Analysis using Decision Tree Classification in R
Muriithi Nduta Ruth (MR. N)
Research Question
A Kenyan entrepreneur has created an online cryptography course and would want to advertise it on her blog. She currently targets audiences originating from various countries. In the past, she ran ads to advertise a related course on the same blog and collected data in the process. She would now like to employ your services as a Data Science Consultant to help her identify which individuals are most likely to click on her ads.
1. Defining the Question
1.1 Specifying the data analytic objective
Our main aim is to do thorough exploratory data analysis for univariate and bivariate data and come up with recommendations for our client.
1.2 Defining the metric for success
We aim to build elaborate visualizations for univariate and bivariate analysis
1.3 Recording the Experimental Design
- Loading the data
- Checking the data
- Tidying the data
- Univariate Analysis
- Bivariate Analysis
- Challenging the solution
- Recommendations
- Follow up questions
1.4 Data Relevance
The data provided was relevant for our analysis.
2.Loading and reading Our Datasets
library(tidyverse)## -- Attaching packages --------------------------------------------------------------------------- tidyverse 1.3.0 --## v ggplot2 3.3.2 v purrr 0.3.4
## v tibble 3.0.2 v dplyr 1.0.0
## v tidyr 1.1.0 v stringr 1.4.0
## v readr 1.3.1 v forcats 0.5.0## -- Conflicts ------------------------------------------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()ads <- read.csv("~/moringa-school-projects/RYouWithMe/advertising.csv")
view(ads)Checking the data summary
summary(ads)## Daily.Time.Spent.on.Site Age Area.Income Daily.Internet.Usage
## Min. :32.60 Min. :19.00 Min. :13996 Min. :104.8
## 1st Qu.:51.36 1st Qu.:29.00 1st Qu.:47032 1st Qu.:138.8
## Median :68.22 Median :35.00 Median :57012 Median :183.1
## Mean :65.00 Mean :36.01 Mean :55000 Mean :180.0
## 3rd Qu.:78.55 3rd Qu.:42.00 3rd Qu.:65471 3rd Qu.:218.8
## Max. :91.43 Max. :61.00 Max. :79485 Max. :270.0
## Ad.Topic.Line City Male Country
## Length:1000 Length:1000 Min. :0.000 Length:1000
## Class :character Class :character 1st Qu.:0.000 Class :character
## Mode :character Mode :character Median :0.000 Mode :character
## Mean :0.481
## 3rd Qu.:1.000
## Max. :1.000
## Timestamp Clicked.on.Ad
## Length:1000 Min. :0.0
## Class :character 1st Qu.:0.0
## Mode :character Median :0.5
## Mean :0.5
## 3rd Qu.:1.0
## Max. :1.0From the table above, we can see all our measures of central tendency (median, mean).
Checking top and bottom rows and columns
tail(ads)## Daily.Time.Spent.on.Site Age Area.Income Daily.Internet.Usage
## 995 43.70 28 63126.96 173.01
## 996 72.97 30 71384.57 208.58
## 997 51.30 45 67782.17 134.42
## 998 51.63 51 42415.72 120.37
## 999 55.55 19 41920.79 187.95
## 1000 45.01 26 29875.80 178.35
## Ad.Topic.Line City Male
## 995 Front-line bifurcated ability Nicholasland 0
## 996 Fundamental modular algorithm Duffystad 1
## 997 Grass-roots cohesive monitoring New Darlene 1
## 998 Expanded intangible solution South Jessica 1
## 999 Proactive bandwidth-monitored policy West Steven 0
## 1000 Virtual 5thgeneration emulation Ronniemouth 0
## Country Timestamp Clicked.on.Ad
## 995 Mayotte 2016-04-04 03:57:48 1
## 996 Lebanon 2016-02-11 21:49:00 1
## 997 Bosnia and Herzegovina 2016-04-22 02:07:01 1
## 998 Mongolia 2016-02-01 17:24:57 1
## 999 Guatemala 2016-03-24 02:35:54 0
## 1000 Brazil 2016-06-03 21:43:21 1head(ads)## Daily.Time.Spent.on.Site Age Area.Income Daily.Internet.Usage
## 1 68.95 35 61833.90 256.09
## 2 80.23 31 68441.85 193.77
## 3 69.47 26 59785.94 236.50
## 4 74.15 29 54806.18 245.89
## 5 68.37 35 73889.99 225.58
## 6 59.99 23 59761.56 226.74
## Ad.Topic.Line City Male Country
## 1 Cloned 5thgeneration orchestration Wrightburgh 0 Tunisia
## 2 Monitored national standardization West Jodi 1 Nauru
## 3 Organic bottom-line service-desk Davidton 0 San Marino
## 4 Triple-buffered reciprocal time-frame West Terrifurt 1 Italy
## 5 Robust logistical utilization South Manuel 0 Iceland
## 6 Sharable client-driven software Jamieberg 1 Norway
## Timestamp Clicked.on.Ad
## 1 2016-03-27 00:53:11 0
## 2 2016-04-04 01:39:02 0
## 3 2016-03-13 20:35:42 0
## 4 2016-01-10 02:31:19 0
## 5 2016-06-03 03:36:18 0
## 6 2016-05-19 14:30:17 0Checking the classes
class(ads)## [1] "data.frame"Checking the number of rows and in our dataset
cat("Rows in dataset:", nrow(ads), "\nCols in dataset:", ncol(ads))## Rows in dataset: 1000
## Cols in dataset: 10cat("\nThe dimension of the dataset is:", dim(ads))##
## The dimension of the dataset is: 1000 10Range of Time Spent on Site by users
site.time.range <- range(ads$Daily.Time.Spent.on.Site)
cat("The Range of Time Spent on Site by users is:",site.time.range)## The Range of Time Spent on Site by users is: 32.6 91.43Range of Daily Internet Usage
internet.time.range <- range(ads$Daily.Internet.Usage)
cat("The Range of Daily Internet Usage is:", internet.time.range)## The Range of Daily Internet Usage is: 104.78 269.96Range of Age
age.range <- range(ads$Age)
cat("The Range of Users' age is:",age.range)## The Range of Users' age is: 19 61Range of Income
income.range <- range(ads$Area.Income)
cat("The Range of Users' income is:",income.range)## The Range of Users' income is: 13996.5 79484.8Structure of our dataframe
str(ads)## 'data.frame': 1000 obs. of 10 variables:
## $ Daily.Time.Spent.on.Site: num 69 80.2 69.5 74.2 68.4 ...
## $ Age : int 35 31 26 29 35 23 33 48 30 20 ...
## $ Area.Income : num 61834 68442 59786 54806 73890 ...
## $ Daily.Internet.Usage : num 256 194 236 246 226 ...
## $ Ad.Topic.Line : chr "Cloned 5thgeneration orchestration" "Monitored national standardization" "Organic bottom-line service-desk" "Triple-buffered reciprocal time-frame" ...
## $ City : chr "Wrightburgh" "West Jodi" "Davidton" "West Terrifurt" ...
## $ Male : int 0 1 0 1 0 1 0 1 1 1 ...
## $ Country : chr "Tunisia" "Nauru" "San Marino" "Italy" ...
## $ Timestamp : chr "2016-03-27 00:53:11" "2016-04-04 01:39:02" "2016-03-13 20:35:42" "2016-01-10 02:31:19" ...
## $ Clicked.on.Ad : int 0 0 0 0 0 0 0 1 0 0 ...Our dataset is of type dataframe, with 1000 records and 10 variables. 3 variables of tye numeric, 3 integer types, 4 character types including the date and time which will be converted to the standard format.
Converting the date and time
The time looks like a character string when you display it, but its data type. It should be in the class “POSIXct” “POSIXt” (it has two classes).
class(ads$Timestamp)## [1] "character"ads$Timestamp <- strptime(paste( ads$Timestamp), format = "%Y-%m-%d %H:%M:%S",tz="UTC")
class(ads$Timestamp)## [1] "POSIXlt" "POSIXt"Checking for outliers on the numerical columns using boxplots
boxplot(ads$Area.Income)
boxplot(ads$Daily.Time.Spent.on.Site)
boxplot(ads$Age)
boxplot(ads$Daily.Internet.Usage)
Only one variable had outliers, Area.Income.
Removing outliers;
We’ll remove outliers by limiting extreme values in the statistical data to reduce the effect of possibly spurious outliers.
bench <- 47032 - 1.5 * IQR(ads$Area.Income)
ads$Area.Income[ads$Area.Income < bench]<- bench
boxplot(ads$Area.Income)
The boxplot shows that the outliers in that column have been removed.
Checking for duplicates
sum(duplicated(ads))## [1] 0There are no duplicates in our data
Checking for missing values
colSums(is.na(ads))## Daily.Time.Spent.on.Site Age Area.Income
## 0 0 0
## Daily.Internet.Usage Ad.Topic.Line City
## 0 0 0
## Male Country Timestamp
## 0 0 0
## Clicked.on.Ad
## 0The dataset has no missing values in any of the columns.
Exploratory Data Analysis
install.packages("dataMaid", repos = "http://cran.us.r-project.org")## Installing package into 'C:/Users/ruth/Documents/R/win-library/4.0'
## (as 'lib' is unspecified)## package 'dataMaid' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\ruth\AppData\Local\Temp\RtmpQNUhY6\downloaded_packagesinstall.packages("inspectdf", repos = "http://cran.us.r-project.org")## Installing package into 'C:/Users/ruth/Documents/R/win-library/4.0'
## (as 'lib' is unspecified)## package 'inspectdf' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'inspectdf'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying C:
## \Users\ruth\Documents\R\win-library\4.0\00LOCK\inspectdf\libs\x64\inspectdf.dll
## to C:\Users\ruth\Documents\R\win-library\4.0\inspectdf\libs\x64\inspectdf.dll:
## Permission denied## Warning: restored 'inspectdf'##
## The downloaded binary packages are in
## C:\Users\ruth\AppData\Local\Temp\RtmpQNUhY6\downloaded_packagesCalling the libraries
library(dplyr)
library(inspectdf)## Warning: package 'inspectdf' was built under R version 4.0.3The 2 packages will give us more insights on our data.
inspect_cat(ads)## Warning: `...` is not empty.
##
## We detected these problematic arguments:
## * `needs_dots`
##
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?## # A tibble: 3 x 5
## col_name cnt common common_pcnt levels
## <chr> <int> <chr> <dbl> <named list>
## 1 Ad.Topic.Line 1000 Adaptive 24hour Graphic Int~ 0.1 <tibble [1,000 x~
## 2 City 969 Lisamouth 0.3 <tibble [969 x 3~
## 3 Country 237 Czech Republic 0.900 <tibble [237 x 3~common_pcnt, the percentage of each column occupied by the most common level shown in common.
Bivariate Analysis visualization
Here we check for correlation between the different columns and the target variable Clicked.On.Ad.
inspect_cor(ads, df2 = NULL, method = "pearson", with_col = 'Clicked.on.Ad', alpha = 0.05)## Warning: Columns with 0 variance found: Male, Clicked.on.Ad## Warning: `...` is not empty.
##
## We detected these problematic arguments:
## * `needs_dots`
##
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?## # A tibble: 5 x 7
## col_1 col_2 corr p_value lower upper pcnt_nna
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Clicked.on.Ad Daily.Internet.Usage -0.787 3.74e-136 -0.809 -0.762 100
## 2 Clicked.on.Ad Daily.Time.Spent.on.~ -0.748 2.29e-123 -0.774 -0.719 100
## 3 Clicked.on.Ad Age 0.493 1.55e- 54 0.444 0.538 100
## 4 Clicked.on.Ad Area.Income -0.477 3.04e- 51 -0.523 -0.428 100
## 5 Clicked.on.Ad Male -0.0380 2.30e- 1 -0.0998 0.0240 100The summary above covers Pearson’s correlation coefficients for all the numeric columns, compared against the Clicked.On.Ads column.
Across the board, we can see that there are negative correlation values for Daily.Internet.Usage, Daily.Time.Spent.on.Site, Area Income. The only positive correlation is between Clicked.On.Ad and Age.
inspect_cor(ads, df2 = NULL, method = "pearson", alpha = 0.05)## Warning: Columns with 0 variance found: Male, Clicked.on.Ad## Warning: `...` is not empty.
##
## We detected these problematic arguments:
## * `needs_dots`
##
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?## # A tibble: 15 x 7
## col_1 col_2 corr p_value lower upper pcnt_nna
## <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Clicked.on.Ad Daily.Internet.U~ -0.787 3.74e-136 -0.809 -0.762 100
## 2 Clicked.on.Ad Daily.Time.Spent~ -0.748 2.29e-123 -0.774 -0.719 100
## 3 Daily.Internet~ Daily.Time.Spent~ 0.519 2.80e- 60 0.472 0.563 100
## 4 Clicked.on.Ad Age 0.493 1.55e- 54 0.444 0.538 100
## 5 Clicked.on.Ad Area.Income -0.477 3.04e- 51 -0.523 -0.428 100
## 6 Daily.Internet~ Age -0.367 4.38e- 31 -0.420 -0.312 100
## 7 Daily.Internet~ Area.Income 0.339 8.86e- 27 0.283 0.393 100
## 8 Age Daily.Time.Spent~ -0.332 1.22e- 25 -0.386 -0.275 100
## 9 Area.Income Daily.Time.Spent~ 0.312 6.35e- 23 0.255 0.367 100
## 10 Area.Income Age -0.182 8.44e- 9 -0.242 -0.122 100
## 11 Clicked.on.Ad Male -0.0380 2.30e- 1 -0.0998 0.0240 100
## 12 Male Daily.Internet.U~ 0.0280 3.76e- 1 -0.0340 0.0898 100
## 13 Male Age -0.0210 5.06e- 1 -0.0829 0.0410 100
## 14 Male Daily.Time.Spent~ -0.0190 5.50e- 1 -0.0808 0.0431 100
## 15 Male Area.Income 0.00252 9.36e- 1 -0.0595 0.0645 100install.packages("PerformanceAnalytics")## Installing package into 'C:/Users/ruth/Documents/R/win-library/4.0'
## (as 'lib' is unspecified)## package 'PerformanceAnalytics' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\ruth\AppData\Local\Temp\RtmpQNUhY6\downloaded_packagesinstall.packages("corrplot")## Installing package into 'C:/Users/ruth/Documents/R/win-library/4.0'
## (as 'lib' is unspecified)## package 'corrplot' successfully unpacked and MD5 sums checked
##
## The downloaded binary packages are in
## C:\Users\ruth\AppData\Local\Temp\RtmpQNUhY6\downloaded_packageslibrary(corrplot)## corrplot 0.84 loadedads_num <- Filter(is.numeric, ads)
corrplot(cor(ads_num))
The Daily internet usage and Daily time spent on the site columns have a large positive correlation and so does the Clicked.On.Ad and age columns.
We plan on using the Clicked.On.Ad feature to determine fill colors for these graphs, but that won’t work if they stay as they’re currently set (integer data type). I’ll change that in the following code chunk.
library(ggplot2)
ggplot(data = ads, aes(x = Age, fill = Clicked.on.Ad))+
geom_histogram(bins = 27, color = 'cyan') +
labs(title = 'Age distribution with Ad clicks', x = 'Age', y = 'Frequency', fill = 'Clicked.on.Ad') +
scale_color_brewer(palette = 'Set2') 
Income and Click on Ad distribution
ggplot(data = ads, aes(x = Area.Income, fill = Clicked.on.Ad))+
geom_histogram(bins = 20, color = 'cyan') +
labs(title = 'Income distribution', x = 'Income', y = 'Frequency', fill = 'Clicked.on.Ad') +
scale_color_brewer(palette = 'Set1') 
Daily Internet Use and the clicked on ad relationship
ggplot(data = ads, aes(x = Daily.Internet.Usage, fill = Clicked.on.Ad))+
geom_histogram(bins = 35, color = 'cyan') +
labs(title = 'Daily Internet Use distribution', x = 'Daily Internet Usage (minutes)', y = 'Frequency', fill = 'Clicked.on.Ad') +
scale_color_brewer(palette = 'Set1')
Daily Time Spent on Site and the clicked on ad relationship
ggplot(data = ads, aes(x = Daily.Time.Spent.on.Site, fill = Clicked.on.Ad))+
geom_histogram(bins = 25, color = 'cyan') +
labs(title = 'Daily Time Spent On Site', x = 'Time Spent(minutes)', y = 'Frequency', fill = 'Clicked.on.Ad') +
scale_color_brewer(palette = 'Set1') 
Daily Time Spent on Site and the Income relationship
ggplot(data = ads, aes(x =Area.Income , fill = Daily.Time.Spent.on.Site))+
geom_histogram(bins = 30, color = 'cyan') +
labs(title = 'Daily Time Spent On Site vs Income', x = 'Income', y = 'Frequency', fill = 'Clicked.on.Ad') +
scale_color_brewer(palette = 'Set1') 
Daily Time Spent on Site and the Users’ Age relationship
ggplot(data = ads, aes(x =Age , fill = Daily.Time.Spent.on.Site))+
geom_histogram(bins = 30, color = 'cyan') +
labs(title = 'Daily Time Spent On Site vs Age', x = 'Age', y = 'Frequency', fill = 'Clicked.on.Ad') +
scale_color_brewer(palette = 'Set1') 
Daily Internet Usage per Country
ads %>% group_by(Country, Daily.Internet.Usage)%>% head(10)%>% arrange(desc(Daily.Internet.Usage))## Warning: `...` is not empty.
##
## We detected these problematic arguments:
## * `needs_dots`
##
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?## # A tibble: 10 x 10
## # Groups: Country, Daily.Internet.Usage [10]
## Daily.Time.Spen~ Age Area.Income Daily.Internet.~ Ad.Topic.Line City Male
## <dbl> <int> <dbl> <dbl> <chr> <chr> <int>
## 1 69.0 35 61834. 256. Cloned 5thge~ Wrig~ 0
## 2 74.2 29 54806. 246. Triple-buffe~ West~ 1
## 3 69.5 26 59786. 236. Organic bott~ Davi~ 0
## 4 60.0 23 59762. 227. Sharable cli~ Jami~ 1
## 5 68.4 35 73890. 226. Robust logis~ Sout~ 0
## 6 74.5 30 68862 222. Configurable~ West~ 1
## 7 88.9 33 53853. 208. Enhanced ded~ Bran~ 0
## 8 80.2 31 68442. 194. Monitored na~ West~ 1
## 9 69.9 20 55642. 184. Mandatory ho~ Rami~ 1
## 10 66 48 24593. 132. Reactive loc~ Port~ 1
## # ... with 3 more variables: Country <chr>, Timestamp <dttm>,
## # Clicked.on.Ad <int>Daily Time Spent on Site per Country
ads %>% group_by(Country, Daily.Time.Spent.on.Site)%>% head(10)%>% arrange(desc(Daily.Time.Spent.on.Site))## Warning: `...` is not empty.
##
## We detected these problematic arguments:
## * `needs_dots`
##
## These dots only exist to allow future extensions and should be empty.
## Did you misspecify an argument?## # A tibble: 10 x 10
## # Groups: Country, Daily.Time.Spent.on.Site [10]
## Daily.Time.Spen~ Age Area.Income Daily.Internet.~ Ad.Topic.Line City Male
## <dbl> <int> <dbl> <dbl> <chr> <chr> <int>
## 1 88.9 33 53853. 208. Enhanced ded~ Bran~ 0
## 2 80.2 31 68442. 194. Monitored na~ West~ 1
## 3 74.5 30 68862 222. Configurable~ West~ 1
## 4 74.2 29 54806. 246. Triple-buffe~ West~ 1
## 5 69.9 20 55642. 184. Mandatory ho~ Rami~ 1
## 6 69.5 26 59786. 236. Organic bott~ Davi~ 0
## 7 69.0 35 61834. 256. Cloned 5thge~ Wrig~ 0
## 8 68.4 35 73890. 226. Robust logis~ Sout~ 0
## 9 66 48 24593. 132. Reactive loc~ Port~ 1
## 10 60.0 23 59762. 227. Sharable cli~ Jami~ 1
## # ... with 3 more variables: Country <chr>, Timestamp <dttm>,
## # Clicked.on.Ad <int>I’ll use the decision tree to do classification.
Modelling
# dropping irrelevant columns
mod_ads <- subset(ads, select = -c(Timestamp, Country, City, Ad.Topic.Line) )
mod_ads## Daily.Time.Spent.on.Site Age Area.Income Daily.Internet.Usage Male
## 1 68.95 35 61833.90 256.09 0
## 2 80.23 31 68441.85 193.77 1
## 3 69.47 26 59785.94 236.50 0
## 4 74.15 29 54806.18 245.89 1
## 5 68.37 35 73889.99 225.58 0
## 6 59.99 23 59761.56 226.74 1
## 7 88.91 33 53852.85 208.36 0
## 8 66.00 48 24593.33 131.76 1
## 9 74.53 30 68862.00 221.51 1
## 10 69.88 20 55642.32 183.82 1
## 11 47.64 49 45632.51 122.02 0
## 12 83.07 37 62491.01 230.87 1
## 13 69.57 48 51636.92 113.12 1
## 14 79.52 24 51739.63 214.23 0
## 15 42.95 33 30976.00 143.56 0
## 16 63.45 23 52182.23 140.64 1
## 17 55.39 37 23936.86 129.41 0
## 18 82.03 41 71511.08 187.53 0
## 19 54.70 36 31087.54 118.39 1
## 20 74.58 40 23821.72 135.51 1
## 21 77.22 30 64802.33 224.44 1
## 22 84.59 35 60015.57 226.54 1
## 23 41.49 52 32635.70 164.83 0
## 24 87.29 36 61628.72 209.93 1
## 25 41.39 41 68962.32 167.22 0
## 26 78.74 28 64828.00 204.79 1
## 27 48.53 28 38067.08 134.14 1
## 28 51.95 52 58295.82 129.23 0
## 29 70.20 34 32708.94 119.20 0
## 30 76.02 22 46179.97 209.82 0
## 31 67.64 35 51473.28 267.01 1
## 32 86.41 28 45593.93 207.48 1
## 33 59.05 57 25583.29 169.23 1
## 34 55.60 23 30227.98 212.58 0
## 35 57.64 57 45580.92 133.81 1
## 36 84.37 30 61389.50 201.58 0
## 37 62.26 53 56770.79 125.45 1
## 38 65.82 39 76435.30 221.94 0
## 39 50.43 46 57425.87 119.32 1
## 40 38.93 39 27508.41 162.08 0
## 41 84.98 29 57691.95 202.61 0
## 42 64.24 30 59784.18 252.36 0
## 43 82.52 32 66572.39 198.11 1
## 44 81.38 31 64929.61 212.30 0
## 45 80.47 25 57519.64 204.86 0
## 46 37.68 52 53575.48 172.83 1
## 47 69.62 20 50983.75 202.25 1
## 48 85.40 43 67058.72 198.72 0
## 49 44.33 37 52723.34 123.72 1
## 50 48.01 46 54286.10 119.93 0
## 51 73.18 23 61526.25 196.71 1
## 52 79.94 28 58526.04 225.29 0
## 53 33.33 45 53350.11 193.58 1
## 54 50.33 50 62657.53 133.20 1
## 55 62.31 47 62722.57 119.30 0
## 56 80.60 31 67479.62 177.55 0
## 57 65.19 36 75254.88 150.61 0
## 58 44.98 49 52336.64 129.31 0
## 59 77.63 29 56113.37 239.22 0
## 60 41.82 41 24852.90 156.36 0
## 61 85.61 27 47708.42 183.43 0
## 62 85.84 34 64654.66 192.93 1
## 63 72.08 29 71228.44 169.50 0
## 64 86.06 32 61601.05 178.92 1
## 65 45.96 45 66281.46 141.22 0
## 66 62.42 29 73910.90 198.50 1
## 67 63.89 40 51317.33 105.22 0
## 68 35.33 32 51510.18 200.22 0
## 69 75.74 25 61005.87 215.25 1
## 70 78.53 34 32536.98 131.72 0
## 71 46.13 31 60248.97 139.01 0
## 72 69.01 46 74543.81 222.63 0
## 73 55.35 39 75509.61 153.17 1
## 74 33.21 43 42650.32 167.07 1
## 75 38.46 42 58183.04 145.98 1
## 76 64.10 22 60465.72 215.93 0
## 77 49.81 35 57009.76 120.06 1
## 78 82.73 33 54541.56 238.99 1
## 79 56.14 38 32689.04 113.53 1
## 80 55.13 45 55605.92 111.71 0
## 81 78.11 27 63296.87 209.25 1
## 82 73.46 28 65653.47 222.75 1
## 83 56.64 38 61652.53 115.91 0
## 84 68.94 54 30726.26 138.71 0
## 85 70.79 31 74535.94 184.10 0
## 86 57.76 41 47861.93 105.15 0
## 87 77.51 36 73600.28 200.55 0
## 88 52.70 34 58543.94 118.60 1
## 89 57.70 34 42696.67 109.07 0
## 90 56.89 37 37334.78 109.29 1
## 91 69.90 43 71392.53 138.35 0
## 92 55.79 24 59550.05 149.67 0
## 93 70.03 26 64264.25 227.72 1
## 94 50.08 40 64147.86 125.85 1
## 95 43.67 31 25686.34 166.29 1
## 96 72.84 26 52968.22 238.63 0
## 97 45.72 36 22473.08 154.02 1
## 98 39.94 41 64927.19 156.30 0
## 99 35.61 46 51868.85 158.22 0
## 100 79.71 34 69456.83 211.65 1
## 101 41.49 53 31947.65 169.18 0
## 102 63.60 23 51864.77 235.28 1
## 103 89.91 40 59593.56 194.23 0
## 104 68.18 21 48376.14 218.17 1
## 105 66.49 20 56884.74 202.16 0
## 106 80.49 40 67186.54 229.12 1
## 107 72.23 25 46557.92 241.03 1
## 108 42.39 42 66541.05 150.99 0
## 109 47.53 30 33258.09 135.18 0
## 110 74.02 32 72272.90 210.54 0
## 111 66.63 60 60333.38 176.98 0
## 112 63.24 53 65229.13 235.78 1
## 113 71.00 22 56067.38 211.87 0
## 114 46.13 46 37838.72 123.64 1
## 115 69.00 32 72683.35 221.21 1
## 116 76.99 31 56729.78 244.34 1
## 117 72.60 55 66815.54 162.95 0
## 118 61.88 42 60223.52 112.19 1
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## 1000 1Decision trees
#data splicing
set.seed(12345)
train <- sample(1:nrow(mod_ads),size = ceiling(0.80*nrow(mod_ads)),replace = FALSE)
# training set
mod_ads_train <- mod_ads[train,]
# test set
mod_ads_test <- mod_ads[-train,]# building the classification tree with rpart
library(rpart)
tree <- rpart(Clicked.on.Ad~.,
data=mod_ads_train,
method = "class")# Visualize the decision tree with rpart.plot
library(rpart.plot)
rpart.plot(tree, nn=TRUE)
#Testing the model
pred <- predict(object = tree,
newdata = mod_ads_test,
type = "class")#Calculating accuracy
library(caret)## Loading required package: lattice##
## Attaching package: 'caret'## The following object is masked from 'package:purrr':
##
## liftconf.ads <- confusionMatrix(data = pred,
reference = pred)
conf.ads## Confusion Matrix and Statistics
##
## Reference
## Prediction 0 1
## 0 99 0
## 1 0 101
##
## Accuracy : 1
## 95% CI : (0.9817, 1)
## No Information Rate : 0.505
## P-Value [Acc > NIR] : < 2.2e-16
##
## Kappa : 1
##
## Mcnemar's Test P-Value : NA
##
## Sensitivity : 1.000
## Specificity : 1.000
## Pos Pred Value : 1.000
## Neg Pred Value : 1.000
## Prevalence : 0.495
## Detection Rate : 0.495
## Detection Prevalence : 0.495
## Balanced Accuracy : 1.000
##
## 'Positive' Class : 0
## The output shows that all the samples in the test dataset have been correctly classified and we’ve attained an accuracy of 100% on the test data set with a 95% confidence interval (0.9817, 1).
Class 0 on clicking on ads takes the day.