | Row Total |
## ------------------|------------|------------|------------|------------|
## setosa | 10 | 0 | 0 | 10 |
## | 1.000 | 0.000 | 0.000 | 0.333 |
## | 1.000 | 0.000 | 0.000 | |
## | 0.333 | 0.000 | 0.000 | |
## ------------------|------------|------------|------------|------------|
## versicolor | 0 | 9 | 1 | 10 |
## | 0.000 | 0.900 | 0.100 | 0.333 |
## | 0.000 | 1.000 | 0.091 | |
## | 0.000 | 0.300 | 0.033 | |
## ------------------|------------|------------|------------|------------|
## virginica | 0 | 0 | 10 | 10 |
## | 0.000 | 0.000 | 1.000 | 0.333 |
## | 0.000 | 0.000 | 0.909 | |
## | 0.000 | 0.000 | 0.333 | |
## ------------------|------------|------------|------------|------------|
## Column Total | 10 | 9 | 11 | 30 |
## | 0.333 | 0.300 | 0.367 | |
## ------------------|------------|------------|------------|------------|
##
## test_3 <- CrossTable(x= iris_test$Species, y= iris_knn_test_3, prop.chisq = FALSE)##
##
## Cell Contents
## |-------------------------|
## | N |
## | N / Row Total |
## | N / Col Total |
## | N / Table Total |
## |-------------------------|
##
##
## Total Observations in Table: 30
##
##
## | iris_knn_test_3
## iris_test$Species | setosa | versicolor | virginica | Row Total |
## ------------------|------------|------------|------------|------------|
## setosa | 10 | 0 | 0 | 10 |
## | 1.000 | 0.000 | 0.000 | 0.333 |
## | 1.000 | 0.000 | 0.000 | |
## | 0.333 | 0.000 | 0.000 | |
## ------------------|------------|------------|------------|------------|
## versicolor | 0 | 9 | 1 | 10 |
## | 0.000 | 0.900 | 0.100 | 0.333 |
## | 0.000 | 0.900 | 0.100 | |
## | 0.000 | 0.300 | 0.033 | |
## ------------------|------------|------------|------------|------------|
## virginica | 0 | 1 | 9 | 10 |
## | 0.000 | 0.100 | 0.900 | 0.333 |
## | 0.000 | 0.100 | 0.900 | |
## | 0.000 | 0.033 | 0.300 | |
## ------------------|------------|------------|------------|------------|
## Column Total | 10 | 10 | 10 | 30 |
## | 0.333 | 0.333 | 0.333 | |
## ------------------|------------|------------|------------|------------|
##
## test_1 (k=10) gave 27/30 correct results
test_2 (k=15) gave 29/30 correct results
test_3 (k=20) gave 28/30 correct results