This document provides an overview and examples of various machine learning models in R, including linear regression, decision trees, k-means clustering, k-nearest neighbors (KNN), support vector machines (SVM), naive Bayes, and neural networks. Code examples are given to demonstrate how to build models for predicting iris flower species using each algorithm and evaluate predictions against test data. Models are trained on standard iris, women's height and weight, and randomized iris datasets. Visualizations are also created to analyze model outputs.

1. Machine Learning in R
apolol92

2. Overview
Linear Regression Model
Decision Tree Model
K Means Clustering
K-Nearest Neighbour(knn)
Support Vector Machine(SVM)
Naive Bayes

3. Linear Regression Model
# women heights and weights
str(women)
# Create regression model to predict the weight of a given height
model <-lm(data = women, weight ~ height)
# Predict weight of height=50
predict(model,newdata=data.frame(heights=50))
plot(x=weights, y=heights) # Plot weights ~ heights
abline(model) # Plot regression line

4. Decision Tree Model
install.packages(“party”) # Install package party
library(“party”) # Use party
str(iris) # We will use the iris dataset
# Create the Decision Tree Model to predict species
iris_tree <- ctree(data = iris, Species~Sepal.Length+Sepal.Width+Petal.Length
+Petal.Width)
predict(iris,newdata=iris[1,1:4]) # Predict species of first iris in data frame
plot(iris_tree) # Plot the Decision Tree
print(iris_tree) # Print the Decision Tree

5. K Means Clustering
install.packages("clue") # Use clue package
library("clue")
model <- kmeans(iris[,1:2],3) # Create K Means Model
test_data <- data.frame(c(4,2)) # Create test data
cl_predict(model, test_data) # predict cluster of test data
# Just a way to plot clusters
plot(iris[,1:2],col=cl_predict(party,iris[,1:2]))

6. K-Nearest Neighbour(knn)
install.packages("kknn")
library("kknn")
my_iris <- iris[sample(150),] # Randomize iris data
train_data <- my_iris[1:100,-6] # Get some training data
test_data <- my_iris[101:150,1:4] # Get test data
# Create K-Nearest Neighbour model
my_knn <- kknn(formula = formula(Species~.), train = train_data,
test = test_data,k=7,distance=1)
predict(my_knn, test_data) #Predict Species

7. Support Vector Machines
install.packages('e1071')
library('e1071')
model <- svm(iris$Species~.,data=iris[,c(1,2,5)) # Create SVM-Model
plot(model,iris[, c(1, 2, 5)])
test_data <- data.frame(cbind(c(4,2)) # Create test data
colnames(test_data) <- c("Sepal.Width","Sepal.Length") # Set colnames
predict(model,test_data) # Predict species

8. Naive Bayes
install.packages(‘e1071’)
library(‘e1071’)
model <- naiveBayes(x=iris[,1:4],y=iris[,5]) # Create Naive Bayes Model
predict(model,iris[1,-5]) # Predict
species

9. Neural Network(MLP)
install.packages(‘RSNNS’)
library(RSNNS)
iris[,5] <- as.numeric(iris[,5]) # Cast factor to numeric
model <- mlp(iris[,-5],iris[,5]) # Create default MLP
predict(model,c(2,3,4,1)) # Predict species