For the final project of this course I created a multivariate regression model to predict if an email is spam or not.
The email data set is a collection of emails from the open intro library. The emails are all from the gmail account of David Diez and were received in the first few months of 2012. There was a number of different variables that could be used to predict if emails are spam or not. I chose to work with this data because I wanted to use the processes I learned from the NSDM competiton on a different data set.
# Libraries
library(rmarkdown)
library(knitr)
library(openintro)## Please visit openintro.org for free statistics materials##
## Attaching package: 'openintro'## The following objects are masked from 'package:datasets':
##
## cars, treeslibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, unionlibrary(caret)## Loading required package: lattice##
## Attaching package: 'lattice'## The following object is masked from 'package:openintro':
##
## lsegments## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following object is masked from 'package:openintro':
##
## diamonds##
## Attaching package: 'caret'## The following object is masked from 'package:openintro':
##
## dotPlotlibrary(quanteda)## Package version: 1.2.0## Parallel computing: 2 of 4 threads used.## See https://quanteda.io for tutorials and examples.##
## Attaching package: 'quanteda'## The following object is masked from 'package:utils':
##
## Viewlibrary(e1071)
library(lattice)
library(ggplot2)email <- emailstr(email)## 'data.frame': 3921 obs. of 21 variables:
## $ spam : num 0 0 0 0 0 0 0 0 0 0 ...
## $ to_multiple : num 0 0 0 0 0 0 1 1 0 0 ...
## $ from : num 1 1 1 1 1 1 1 1 1 1 ...
## $ cc : int 0 0 0 0 0 0 0 1 0 0 ...
## $ sent_email : num 0 0 0 0 0 0 1 1 0 0 ...
## $ time : POSIXct, format: "2012-01-01 01:16:41" "2012-01-01 02:03:59" ...
## $ image : num 0 0 0 0 0 0 0 1 0 0 ...
## $ attach : num 0 0 0 0 0 0 0 1 0 0 ...
## $ dollar : num 0 0 4 0 0 0 0 0 0 0 ...
## $ winner : Factor w/ 2 levels "no","yes": 1 1 1 1 1 1 1 1 1 1 ...
## $ inherit : num 0 0 1 0 0 0 0 0 0 0 ...
## $ viagra : num 0 0 0 0 0 0 0 0 0 0 ...
## $ password : num 0 0 0 0 2 2 0 0 0 0 ...
## $ num_char : num 11.37 10.5 7.77 13.26 1.23 ...
## $ line_breaks : int 202 202 192 255 29 25 193 237 69 68 ...
## $ format : num 1 1 1 1 0 0 1 1 0 1 ...
## $ re_subj : num 0 0 0 0 0 0 0 0 0 0 ...
## $ exclaim_subj: num 0 0 0 0 0 0 0 0 0 0 ...
## $ urgent_subj : num 0 0 0 0 0 0 0 0 0 0 ...
## $ exclaim_mess: num 0 1 6 48 1 1 1 18 1 0 ...
## $ number : Factor w/ 3 levels "none","small",..: 3 2 2 2 1 1 3 2 2 2 ...scramble <- sample(1:3921)
email <- email[scramble,]train <- email[1:3000,]
test <- email[3001:3921,]train## spam to_multiple from cc sent_email time image attach
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## 720 0 0 0 1 smalltrain_features <- select(train, -spam, -time, -from, -number, -winner, -re_subj)
train_response <- as.factor(train$spam)
str(train_features)## 'data.frame': 3000 obs. of 15 variables:
## $ to_multiple : num 0 0 0 0 1 1 0 0 0 0 ...
## $ cc : int 0 0 1 0 0 0 0 0 0 0 ...
## $ sent_email : num 0 0 0 1 1 0 0 0 0 1 ...
## $ image : num 0 0 0 0 0 0 0 0 0 0 ...
## $ attach : num 2 0 0 0 1 1 0 0 0 0 ...
## $ dollar : num 0 9 0 0 0 0 0 0 0 0 ...
## $ inherit : num 0 0 0 0 0 0 0 0 0 0 ...
## $ viagra : num 0 0 0 0 0 0 0 0 0 0 ...
## $ password : num 0 0 0 0 0 0 0 0 0 0 ...
## $ num_char : num 0.424 10.105 22.971 8.512 5.834 ...
## $ line_breaks : int 22 243 408 136 127 41 23 626 648 32 ...
## $ format : num 0 1 1 1 1 0 0 1 1 1 ...
## $ exclaim_subj: num 0 0 1 0 0 0 0 0 0 0 ...
## $ urgent_subj : num 0 0 0 0 0 0 0 0 0 0 ...
## $ exclaim_mess: num 0 2 0 4 5 0 0 1 1 0 ...str(train_response)## Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...mod <- train(x = train_features, y = train_response, method = "xgbTree")preds <- predict(mod, test)
tbl <- table(preds, test$spam)
tbl##
## preds 0 1
## 0 813 48
## 1 21 39percentage <- function(tbl){
(tbl[1,1] + tbl[2,2]) / 921*100
}
percentage(tbl)## [1] 92.50814I used a multivariate regression model in order to find that result. More specifically the eXtreme Gradient Boosting tree from the caret library was used. The majority of the variables were used to predict the accuracy rating.
There was some of the variables that could have been good predictors, but I wasn’t able to use them in my model. That was because I wasn’t sure how to transform the data to be usable in the model.
This document was produced as a final project for MAT 143H - Introduction to Statistics (Honors) at North Shore Community College. The course was led by Professor Billy Jackson. Student Name: Matthew Carleton Semester: Spring 2018