Load the data. subscribers <- read.csv("C:/Users/Bhawna/Documents/blog/data/flowingdata_subscribers.csv", sep=",", header=TRUE) View(subscribers) Default plot with points. plot(subscribers$Subscribers) Default plot with type explicity specified. plot(subscribers$Subscribers, type="p", ylim=c(0, 30000)) Draw vertical lines. plot(subscribers$Subscribers, type="h", ylim=c(0, 30000)) Draw points with above lines. plot(subscribers$Subscribers, type="h", ylim=c(0, 30000)) points(subscribers$Subscribers) Edits with colors and labels. plot(subscribers$Subscribers, type="h", ylim=c(0, 30000), xlab="Day", ylab="Subscribers") points(subscribers$Subscribers, pch=19, col="blue") Reference:The data was downloaded from the following website website.
Perform the SMS spam filtering analysis. Step 1: Load data
Step 2: Exploring and preparing the data
read the sms data into the sms data frame
sms_raw <- read.csv("C:/Users/Bhawna/Documents/blog/data/sms_spam.csv", stringsAsFactors = FALSE)
examine the structure of the sms data
str(sms_raw)
'data.frame': 5559 obs. of 2 variables:
$ type: chr "ham" "ham" "ham" "spam" …
$ text: chr "Hope you are having a good week. Just checking in" "K.
The main purpose of the Support Vector Machine(SVM) method is to identify a hyperplane which separates the samples. This technique can be used for both classification and numerical prediction. Usually used for only binary classification. Benefits include high scalability, but drawbacks, as for neural nets, include models which are very difficult to interpret. Plot below help visualize overall way to visualize SVM technques.The hyperplane is a multidimensional surface, but it’s easiest to think of it as a straight line between two linearly separable groups of examples.
