Random Forest Assignment
Sonia Mathew
September 12, 2017
Introduction
Random forest algorithm is a supervised classification algorithm.As the name suggest, this algorithm creates the forest with a number of trees.
Problem Definition
Predict whether the Emails are spam or not Using Random Forest.
Data Location
The dataset can be found on
https://archive.ics.uci.edu/ml/datasets/spambase
Data Description
Number of Attributes: 58 (57 continuous, 1 nominal class label)
The last column of ‘spambase.data’ denotes whether the e-mail was considered spam (1) or not (0).
The run-length attributes (55-57) measure the length of sequences of consecutive capital letters.
Type word_freq_WORD = percentage of words in the e-mail that match WORD.
Type char_freq_CHAR= percentage of characters in the e-mail that match CHAR.
Continuous real [1,…] attribute of type capital_run_length_average= average length of uninterrupted sequences of capital letters. Continuous integer [1,…] attribute of type capital_run_length_longest= length of longest uninterrupted sequence of capital letters.
Continuous integer [1,…] attribute of type capital_run_length_total= sum of length of uninterrupted 8- sequences of capital letters= total number of capital letters in the e-mail.
Setup
Load Libs
library(plyr)
library(tidyr)
library(dplyr)
library(ggplot2)
library(caret)
library(randomForest)
library(gbm)
library(corrgram)Functions
detectNA <- function(inp) {
sum(is.na(inp))
}
detectCor <- function(x) {
cor(as.numeric(dfrDataset[, x]),
as.numeric(dfrDataset$status),
method="spearman")
}Load Dataset
dfrDataset <- read.csv("D://PGDM//Trim 4//MachineLearning//Data//spambase.csv", header=T, stringsAsFactors=T)
head(dfrDataset)## word_freq_make word_freq_address word_freq_all word_freq_3d
## 1 0.00 0.64 0.64 0
## 2 0.21 0.28 0.50 0
## 3 0.06 0.00 0.71 0
## 4 0.00 0.00 0.00 0
## 5 0.00 0.00 0.00 0
## 6 0.00 0.00 0.00 0
## word_freq_our word_freq_over word_freq_remove word_freq_internet
## 1 0.32 0.00 0.00 0.00
## 2 0.14 0.28 0.21 0.07
## 3 1.23 0.19 0.19 0.12
## 4 0.63 0.00 0.31 0.63
## 5 0.63 0.00 0.31 0.63
## 6 1.85 0.00 0.00 1.85
## word_freq_order word_freq_mail word_freq_receive word_freq_will
## 1 0.00 0.00 0.00 0.64
## 2 0.00 0.94 0.21 0.79
## 3 0.64 0.25 0.38 0.45
## 4 0.31 0.63 0.31 0.31
## 5 0.31 0.63 0.31 0.31
## 6 0.00 0.00 0.00 0.00
## word_freq_people word_freq_report word_freq_addresses word_freq_free
## 1 0.00 0.00 0.00 0.32
## 2 0.65 0.21 0.14 0.14
## 3 0.12 0.00 1.75 0.06
## 4 0.31 0.00 0.00 0.31
## 5 0.31 0.00 0.00 0.31
## 6 0.00 0.00 0.00 0.00
## word_freq_business word_freq_email word_freq_you word_freq_credit
## 1 0.00 1.29 1.93 0.00
## 2 0.07 0.28 3.47 0.00
## 3 0.06 1.03 1.36 0.32
## 4 0.00 0.00 3.18 0.00
## 5 0.00 0.00 3.18 0.00
## 6 0.00 0.00 0.00 0.00
## word_freq_your word_freq_font word_freq_000 word_freq_money word_freq_hp
## 1 0.96 0 0.00 0.00 0
## 2 1.59 0 0.43 0.43 0
## 3 0.51 0 1.16 0.06 0
## 4 0.31 0 0.00 0.00 0
## 5 0.31 0 0.00 0.00 0
## 6 0.00 0 0.00 0.00 0
## word_freq_hpl word_freq_george word_freq_650 word_freq_lab
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## word_freq_labs word_freq_telnet word_freq_857 word_freq_data
## 1 0 0 0 0
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## word_freq_415 word_freq_85 word_freq_technology word_freq_1999
## 1 0 0 0 0.00
## 2 0 0 0 0.07
## 3 0 0 0 0.00
## 4 0 0 0 0.00
## 5 0 0 0 0.00
## 6 0 0 0 0.00
## word_freq_parts word_freq_pm word_freq_direct word_freq_cs
## 1 0 0 0.00 0
## 2 0 0 0.00 0
## 3 0 0 0.06 0
## 4 0 0 0.00 0
## 5 0 0 0.00 0
## 6 0 0 0.00 0
## word_freq_meeting word_freq_original word_freq_project word_freq_re
## 1 0 0.00 0 0.00
## 2 0 0.00 0 0.00
## 3 0 0.12 0 0.06
## 4 0 0.00 0 0.00
## 5 0 0.00 0 0.00
## 6 0 0.00 0 0.00
## word_freq_edu word_freq_table word_freq_conference char_freq_.
## 1 0.00 0 0 0.00
## 2 0.00 0 0 0.00
## 3 0.06 0 0 0.01
## 4 0.00 0 0 0.00
## 5 0.00 0 0 0.00
## 6 0.00 0 0 0.00
## char_freq_..1 char_freq_..2 char_freq_..3 char_freq_..4 char_freq_..5
## 1 0.000 0 0.778 0.000 0.000
## 2 0.132 0 0.372 0.180 0.048
## 3 0.143 0 0.276 0.184 0.010
## 4 0.137 0 0.137 0.000 0.000
## 5 0.135 0 0.135 0.000 0.000
## 6 0.223 0 0.000 0.000 0.000
## capital_run_length_average capital_run_length_longest
## 1 3.756 61
## 2 5.114 101
## 3 9.821 485
## 4 3.537 40
## 5 3.537 40
## 6 3.000 15
## capital_run_length_total status
## 1 278 1
## 2 1028 1
## 3 2259 1
## 4 191 1
## 5 191 1
## 6 54 1Dataframe Stucture
str(dfrDataset)## 'data.frame': 4601 obs. of 58 variables:
## $ word_freq_make : num 0 0.21 0.06 0 0 0 0 0 0.15 0.06 ...
## $ word_freq_address : num 0.64 0.28 0 0 0 0 0 0 0 0.12 ...
## $ word_freq_all : num 0.64 0.5 0.71 0 0 0 0 0 0.46 0.77 ...
## $ word_freq_3d : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_our : num 0.32 0.14 1.23 0.63 0.63 1.85 1.92 1.88 0.61 0.19 ...
## $ word_freq_over : num 0 0.28 0.19 0 0 0 0 0 0 0.32 ...
## $ word_freq_remove : num 0 0.21 0.19 0.31 0.31 0 0 0 0.3 0.38 ...
## $ word_freq_internet : num 0 0.07 0.12 0.63 0.63 1.85 0 1.88 0 0 ...
## $ word_freq_order : num 0 0 0.64 0.31 0.31 0 0 0 0.92 0.06 ...
## $ word_freq_mail : num 0 0.94 0.25 0.63 0.63 0 0.64 0 0.76 0 ...
## $ word_freq_receive : num 0 0.21 0.38 0.31 0.31 0 0.96 0 0.76 0 ...
## $ word_freq_will : num 0.64 0.79 0.45 0.31 0.31 0 1.28 0 0.92 0.64 ...
## $ word_freq_people : num 0 0.65 0.12 0.31 0.31 0 0 0 0 0.25 ...
## $ word_freq_report : num 0 0.21 0 0 0 0 0 0 0 0 ...
## $ word_freq_addresses : num 0 0.14 1.75 0 0 0 0 0 0 0.12 ...
## $ word_freq_free : num 0.32 0.14 0.06 0.31 0.31 0 0.96 0 0 0 ...
## $ word_freq_business : num 0 0.07 0.06 0 0 0 0 0 0 0 ...
## $ word_freq_email : num 1.29 0.28 1.03 0 0 0 0.32 0 0.15 0.12 ...
## $ word_freq_you : num 1.93 3.47 1.36 3.18 3.18 0 3.85 0 1.23 1.67 ...
## $ word_freq_credit : num 0 0 0.32 0 0 0 0 0 3.53 0.06 ...
## $ word_freq_your : num 0.96 1.59 0.51 0.31 0.31 0 0.64 0 2 0.71 ...
## $ word_freq_font : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_000 : num 0 0.43 1.16 0 0 0 0 0 0 0.19 ...
## $ word_freq_money : num 0 0.43 0.06 0 0 0 0 0 0.15 0 ...
## $ word_freq_hp : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_hpl : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_george : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_650 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_lab : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_labs : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_telnet : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_857 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_data : num 0 0 0 0 0 0 0 0 0.15 0 ...
## $ word_freq_415 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_85 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_technology : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_1999 : num 0 0.07 0 0 0 0 0 0 0 0 ...
## $ word_freq_parts : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_pm : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_direct : num 0 0 0.06 0 0 0 0 0 0 0 ...
## $ word_freq_cs : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_meeting : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_original : num 0 0 0.12 0 0 0 0 0 0.3 0 ...
## $ word_freq_project : num 0 0 0 0 0 0 0 0 0 0.06 ...
## $ word_freq_re : num 0 0 0.06 0 0 0 0 0 0 0 ...
## $ word_freq_edu : num 0 0 0.06 0 0 0 0 0 0 0 ...
## $ word_freq_table : num 0 0 0 0 0 0 0 0 0 0 ...
## $ word_freq_conference : num 0 0 0 0 0 0 0 0 0 0 ...
## $ char_freq_. : num 0 0 0.01 0 0 0 0 0 0 0.04 ...
## $ char_freq_..1 : num 0 0.132 0.143 0.137 0.135 0.223 0.054 0.206 0.271 0.03 ...
## $ char_freq_..2 : num 0 0 0 0 0 0 0 0 0 0 ...
## $ char_freq_..3 : num 0.778 0.372 0.276 0.137 0.135 0 0.164 0 0.181 0.244 ...
## $ char_freq_..4 : num 0 0.18 0.184 0 0 0 0.054 0 0.203 0.081 ...
## $ char_freq_..5 : num 0 0.048 0.01 0 0 0 0 0 0.022 0 ...
## $ capital_run_length_average: num 3.76 5.11 9.82 3.54 3.54 ...
## $ capital_run_length_longest: int 61 101 485 40 40 15 4 11 445 43 ...
## $ capital_run_length_total : int 278 1028 2259 191 191 54 112 49 1257 749 ...
## $ status : int 1 1 1 1 1 1 1 1 1 1 ...Dataframe Summary
lapply(dfrDataset, FUN=summary)## $word_freq_make
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1046 0.0000 4.5400
##
## $word_freq_address
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.213 0.000 14.280
##
## $word_freq_all
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2807 0.4200 5.1000
##
## $word_freq_3d
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06542 0.00000 42.81000
##
## $word_freq_our
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3122 0.3800 10.0000
##
## $word_freq_over
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0959 0.0000 5.8800
##
## $word_freq_remove
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1142 0.0000 7.2700
##
## $word_freq_internet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1053 0.0000 11.1100
##
## $word_freq_order
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09007 0.00000 5.26000
##
## $word_freq_mail
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2394 0.1600 18.1800
##
## $word_freq_receive
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05982 0.00000 2.61000
##
## $word_freq_will
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.1000 0.5417 0.8000 9.6700
##
## $word_freq_people
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09393 0.00000 5.55000
##
## $word_freq_report
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05863 0.00000 10.00000
##
## $word_freq_addresses
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0492 0.0000 4.4100
##
## $word_freq_free
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2488 0.1000 20.0000
##
## $word_freq_business
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1426 0.0000 7.1400
##
## $word_freq_email
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1847 0.0000 9.0900
##
## $word_freq_you
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.310 1.662 2.640 18.750
##
## $word_freq_credit
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08558 0.00000 18.18000
##
## $word_freq_your
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.2200 0.8098 1.2700 11.1100
##
## $word_freq_font
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1212 0.0000 17.1000
##
## $word_freq_000
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1016 0.0000 5.4500
##
## $word_freq_money
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09427 0.00000 12.50000
##
## $word_freq_hp
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.5495 0.0000 20.8300
##
## $word_freq_hpl
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2654 0.0000 16.6600
##
## $word_freq_george
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.7673 0.0000 33.3300
##
## $word_freq_650
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1248 0.0000 9.0900
##
## $word_freq_lab
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09892 0.00000 14.28000
##
## $word_freq_labs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1029 0.0000 5.8800
##
## $word_freq_telnet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06475 0.00000 12.50000
##
## $word_freq_857
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04705 0.00000 4.76000
##
## $word_freq_data
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09723 0.00000 18.18000
##
## $word_freq_415
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04784 0.00000 4.76000
##
## $word_freq_85
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1054 0.0000 20.0000
##
## $word_freq_technology
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09748 0.00000 7.69000
##
## $word_freq_1999
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.137 0.000 6.890
##
## $word_freq_parts
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0132 0.0000 8.3300
##
## $word_freq_pm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07863 0.00000 11.11000
##
## $word_freq_direct
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06483 0.00000 4.76000
##
## $word_freq_cs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04367 0.00000 7.14000
##
## $word_freq_meeting
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1323 0.0000 14.2800
##
## $word_freq_original
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0461 0.0000 3.5700
##
## $word_freq_project
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0792 0.0000 20.0000
##
## $word_freq_re
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3012 0.1100 21.4200
##
## $word_freq_edu
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1798 0.0000 22.0500
##
## $word_freq_table
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.000000 0.000000 0.005444 0.000000 2.170000
##
## $word_freq_conference
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03187 0.00000 10.00000
##
## $char_freq_.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03857 0.00000 4.38500
##
## $char_freq_..1
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.065 0.139 0.188 9.752
##
## $char_freq_..2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.01698 0.00000 4.08100
##
## $char_freq_..3
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2691 0.3150 32.4800
##
## $char_freq_..4
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07581 0.05200 6.00300
##
## $char_freq_..5
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04424 0.00000 19.83000
##
## $capital_run_length_average
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.588 2.276 5.192 3.706 1102.000
##
## $capital_run_length_longest
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 6.00 15.00 52.17 43.00 9989.00
##
## $capital_run_length_total
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 35.0 95.0 283.3 266.0 15840.0
##
## $status
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.394 1.000 1.000Missing Data
lapply(dfrDataset, FUN=detectNA)## $word_freq_make
## [1] 0
##
## $word_freq_address
## [1] 0
##
## $word_freq_all
## [1] 0
##
## $word_freq_3d
## [1] 0
##
## $word_freq_our
## [1] 0
##
## $word_freq_over
## [1] 0
##
## $word_freq_remove
## [1] 0
##
## $word_freq_internet
## [1] 0
##
## $word_freq_order
## [1] 0
##
## $word_freq_mail
## [1] 0
##
## $word_freq_receive
## [1] 0
##
## $word_freq_will
## [1] 0
##
## $word_freq_people
## [1] 0
##
## $word_freq_report
## [1] 0
##
## $word_freq_addresses
## [1] 0
##
## $word_freq_free
## [1] 0
##
## $word_freq_business
## [1] 0
##
## $word_freq_email
## [1] 0
##
## $word_freq_you
## [1] 0
##
## $word_freq_credit
## [1] 0
##
## $word_freq_your
## [1] 0
##
## $word_freq_font
## [1] 0
##
## $word_freq_000
## [1] 0
##
## $word_freq_money
## [1] 0
##
## $word_freq_hp
## [1] 0
##
## $word_freq_hpl
## [1] 0
##
## $word_freq_george
## [1] 0
##
## $word_freq_650
## [1] 0
##
## $word_freq_lab
## [1] 0
##
## $word_freq_labs
## [1] 0
##
## $word_freq_telnet
## [1] 0
##
## $word_freq_857
## [1] 0
##
## $word_freq_data
## [1] 0
##
## $word_freq_415
## [1] 0
##
## $word_freq_85
## [1] 0
##
## $word_freq_technology
## [1] 0
##
## $word_freq_1999
## [1] 0
##
## $word_freq_parts
## [1] 0
##
## $word_freq_pm
## [1] 0
##
## $word_freq_direct
## [1] 0
##
## $word_freq_cs
## [1] 0
##
## $word_freq_meeting
## [1] 0
##
## $word_freq_original
## [1] 0
##
## $word_freq_project
## [1] 0
##
## $word_freq_re
## [1] 0
##
## $word_freq_edu
## [1] 0
##
## $word_freq_table
## [1] 0
##
## $word_freq_conference
## [1] 0
##
## $char_freq_.
## [1] 0
##
## $char_freq_..1
## [1] 0
##
## $char_freq_..2
## [1] 0
##
## $char_freq_..3
## [1] 0
##
## $char_freq_..4
## [1] 0
##
## $char_freq_..5
## [1] 0
##
## $capital_run_length_average
## [1] 0
##
## $capital_run_length_longest
## [1] 0
##
## $capital_run_length_total
## [1] 0
##
## $status
## [1] 0Check output
dfrStatusFreq <- summarise(group_by(dfrDataset, status), count=n())
ggplot(dfrStatusFreq, aes(x=status, y=count)) +
geom_bar(stat="identity", aes(fill=count)) +
labs(title="Status Frequency Distribution") +
labs(x="Status") +
labs(y="Counts")
Find Corelations
vcnCorsData <- abs(sapply(colnames(dfrDataset), detectCor))
summary(vcnCorsData)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.002525 0.148800 0.253300 0.276900 0.354700 1.000000Show Corelations
vcnCorsData## word_freq_make word_freq_address
## 0.24069974 0.29750940
## word_freq_all word_freq_3d
## 0.33283147 0.09077776
## word_freq_our word_freq_over
## 0.40913946 0.31864550
## word_freq_remove word_freq_internet
## 0.51877779 0.34379623
## word_freq_order word_freq_mail
## 0.30073703 0.29682394
## word_freq_receive word_freq_will
## 0.35496682 0.14847653
## word_freq_people word_freq_report
## 0.21287588 0.14977533
## word_freq_addresses word_freq_free
## 0.26515743 0.50416922
## word_freq_business word_freq_email
## 0.35290749 0.29909391
## word_freq_you word_freq_credit
## 0.36110406 0.32418657
## word_freq_your word_freq_font
## 0.50159062 0.13797471
## word_freq_000 word_freq_money
## 0.42580256 0.47215455
## word_freq_hp word_freq_hpl
## 0.39981558 0.34188069
## word_freq_george word_freq_650
## 0.35393063 0.22619064
## word_freq_lab word_freq_labs
## 0.22068802 0.24580530
## word_freq_telnet word_freq_857
## 0.20467400 0.16983798
## word_freq_data word_freq_415
## 0.15756347 0.15802818
## word_freq_85 word_freq_technology
## 0.21413087 0.16680254
## word_freq_1999 word_freq_parts
## 0.26070752 0.00252536
## word_freq_pm word_freq_direct
## 0.14721389 0.02813193
## word_freq_cs word_freq_meeting
## 0.14453750 0.19574176
## word_freq_original word_freq_project
## 0.10781412 0.14453744
## word_freq_re word_freq_edu
## 0.07176763 0.19702549
## word_freq_table word_freq_conference
## 0.02266674 0.13903044
## char_freq_. char_freq_..1
## 0.05683530 0.03263555
## char_freq_..2 char_freq_..3
## 0.11122690 0.59785363
## char_freq_..4 char_freq_..5
## 0.56563314 0.26668614
## capital_run_length_average capital_run_length_longest
## 0.48794983 0.51515693
## capital_run_length_total status
## 0.44397367 1.00000000Plot Corelations
corrgram(dfrDataset)
High Corelations
vcnCorsData[vcnCorsData>0.6]## status
## 1Create Column spam
dfrDataset <- mutate(dfrDataset, Result= ifelse(dfrDataset$status >0,'Spam','Not Spam'))
dfrDataset$Result <- as.factor(dfrDataset$Result)
table(dfrDataset$Result)##
## Not Spam Spam
## 2788 1813Dataset Split
set.seed(707)
vctTrnRecs <- createDataPartition(y=dfrDataset$status, p=0.7, list=FALSE)
dfrTrnData <- dfrDataset[vctTrnRecs,]
dfrTstData <- dfrDataset[-vctTrnRecs,]Training Dataset RowCount & ColCount
dim(dfrTrnData)## [1] 3221 59Testing Dataset RowCount & ColCount
dim(dfrTstData)## [1] 1380 59Training Dataset Head
head(dfrTrnData)## word_freq_make word_freq_address word_freq_all word_freq_3d
## 2 0.21 0.28 0.50 0
## 3 0.06 0.00 0.71 0
## 4 0.00 0.00 0.00 0
## 10 0.06 0.12 0.77 0
## 11 0.00 0.00 0.00 0
## 12 0.00 0.00 0.25 0
## word_freq_our word_freq_over word_freq_remove word_freq_internet
## 2 0.14 0.28 0.21 0.07
## 3 1.23 0.19 0.19 0.12
## 4 0.63 0.00 0.31 0.63
## 10 0.19 0.32 0.38 0.00
## 11 0.00 0.00 0.96 0.00
## 12 0.38 0.25 0.25 0.00
## word_freq_order word_freq_mail word_freq_receive word_freq_will
## 2 0.00 0.94 0.21 0.79
## 3 0.64 0.25 0.38 0.45
## 4 0.31 0.63 0.31 0.31
## 10 0.06 0.00 0.00 0.64
## 11 0.00 1.92 0.96 0.00
## 12 0.00 0.00 0.12 0.12
## word_freq_people word_freq_report word_freq_addresses word_freq_free
## 2 0.65 0.21 0.14 0.14
## 3 0.12 0.00 1.75 0.06
## 4 0.31 0.00 0.00 0.31
## 10 0.25 0.00 0.12 0.00
## 11 0.00 0.00 0.00 0.00
## 12 0.12 0.00 0.00 0.00
## word_freq_business word_freq_email word_freq_you word_freq_credit
## 2 0.07 0.28 3.47 0.00
## 3 0.06 1.03 1.36 0.32
## 4 0.00 0.00 3.18 0.00
## 10 0.00 0.12 1.67 0.06
## 11 0.00 0.96 3.84 0.00
## 12 0.00 0.00 1.16 0.00
## word_freq_your word_freq_font word_freq_000 word_freq_money
## 2 1.59 0 0.43 0.43
## 3 0.51 0 1.16 0.06
## 4 0.31 0 0.00 0.00
## 10 0.71 0 0.19 0.00
## 11 0.96 0 0.00 0.00
## 12 0.77 0 0.00 0.00
## word_freq_hp word_freq_hpl word_freq_george word_freq_650 word_freq_lab
## 2 0 0 0 0 0
## 3 0 0 0 0 0
## 4 0 0 0 0 0
## 10 0 0 0 0 0
## 11 0 0 0 0 0
## 12 0 0 0 0 0
## word_freq_labs word_freq_telnet word_freq_857 word_freq_data
## 2 0 0 0 0
## 3 0 0 0 0
## 4 0 0 0 0
## 10 0 0 0 0
## 11 0 0 0 0
## 12 0 0 0 0
## word_freq_415 word_freq_85 word_freq_technology word_freq_1999
## 2 0 0 0 0.07
## 3 0 0 0 0.00
## 4 0 0 0 0.00
## 10 0 0 0 0.00
## 11 0 0 0 0.00
## 12 0 0 0 0.00
## word_freq_parts word_freq_pm word_freq_direct word_freq_cs
## 2 0 0 0.00 0
## 3 0 0 0.06 0
## 4 0 0 0.00 0
## 10 0 0 0.00 0
## 11 0 0 0.96 0
## 12 0 0 0.00 0
## word_freq_meeting word_freq_original word_freq_project word_freq_re
## 2 0 0.00 0.00 0.00
## 3 0 0.12 0.00 0.06
## 4 0 0.00 0.00 0.00
## 10 0 0.00 0.06 0.00
## 11 0 0.00 0.00 0.00
## 12 0 0.00 0.00 0.00
## word_freq_edu word_freq_table word_freq_conference char_freq_.
## 2 0.00 0 0 0.000
## 3 0.06 0 0 0.010
## 4 0.00 0 0 0.000
## 10 0.00 0 0 0.040
## 11 0.00 0 0 0.000
## 12 0.00 0 0 0.022
## char_freq_..1 char_freq_..2 char_freq_..3 char_freq_..4 char_freq_..5
## 2 0.132 0 0.372 0.180 0.048
## 3 0.143 0 0.276 0.184 0.010
## 4 0.137 0 0.137 0.000 0.000
## 10 0.030 0 0.244 0.081 0.000
## 11 0.000 0 0.462 0.000 0.000
## 12 0.044 0 0.663 0.000 0.000
## capital_run_length_average capital_run_length_longest
## 2 5.114 101
## 3 9.821 485
## 4 3.537 40
## 10 1.729 43
## 11 1.312 6
## 12 1.243 11
## capital_run_length_total status Result
## 2 1028 1 Spam
## 3 2259 1 Spam
## 4 191 1 Spam
## 10 749 1 Spam
## 11 21 1 Spam
## 12 184 1 SpamTesting Dataset Head
head(dfrTstData)## word_freq_make word_freq_address word_freq_all word_freq_3d
## 1 0.00 0.64 0.64 0
## 5 0.00 0.00 0.00 0
## 6 0.00 0.00 0.00 0
## 7 0.00 0.00 0.00 0
## 8 0.00 0.00 0.00 0
## 9 0.15 0.00 0.46 0
## word_freq_our word_freq_over word_freq_remove word_freq_internet
## 1 0.32 0 0.00 0.00
## 5 0.63 0 0.31 0.63
## 6 1.85 0 0.00 1.85
## 7 1.92 0 0.00 0.00
## 8 1.88 0 0.00 1.88
## 9 0.61 0 0.30 0.00
## word_freq_order word_freq_mail word_freq_receive word_freq_will
## 1 0.00 0.00 0.00 0.64
## 5 0.31 0.63 0.31 0.31
## 6 0.00 0.00 0.00 0.00
## 7 0.00 0.64 0.96 1.28
## 8 0.00 0.00 0.00 0.00
## 9 0.92 0.76 0.76 0.92
## word_freq_people word_freq_report word_freq_addresses word_freq_free
## 1 0.00 0 0 0.32
## 5 0.31 0 0 0.31
## 6 0.00 0 0 0.00
## 7 0.00 0 0 0.96
## 8 0.00 0 0 0.00
## 9 0.00 0 0 0.00
## word_freq_business word_freq_email word_freq_you word_freq_credit
## 1 0 1.29 1.93 0.00
## 5 0 0.00 3.18 0.00
## 6 0 0.00 0.00 0.00
## 7 0 0.32 3.85 0.00
## 8 0 0.00 0.00 0.00
## 9 0 0.15 1.23 3.53
## word_freq_your word_freq_font word_freq_000 word_freq_money word_freq_hp
## 1 0.96 0 0 0.00 0
## 5 0.31 0 0 0.00 0
## 6 0.00 0 0 0.00 0
## 7 0.64 0 0 0.00 0
## 8 0.00 0 0 0.00 0
## 9 2.00 0 0 0.15 0
## word_freq_hpl word_freq_george word_freq_650 word_freq_lab
## 1 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## word_freq_labs word_freq_telnet word_freq_857 word_freq_data
## 1 0 0 0 0.00
## 5 0 0 0 0.00
## 6 0 0 0 0.00
## 7 0 0 0 0.00
## 8 0 0 0 0.00
## 9 0 0 0 0.15
## word_freq_415 word_freq_85 word_freq_technology word_freq_1999
## 1 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## word_freq_parts word_freq_pm word_freq_direct word_freq_cs
## 1 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## word_freq_meeting word_freq_original word_freq_project word_freq_re
## 1 0 0.0 0 0
## 5 0 0.0 0 0
## 6 0 0.0 0 0
## 7 0 0.0 0 0
## 8 0 0.0 0 0
## 9 0 0.3 0 0
## word_freq_edu word_freq_table word_freq_conference char_freq_.
## 1 0 0 0 0
## 5 0 0 0 0
## 6 0 0 0 0
## 7 0 0 0 0
## 8 0 0 0 0
## 9 0 0 0 0
## char_freq_..1 char_freq_..2 char_freq_..3 char_freq_..4 char_freq_..5
## 1 0.000 0 0.778 0.000 0.000
## 5 0.135 0 0.135 0.000 0.000
## 6 0.223 0 0.000 0.000 0.000
## 7 0.054 0 0.164 0.054 0.000
## 8 0.206 0 0.000 0.000 0.000
## 9 0.271 0 0.181 0.203 0.022
## capital_run_length_average capital_run_length_longest
## 1 3.756 61
## 5 3.537 40
## 6 3.000 15
## 7 1.671 4
## 8 2.450 11
## 9 9.744 445
## capital_run_length_total status Result
## 1 278 1 Spam
## 5 191 1 Spam
## 6 54 1 Spam
## 7 112 1 Spam
## 8 49 1 Spam
## 9 1257 1 SpamTraining Dataset Summary
lapply(dfrTrnData, FUN=summary)## $word_freq_make
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1001 0.0000 4.5400
##
## $word_freq_address
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1901 0.0000 14.2800
##
## $word_freq_all
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.279 0.400 5.100
##
## $word_freq_3d
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05219 0.00000 42.81000
##
## $word_freq_our
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3027 0.3600 10.0000
##
## $word_freq_over
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09372 0.00000 2.63000
##
## $word_freq_remove
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1146 0.0000 7.2700
##
## $word_freq_internet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.112 0.000 11.110
##
## $word_freq_order
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08978 0.00000 5.26000
##
## $word_freq_mail
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2288 0.1300 18.1800
##
## $word_freq_receive
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05888 0.00000 2.61000
##
## $word_freq_will
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.1000 0.5349 0.7700 7.6900
##
## $word_freq_people
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09223 0.00000 5.55000
##
## $word_freq_report
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05904 0.00000 10.00000
##
## $word_freq_addresses
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04824 0.00000 4.41000
##
## $word_freq_free
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2464 0.1000 16.6600
##
## $word_freq_business
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.138 0.000 7.140
##
## $word_freq_email
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1818 0.0000 9.0900
##
## $word_freq_you
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.290 1.671 2.660 18.750
##
## $word_freq_credit
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08912 0.00000 18.18000
##
## $word_freq_your
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.2000 0.7921 1.2700 11.1100
##
## $word_freq_font
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1223 0.0000 15.4300
##
## $word_freq_000
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1051 0.0000 5.4500
##
## $word_freq_money
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09648 0.00000 12.50000
##
## $word_freq_hp
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.5416 0.0000 20.8300
##
## $word_freq_hpl
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2673 0.0000 10.8600
##
## $word_freq_george
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.7712 0.0000 33.3300
##
## $word_freq_650
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1322 0.0000 9.0900
##
## $word_freq_lab
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09695 0.00000 14.28000
##
## $word_freq_labs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1046 0.0000 5.8800
##
## $word_freq_telnet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06896 0.00000 12.50000
##
## $word_freq_857
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04833 0.00000 4.76000
##
## $word_freq_data
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1035 0.0000 18.1800
##
## $word_freq_415
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04945 0.00000 4.76000
##
## $word_freq_85
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1092 0.0000 20.0000
##
## $word_freq_technology
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1037 0.0000 7.6900
##
## $word_freq_1999
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1381 0.0000 5.0500
##
## $word_freq_parts
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.01352 0.00000 8.33000
##
## $word_freq_pm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07996 0.00000 9.75000
##
## $word_freq_direct
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06304 0.00000 4.76000
##
## $word_freq_cs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04513 0.00000 7.14000
##
## $word_freq_meeting
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1367 0.0000 14.2800
##
## $word_freq_original
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04738 0.00000 3.57000
##
## $word_freq_project
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08829 0.00000 20.00000
##
## $word_freq_re
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2993 0.1200 20.0000
##
## $word_freq_edu
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1828 0.0000 22.0500
##
## $word_freq_table
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.000000 0.000000 0.004713 0.000000 1.910000
##
## $word_freq_conference
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03038 0.00000 10.00000
##
## $char_freq_.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04054 0.00000 4.38500
##
## $char_freq_..1
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0660 0.1399 0.1890 5.2770
##
## $char_freq_..2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.01506 0.00000 4.08100
##
## $char_freq_..3
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2741 0.3110 32.4800
##
## $char_freq_..4
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07403 0.04800 5.30000
##
## $char_freq_..5
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03887 0.00000 13.13000
##
## $capital_run_length_average
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.564 2.250 5.280 3.706 1102.000
##
## $capital_run_length_longest
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 6.00 14.00 49.71 43.00 2204.00
##
## $capital_run_length_total
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1 35 94 282 268 15840
##
## $status
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3971 1.0000 1.0000
##
## $Result
## Not Spam Spam
## 1942 1279Testing Dataset Summary
lapply(dfrTstData, FUN=summary)## $word_freq_make
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.115 0.000 4.000
##
## $word_freq_address
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2666 0.0000 14.2800
##
## $word_freq_all
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2844 0.4500 4.5400
##
## $word_freq_3d
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09632 0.00000 40.13000
##
## $word_freq_our
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3346 0.4325 7.1400
##
## $word_freq_over
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.101 0.000 5.880
##
## $word_freq_remove
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1133 0.0000 7.2700
##
## $word_freq_internet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08964 0.00000 4.62000
##
## $word_freq_order
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09074 0.00000 3.33000
##
## $word_freq_mail
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2642 0.2200 11.1100
##
## $word_freq_receive
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06204 0.00000 2.00000
##
## $word_freq_will
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.1200 0.5576 0.8500 9.6700
##
## $word_freq_people
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09789 0.00000 5.55000
##
## $word_freq_report
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05767 0.00000 5.55000
##
## $word_freq_addresses
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05145 0.00000 2.31000
##
## $word_freq_free
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2546 0.1125 20.0000
##
## $word_freq_business
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1532 0.0000 4.8700
##
## $word_freq_email
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1917 0.0000 4.5100
##
## $word_freq_you
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 1.330 1.641 2.590 14.280
##
## $word_freq_credit
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07731 0.00000 6.25000
##
## $word_freq_your
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.2700 0.8509 1.2800 10.7100
##
## $word_freq_font
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1186 0.0000 17.1000
##
## $word_freq_000
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0937 0.0000 3.3800
##
## $word_freq_money
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.0891 0.0000 5.9800
##
## $word_freq_hp
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.5679 0.0000 20.0000
##
## $word_freq_hpl
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2609 0.0000 16.6600
##
## $word_freq_george
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.7582 0.0000 33.3300
##
## $word_freq_650
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1078 0.0000 4.7600
##
## $word_freq_lab
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1035 0.0000 10.0000
##
## $word_freq_labs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09869 0.00000 4.76000
##
## $word_freq_telnet
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05493 0.00000 4.76000
##
## $word_freq_857
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04405 0.00000 4.76000
##
## $word_freq_data
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08264 0.00000 8.33000
##
## $word_freq_415
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04407 0.00000 4.76000
##
## $word_freq_85
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.09651 0.00000 4.76000
##
## $word_freq_technology
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.08303 0.00000 4.76000
##
## $word_freq_1999
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1344 0.0000 6.8900
##
## $word_freq_parts
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.01246 0.00000 7.40000
##
## $word_freq_pm
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07551 0.00000 11.11000
##
## $word_freq_direct
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.06901 0.00000 4.76000
##
## $word_freq_cs
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04025 0.00000 4.75000
##
## $word_freq_meeting
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1221 0.0000 7.6900
##
## $word_freq_original
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.04311 0.00000 3.44000
##
## $word_freq_project
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05797 0.00000 4.54000
##
## $word_freq_re
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.3056 0.0800 21.4200
##
## $word_freq_edu
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.1729 0.0000 9.5200
##
## $word_freq_table
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000000 0.000000 0.000000 0.007152 0.000000 2.170000
##
## $word_freq_conference
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03534 0.00000 8.33000
##
## $char_freq_.
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.03398 0.00000 4.18700
##
## $char_freq_..1
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0600 0.1371 0.1772 9.7520
##
## $char_freq_..2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.02145 0.00000 2.77700
##
## $char_freq_..3
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.0000 0.0000 0.0000 0.2573 0.3302 5.8440
##
## $char_freq_..4
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.07996 0.06225 6.00300
##
## $char_freq_..5
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.00000 0.00000 0.00000 0.05676 0.00000 19.83000
##
## $capital_run_length_average
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.000 1.630 2.318 4.984 3.706 443.700
##
## $capital_run_length_longest
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.00 6.00 15.00 57.92 45.00 9989.00
##
## $capital_run_length_total
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1.0 35.0 95.5 286.3 259.2 10060.0
##
## $status
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.000 0.000 0.000 0.387 1.000 1.000
##
## $Result
## Not Spam Spam
## 846 534Random Forest Approach
Create Model - Random Forest (Default)
## set seed
set.seed(707)
# mtry
myMtry=sqrt(ncol(dfrTrnData)-2)
myNtrees=500
# start time
vctProcStrt <- proc.time()
#Proc.time gives statistics of the process.
# random forest (default)
mdlRndForDef <- randomForest(Result~.-status, data=dfrTrnData,
mtry=myMtry, ntree=myNtrees)
# end time
vctProcEnds <- proc.time()
# print
print(paste("Model Created ...",vctProcEnds[1] - vctProcStrt[1]))## [1] "Model Created ... 21.72"View Model - Default Random Forest
mdlRndForDef##
## Call:
## randomForest(formula = Result ~ . - status, data = dfrTrnData, mtry = myMtry, ntree = myNtrees)
## Type of random forest: classification
## Number of trees: 500
## No. of variables tried at each split: 8
##
## OOB estimate of error rate: 4.84%
## Confusion matrix:
## Not Spam Spam class.error
## Not Spam 1883 59 0.03038105
## Spam 97 1182 0.07584050View Model Summary - Default Random Forest
summary(mdlRndForDef)## Length Class Mode
## call 5 -none- call
## type 1 -none- character
## predicted 3221 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 6442 matrix numeric
## oob.times 3221 -none- numeric
## classes 2 -none- character
## importance 57 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 3221 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## terms 3 terms callPrediction - Test Data - Random Forest (Default)
vctRndForDef <- predict(mdlRndForDef, newdata=dfrTstData)
cmxRndForDef <- confusionMatrix(vctRndForDef, dfrTstData$Result)
cmxRndForDef## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Spam Spam
## Not Spam 807 28
## Spam 39 506
##
## Accuracy : 0.9514
## 95% CI : (0.9387, 0.9622)
## No Information Rate : 0.613
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.8981
## Mcnemar's Test P-Value : 0.2218
##
## Sensitivity : 0.9539
## Specificity : 0.9476
## Pos Pred Value : 0.9665
## Neg Pred Value : 0.9284
## Prevalence : 0.6130
## Detection Rate : 0.5848
## Detection Prevalence : 0.6051
## Balanced Accuracy : 0.9507
##
## 'Positive' Class : Not Spam
## Create Model - Random Forest (RFM)
## set seed
set.seed(707)
# start time
vctProcStrt <- proc.time()
# random forest (default)
myControl <- trainControl(method="cv", number=10, repeats=3)
myMetric <- "Accuracy"
myMtry <- sqrt(ncol(dfrTrnData)-2)
myNtrees <- 500
myTuneGrid <- expand.grid(.mtry=myMtry)
mdlRndForRfm <- train(Result~.-status, data=dfrTrnData, method="rf",
verbose=F, metric=myMetric, trControl=myControl,
tuneGrid=myTuneGrid, ntree=myNtrees)
# end time
vctProcEnds <- proc.time()
# print
print(paste("Model Created ...",vctProcEnds[1] - vctProcStrt[1]))## [1] "Model Created ... 190.11"View Model - Random Forest (RFM)
mdlRndForRfm## Random Forest
##
## 3221 samples
## 58 predictor
## 2 classes: 'Not Spam', 'Spam'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold)
## Summary of sample sizes: 2898, 2899, 2900, 2898, 2899, 2899, ...
## Resampling results:
##
## Accuracy Kappa
## 0.9515719 0.8982131
##
## Tuning parameter 'mtry' was held constant at a value of 7.549834View Model Summary - Random Forest (RFM)
summary(mdlRndForRfm)## Length Class Mode
## call 6 -none- call
## type 1 -none- character
## predicted 3221 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 6442 matrix numeric
## oob.times 3221 -none- numeric
## classes 2 -none- character
## importance 57 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 3221 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 57 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## param 2 -none- listPrediction - Test Data - Random Forest (RFM)
vctRndForRfm <- predict(mdlRndForRfm, newdata=dfrTstData)
cmxRndForRfm <- confusionMatrix(vctRndForRfm, dfrTstData$Result)
cmxRndForRfm## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Spam Spam
## Not Spam 807 26
## Spam 39 508
##
## Accuracy : 0.9529
## 95% CI : (0.9404, 0.9635)
## No Information Rate : 0.613
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9012
## Mcnemar's Test P-Value : 0.1366
##
## Sensitivity : 0.9539
## Specificity : 0.9513
## Pos Pred Value : 0.9688
## Neg Pred Value : 0.9287
## Prevalence : 0.6130
## Detection Rate : 0.5848
## Detection Prevalence : 0.6036
## Balanced Accuracy : 0.9526
##
## 'Positive' Class : Not Spam
## Create Model - Random Forest (GBM)- General bagging method.
## set seed
set.seed(707)
# start time
vctProcStrt <- proc.time()
# random forest (default)
myControl <- trainControl(method="repeatedcv", number=10, repeats=3)
myMetric <- "Accuracy"
myMtry <- sqrt(ncol(dfrTrnData)-2)
myNtrees <- 500
myTuneGrid <- expand.grid(.mtry=myMtry)
mdlRndForGbm <- train(Result~.-status, data=dfrTrnData, method="gbm",
verbose=F, metric=myMetric, trControl=myControl)
# end time
vctProcEnds <- proc.time()
# print
print(paste("Model Created ...",vctProcEnds[1] - vctProcStrt[1]))## [1] "Model Created ... 202.63"View Model - Random Forest (GBM)
mdlRndForGbm## Stochastic Gradient Boosting
##
## 3221 samples
## 58 predictor
## 2 classes: 'Not Spam', 'Spam'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 3 times)
## Summary of sample sizes: 2898, 2899, 2900, 2898, 2899, 2899, ...
## Resampling results across tuning parameters:
##
## interaction.depth n.trees Accuracy Kappa
## 1 50 0.9119364 0.8120901
## 1 100 0.9305600 0.8530141
## 1 150 0.9349085 0.8625545
## 2 50 0.9303539 0.8527418
## 2 100 0.9398768 0.8733088
## 2 150 0.9440131 0.8821706
## 3 50 0.9347011 0.8622468
## 3 100 0.9437057 0.8814933
## 3 150 0.9484635 0.8917012
##
## Tuning parameter 'shrinkage' was held constant at a value of 0.1
##
## Tuning parameter 'n.minobsinnode' was held constant at a value of 10
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were n.trees = 150,
## interaction.depth = 3, shrinkage = 0.1 and n.minobsinnode = 10.View Model Summary - Random Forest (GBM)
summary(mdlRndForGbm)
## var rel.inf
## char_freq_..3 char_freq_..3 25.13448479
## char_freq_..4 char_freq_..4 17.73569393
## word_freq_remove word_freq_remove 10.29166207
## word_freq_free word_freq_free 8.03319338
## word_freq_hp word_freq_hp 7.32712662
## capital_run_length_average capital_run_length_average 5.39872005
## word_freq_your word_freq_your 5.17722583
## capital_run_length_longest capital_run_length_longest 3.06771805
## capital_run_length_total capital_run_length_total 2.37637288
## word_freq_edu word_freq_edu 2.36886986
## word_freq_george word_freq_george 2.23737649
## word_freq_our word_freq_our 1.91405678
## word_freq_money word_freq_money 1.86852638
## word_freq_1999 word_freq_1999 1.19302329
## word_freq_000 word_freq_000 0.89483913
## word_freq_you word_freq_you 0.56772121
## word_freq_meeting word_freq_meeting 0.56184568
## word_freq_re word_freq_re 0.49338930
## word_freq_internet word_freq_internet 0.45674727
## char_freq_. char_freq_. 0.38814204
## word_freq_will word_freq_will 0.35496666
## word_freq_receive word_freq_receive 0.31982874
## word_freq_650 word_freq_650 0.30196695
## char_freq_..1 char_freq_..1 0.28366668
## word_freq_font word_freq_font 0.18729687
## word_freq_email word_freq_email 0.18116648
## word_freq_business word_freq_business 0.14833434
## word_freq_hpl word_freq_hpl 0.14274872
## word_freq_technology word_freq_technology 0.10444119
## word_freq_over word_freq_over 0.09742430
## word_freq_report word_freq_report 0.09059711
## word_freq_all word_freq_all 0.07754641
## word_freq_3d word_freq_3d 0.06375843
## word_freq_credit word_freq_credit 0.03340249
## word_freq_project word_freq_project 0.03264960
## word_freq_make word_freq_make 0.02808895
## word_freq_pm word_freq_pm 0.02398858
## word_freq_conference word_freq_conference 0.01519494
## word_freq_mail word_freq_mail 0.01326804
## word_freq_parts word_freq_parts 0.01292947
## word_freq_address word_freq_address 0.00000000
## word_freq_order word_freq_order 0.00000000
## word_freq_people word_freq_people 0.00000000
## word_freq_addresses word_freq_addresses 0.00000000
## word_freq_lab word_freq_lab 0.00000000
## word_freq_labs word_freq_labs 0.00000000
## word_freq_telnet word_freq_telnet 0.00000000
## word_freq_857 word_freq_857 0.00000000
## word_freq_data word_freq_data 0.00000000
## word_freq_415 word_freq_415 0.00000000
## word_freq_85 word_freq_85 0.00000000
## word_freq_direct word_freq_direct 0.00000000
## word_freq_cs word_freq_cs 0.00000000
## word_freq_original word_freq_original 0.00000000
## word_freq_table word_freq_table 0.00000000
## char_freq_..2 char_freq_..2 0.00000000
## char_freq_..5 char_freq_..5 0.00000000Prediction - Test Data - Random Forest (GBM)
vctRndForGbm <- predict(mdlRndForGbm, newdata=dfrTstData)
cmxRndForGbm <- confusionMatrix(vctRndForGbm, dfrTstData$Result)
cmxRndForGbm## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Spam Spam
## Not Spam 804 29
## Spam 42 505
##
## Accuracy : 0.9486
## 95% CI : (0.9355, 0.9596)
## No Information Rate : 0.613
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.892
## Mcnemar's Test P-Value : 0.1544
##
## Sensitivity : 0.9504
## Specificity : 0.9457
## Pos Pred Value : 0.9652
## Neg Pred Value : 0.9232
## Prevalence : 0.6130
## Detection Rate : 0.5826
## Detection Prevalence : 0.6036
## Balanced Accuracy : 0.9480
##
## 'Positive' Class : Not Spam
## Create Model - Random Forest (OOB)
## set seed
set.seed(707)
# start time
vctProcStrt <- proc.time()
# random forest (default)
myControl <- trainControl(method="oob", number=10, repeats=3)
myMetric <- "Accuracy"
myMtry <- sqrt(ncol(dfrTrnData)-2) #sqrt(ncol(dfrTrnData)-2)
myNtrees <- 500
myTuneGrid <- expand.grid(.mtry=myMtry)
mdlRndForOob <- train(Result~.-status, data=dfrTrnData,
verbose=F, metric=myMetric, trControl=myControl,
tuneGrid=myTuneGrid, ntree=myNtrees)
# end time
vctProcEnds <- proc.time()
# print
print(paste("Model Created ...",vctProcEnds[1] - vctProcStrt[1]))## [1] "Model Created ... 39.72"View Model - Random Formbest (OOB)
mdlRndForOob## Random Forest
##
## 3221 samples
## 58 predictor
## 2 classes: 'Not Spam', 'Spam'
##
## No pre-processing
## Resampling results:
##
## Accuracy Kappa
## 0.9531201 0.9015534
##
## Tuning parameter 'mtry' was held constant at a value of 7.549834View Model Summary - Random Forest (OOB)
summary(mdlRndForOob)## Length Class Mode
## call 6 -none- call
## type 1 -none- character
## predicted 3221 factor numeric
## err.rate 1500 -none- numeric
## confusion 6 -none- numeric
## votes 6442 matrix numeric
## oob.times 3221 -none- numeric
## classes 2 -none- character
## importance 57 -none- numeric
## importanceSD 0 -none- NULL
## localImportance 0 -none- NULL
## proximity 0 -none- NULL
## ntree 1 -none- numeric
## mtry 1 -none- numeric
## forest 14 -none- list
## y 3221 factor numeric
## test 0 -none- NULL
## inbag 0 -none- NULL
## xNames 57 -none- character
## problemType 1 -none- character
## tuneValue 1 data.frame list
## obsLevels 2 -none- character
## param 2 -none- listPrediction - Test Data - Random Forest (OOB)
vctRndForOob <- predict(mdlRndForOob, newdata=dfrTstData)
cmxRndForOob <- confusionMatrix(vctRndForOob, dfrTstData$Result)
cmxRndForOob## Confusion Matrix and Statistics
##
## Reference
## Prediction Not Spam Spam
## Not Spam 809 27
## Spam 37 507
##
## Accuracy : 0.9536
## 95% CI : (0.9412, 0.9641)
## No Information Rate : 0.613
## P-Value [Acc > NIR] : <2e-16
##
## Kappa : 0.9026
## Mcnemar's Test P-Value : 0.2606
##
## Sensitivity : 0.9563
## Specificity : 0.9494
## Pos Pred Value : 0.9677
## Neg Pred Value : 0.9320
## Prevalence : 0.6130
## Detection Rate : 0.5862
## Detection Prevalence : 0.6058
## Balanced Accuracy : 0.9529
##
## 'Positive' Class : Not Spam
## Conclusion
From all the above models executed we can see that Default Random Forest gave us an accruacy of 0.9507 , Random Forest Method gave us an accruacy of 0.9529 , General bagging method gave us an accruacy of 0.9486 and Random Forest (OOB) gave us an accruacy of 0.9543. Hence we go with Out of bag model for the prediction.
Wind Up
print("Wind Up")## [1] "Wind Up"