International Center for Applied Mathematical Modelling and Data Analysis (ICAMMDA)
Graduate Internship Training (GIT) Presentation
Opayinka Mercy Oluwafunmilayo
2-5-2025
Supervisor: Mr Idowu Olasupo
Opayinka Mercy Oluwafunmilayo
2-5-2025
Supervisor: Mr Idowu Olasupo
Load necessary libraries
library(dplyr)#for data manipulation(filtering,summarizing etc)
library(tidyverse)#this package is contains other packages like ggplot2,dplr,tidyr,etc
library(ggplot2)#for creating complex data visualization
library(gridExtra)#for arranging multiple ggplot objects in a grid layout.Importing data set
Importing the data set using the read.csv() function,(comma separated variable), and assessing the quality of the data set, by checking the structure, and then checked for missing,duplicate values,and dimension of the data set.
## case_id generation date_infection date_onset
## Length:454 Min. : 2.00 Length:454 Length:454
## Class :character 1st Qu.:13.00 Class :character Class :character
## Mode :character Median :16.00 Mode :character Mode :character
## Mean :16.88
## 3rd Qu.:20.00
## Max. :36.00
##
## date_hospitalisation date_outcome outcome gender
## Length:454 Length:454 Length:454 Length:454
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## age age_unit age_years age_cat
## Min. : 0.0 Length:454 Min. : 0.00 Length:454
## 1st Qu.: 7.0 Class :character 1st Qu.: 7.00 Class :character
## Median :15.0 Mode :character Median :15.00 Mode :character
## Mean :17.4 Mean :17.38
## 3rd Qu.:24.0 3rd Qu.:24.00
## Max. :87.0 Max. :87.00
## NA's :9 NA's :9
## age_cat5 hospital lon lat
## Length:454 Length:454 Min. :-13.27 Min. :8.448
## Class :character Class :character 1st Qu.:-13.25 1st Qu.:8.460
## Mode :character Mode :character Median :-13.23 Median :8.468
## Mean :-13.23 Mean :8.469
## 3rd Qu.:-13.22 3rd Qu.:8.479
## Max. :-13.21 Max. :8.490
##
## infector source wt_kg ht_cm
## Length:454 Length:454 Min. : -2.00 Min. : 15.0
## Class :character Class :character 1st Qu.: 43.00 1st Qu.: 97.0
## Mode :character Mode :character Median : 57.50 Median :135.0
## Mean : 55.13 Mean :129.7
## 3rd Qu.: 67.00 3rd Qu.:161.0
## Max. :103.00 Max. :335.0
##
## ct_blood fever chills cough
## Min. :17.00 Length:454 Length:454 Length:454
## 1st Qu.:20.00 Class :character Class :character Class :character
## Median :22.00 Mode :character Mode :character Mode :character
## Mean :21.19
## 3rd Qu.:22.00
## Max. :25.00
##
## aches vomit temp time_admission
## Length:454 Length:454 Min. :35.7 Length:454
## Class :character Class :character 1st Qu.:37.8 Class :character
## Mode :character Mode :character Median :38.8 Mode :character
## Mean :38.5
## 3rd Qu.:39.2
## Max. :40.4
## NA's :3
## bmi days_onset_hosp
## Min. :-41.32 Min. : 0.000
## 1st Qu.: 24.10 1st Qu.: 1.000
## Median : 32.23 Median : 1.000
## Mean : 44.57 Mean : 1.852
## 3rd Qu.: 47.71 3rd Qu.: 2.000
## Max. :370.37 Max. :12.000
## ## case_id generation date_infection date_onset date_hospitalisation
## 1 275cc7 5 2014-05-24 2014-05-27 2014-05-28
## 2 64c8ef 6 2014-07-14 2014-07-15
## 3 e56412 9 2014-07-12 2014-07-15 2014-07-17
## 4 62a2ef 9 2014-07-02 2014-07-17 2014-07-19
## 5 92e129 10 2014-08-01 2014-08-10 2014-08-12
## 6 240da4 10 2014-08-20 2014-08-20
## 7 31e797 13 2014-08-21 2014-08-26 2014-08-28
## 8 9b4647 9 2014-08-03 2014-08-29 2014-08-29
## 9 fd4dd4 13 2014-09-04 2014-09-05 2014-09-05
## 10 24ef7d 11 2014-09-06 2014-09-12 2014-09-14
## date_outcome outcome gender age age_unit age_years age_cat age_cat5
## 1 2014-06-07 Death f 4 years 4 0-4 0-4
## 2 2014-07-25 Recover m 30 years 30 30-49 30-34
## 3 2014-07-19 Death f 23 years 23 20-29 20-24
## 4 2014-07-23 Death f 14 years 14 10-14 10-14
## 5 2014-08-22 Recover f 42 years 42 30-49 40-44
## 6 Death m 1 years 1 0-4 0-4
## 7 Death m 49 years 49 30-49 45-49
## 8 2014-08-27 Death m 44 years 44 30-49 40-44
## 9 2014-09-07 Death f 2 years 2 0-4 0-4
## 10 2014-10-09 Recover m 52 years 52 50-69 50-54
## hospital lon lat infector source wt_kg ht_cm ct_blood
## 1 Central Hospital -13.23694 8.469161 e02f66 other 41 75 23
## 2 Central Hospital -13.26147 8.457506 66 177 23
## 3 Central Hospital -13.23433 8.478321 894024 funeral 55 150 23
## 4 Central Hospital -13.23794 8.469774 b0c500 other 59 136 22
## 5 Central Hospital -13.23573 8.474329 4b501e other 85 196 23
## 6 Central Hospital -13.26799 8.462502 17 47 23
## 7 Central Hospital -13.26021 8.457613 c276a4 other 82 220 21
## 8 Central Hospital -13.21540 8.482433 2b36fa other 73 207 23
## 9 Central Hospital -13.21277 8.464897 ea996f funeral 21 39 23
## 10 Central Hospital -13.22377 8.461412 a8c00d other 90 236 22
## fever chills cough aches vomit temp time_admission bmi days_onset_hosp
## 1 no no yes no no 37.2 09:21 72.88889 1
## 2 no yes yes no no 36.8 10:12 21.06674 1
## 3 no no no no yes 37.4 16:46 24.44444 2
## 4 no yes yes no no 36.6 13:52 31.89879 2
## 5 36.7 14:25 22.12620 2
## 6 no yes yes no yes 36.3 16:06 76.95790 0
## 7 no no yes no no 36.2 13:10 16.94215 2
## 8 37.8 04:00 17.03657 0
## 9 no yes no no no 37.1 15:28 138.06706 0
## 10 37.5 13:46 16.15915 2## [1] 890## case_id generation date_infection
## 0 0 154
## date_onset date_hospitalisation date_outcome
## 0 0 74
## outcome gender age
## 96 26 9
## age_unit age_years age_cat
## 0 9 9
## age_cat5 hospital lon
## 9 0 0
## lat infector source
## 0 154 154
## wt_kg ht_cm ct_blood
## 0 0 0
## fever chills cough
## 26 26 26
## aches vomit temp
## 26 26 3
## time_admission bmi days_onset_hosp
## 63 0 0missing_summary <- sapply(centhos, function(x) sum(is.na(x) | x == ""))
missing_summary <- missing_summary[missing_summary > 0]
missing_summary## date_infection date_outcome outcome gender age
## 154 74 96 26 9
## age_years age_cat age_cat5 infector source
## 9 9 9 154 154
## fever chills cough aches vomit
## 26 26 26 26 26
## temp time_admission
## 3 63## [1] 0## [1] 454 30Handling missing data
To handle the missing values, i discovered that most of the columns have too much missing values, and some are irrelevant so i removed them! like the date_infection, date_outcome, source, infector columns has a lot of missing values and the data are irrelevant and for the other age_cat is the same as age_cat5 so i removed age_cat.
## case_id generation outcome gender age age_cat5 lon lat wt_kg ht_cm
## 1 275cc7 5 Death f 4 0-4 -13.23694 8.469161 41 75
## 2 64c8ef 6 Recover m 30 30-34 -13.26147 8.457506 66 177
## 3 e56412 9 Death f 23 20-24 -13.23433 8.478321 55 150
## 4 62a2ef 9 Death f 14 10-14 -13.23794 8.469774 59 136
## 5 92e129 10 Recover f 42 40-44 -13.23573 8.474329 85 196
## 6 240da4 10 Death m 1 0-4 -13.26799 8.462502 17 47
## ct_blood fever chills cough aches vomit temp time_admission bmi
## 1 23 no no yes no no 37.2 09:21 72.88889
## 2 23 no yes yes no no 36.8 10:12 21.06674
## 3 23 no no no no yes 37.4 16:46 24.44444
## 4 22 no yes yes no no 36.6 13:52 31.89879
## 5 23 36.7 14:25 22.12620
## 6 23 no yes yes no yes 36.3 16:06 76.95790
## days_onset_hosp
## 1 1
## 2 1
## 3 2
## 4 2
## 5 2
## 6 0## [1] 454 20Handling missing data in the age and time_admission column
For the age and time_admissin col, this columns are important so i replaced the missing values in the age column with the mean, since they are just 9 missing rows.
## [1] NA## Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
## 0.0 7.0 15.0 17.4 24.0 87.0 9## [1] 9newcenthos$age <- ifelse(is.na(newcenthos$age) | newcenthos$age == "", mean(newcenthos$age, na.rm = TRUE), newcenthos$age)
newcenthos$age <- round(newcenthos$age)
head(newcenthos$age)## [1] 4 30 23 14 42 1Handling missing values in the time_admission column
For the time_admission column, there are some missing values there and i don’t want to just delete the rows, so i replaced them with 00:00
## [1] 63## Length Class Mode
## 454 character characternewcenthos$time_admission <- ifelse(is.na(newcenthos$time_admission) | newcenthos$time_admission == "","00:00", newcenthos$time_admission)
head(newcenthos$time_admission)## [1] "09:21" "10:12" "16:46" "13:52" "14:25" "16:06"## case_id generation outcome gender age
## 0 0 96 26 0
## age_cat5 lon lat wt_kg ht_cm
## 9 0 0 0 0
## ct_blood fever chills cough aches
## 0 26 26 26 26
## vomit temp time_admission bmi days_onset_hosp
## 26 3 0 0 0Removing all rows with missing data
After all this cleaning there are still some missing rows, so i would be removing them.
rows_with_missing <- newcenthos[rowSums(is.na(newcenthos)|newcenthos=="")>0, ]
head(rows_with_missing)## case_id generation outcome gender age age_cat5 lon lat wt_kg
## 5 92e129 10 Recover f 42 40-44 -13.23573 8.474329 85
## 8 9b4647 9 Death m 44 40-44 -13.21540 8.482433 73
## 10 24ef7d 11 Recover m 52 50-54 -13.22377 8.461412 90
## 12 f5e8d0 11 m 22 20-24 -13.25317 8.457906 77
## 13 7c67f1 14 Recover f 18 15-19 -13.21197 8.483053 59
## 14 5d6f13 7 Recover m 27 25-29 -13.26767 8.462242 71
## ht_cm ct_blood fever chills cough aches vomit temp time_admission bmi
## 5 196 23 36.7 14:25 22.12620
## 8 207 23 37.8 04:00 17.03657
## 10 236 22 37.5 13:46 16.15915
## 12 147 21 36.9 09:32 35.63330
## 13 185 22 37.8 00:00 17.23886
## 14 175 23 35.8 10:35 23.18367
## days_onset_hosp
## 5 2
## 8 0
## 10 2
## 12 0
## 13 0
## 14 2## [1] 141## Joining with `by = join_by(case_id, generation, outcome, gender, age, age_cat5,
## lon, lat, wt_kg, ht_cm, ct_blood, fever, chills, cough, aches, vomit, temp,
## time_admission, bmi, days_onset_hosp)`## [1] 313 20## case_id generation outcome gender age age_cat5 lon lat wt_kg ht_cm
## 1 275cc7 5 Death f 4 0-4 -13.23694 8.469161 41 75
## 2 64c8ef 6 Recover m 30 30-34 -13.26147 8.457506 66 177
## 3 e56412 9 Death f 23 20-24 -13.23433 8.478321 55 150
## 4 62a2ef 9 Death f 14 10-14 -13.23794 8.469774 59 136
## 5 240da4 10 Death m 1 0-4 -13.26799 8.462502 17 47
## 6 31e797 13 Death m 49 45-49 -13.26021 8.457613 82 220
## ct_blood fever chills cough aches vomit temp time_admission bmi
## 1 23 no no yes no no 37.2 09:21 72.88889
## 2 23 no yes yes no no 36.8 10:12 21.06674
## 3 23 no no no no yes 37.4 16:46 24.44444
## 4 22 no yes yes no no 36.6 13:52 31.89879
## 5 23 no yes yes no yes 36.3 16:06 76.95790
## 6 21 no no yes no no 36.2 13:10 16.94215
## days_onset_hosp
## 1 1
## 2 1
## 3 2
## 4 2
## 5 0
## 6 2## case_id generation outcome gender
## Length:313 Min. : 2.00 Length:313 Length:313
## Class :character 1st Qu.:13.00 Class :character Class :character
## Mode :character Median :16.00 Mode :character Mode :character
## Mean :16.93
## 3rd Qu.:20.00
## Max. :35.00
## age age_cat5 lon lat
## Min. : 0.00 Length:313 Min. :-13.27 Min. :8.449
## 1st Qu.: 7.00 Class :character 1st Qu.:-13.25 1st Qu.:8.460
## Median :15.00 Mode :character Median :-13.23 Median :8.468
## Mean :17.68 Mean :-13.23 Mean :8.469
## 3rd Qu.:24.00 3rd Qu.:-13.22 3rd Qu.:8.479
## Max. :73.00 Max. :-13.21 Max. :8.490
## wt_kg ht_cm ct_blood fever
## Min. : 0.00 Min. : 26.0 Min. :17.00 Length:313
## 1st Qu.: 44.00 1st Qu.: 96.0 1st Qu.:20.00 Class :character
## Median : 58.00 Median :135.0 Median :22.00 Mode :character
## Mean : 55.53 Mean :129.7 Mean :21.16
## 3rd Qu.: 68.00 3rd Qu.:161.0 3rd Qu.:22.00
## Max. :100.00 Max. :281.0 Max. :25.00
## chills cough aches vomit
## Length:313 Length:313 Length:313 Length:313
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## temp time_admission bmi days_onset_hosp
## Min. :35.90 Length:313 Min. : 0.00 Min. : 0.000
## 1st Qu.:38.20 Class :character 1st Qu.: 24.45 1st Qu.: 1.000
## Median :38.80 Mode :character Median : 32.63 Median : 1.000
## Mean :38.57 Mean : 44.15 Mean : 1.904
## 3rd Qu.:39.20 3rd Qu.: 47.90 3rd Qu.: 3.000
## Max. :40.40 Max. :370.37 Max. :11.000removing negative values from bmi and wt_kg
In this columns there are some in consistencies, some data have some negative signs which is not supposed to be there, so i removed it! cause bmi and wt_kg cant be negative!
## [1] 72.88889 21.06674 24.44444 31.89879 76.95790 16.94215## [1] 41 66 55 59 17 82Checking for outliers
Outliers are data points that are significantly different from other observation in a data set, so to check for outliers in this data, i would be using boxplot() function.
p1 <- ggplot(centralhospital, aes(age))+
geom_boxplot(fill = "skyblue")+
labs(title = "Age Distribution")
p2 <- ggplot(centralhospital, aes(ht_cm))+
geom_boxplot(fill = "skyblue")+
labs(title = "Height Distribution")
p3 <- ggplot(centralhospital, aes(bmi))+
geom_boxplot(fill = "skyblue")+
labs(title = "bmi Distribution")
p4 <- ggplot(centralhospital, aes(days_onset_hosp))+
geom_boxplot(fill = "skyblue")+
labs(title = "days onset distribution")
p5 <- ggplot(centralhospital, aes(wt_kg))+
geom_boxplot(fill = "skyblue")+
labs(title = "wt_kg Distribution")
p6 <- ggplot(centralhospital, aes(temp))+
geom_boxplot(fill = "skyblue")+
labs(title = "temp Distribution")
grid.arrange(p1,p2,p3,p4,p5,p6, ncol=3)
Bar plot of outcome against gender
This plot shows that more males dies than females, and dthe number of recovered are almost equal for both genders.
ggplot(centralhospital, aes(x = gender, fill = outcome))+
geom_bar(position = "dodge")+
labs(title = "Gender vs Outcome", x = "Gender", y = "count")+
scale_fill_manual(values = c("Recover" = "yellow","Death"="blue"))+
theme_minimal()
Plot of thhe outcome
This plot shows that more people died and less people recovered
ggplot(centralhospital, aes(x = outcome))+
geom_bar(fill = "orange")+
labs(title = "Outcome Distribution", x = "Outcome", y = "count")+
theme_minimal()
The Cognifyz data set contains detailed information on over 9,500 restaurants across various cities and countries. It includes data on restaurant names, locations, cuisines offered, average costs, delivery and booking options, customer ratings, and reviews. I would be analyzing trends, customer preferences, service availability, and geographic distribution of restaurants.
Aim
Research Questions
Objectives
Loading necessary libraries
library(dplyr)
library(labeling)
library(ggplot2)
library(sf)
library(ggmap)
library(dbscan)
library(labeling)
library(stringr)
library(tidyverse)
library(tidytext)
library(wordcloud)
library(textdata)Loading and checking for missing values
loading the data set and then checking for missing values, we have just 9 missing values, so we are removing it.
cognifyz <- read.csv("C:/Users/User/Desktop/GIT presentation/COGNIFYZ DATASET.csv")
head(cognifyz,5)## Restaurant.ID Restaurant.Name Country.Code City
## 1 6317637 Le Petit Souffle 162 Makati City
## 2 6304287 Izakaya Kikufuji 162 Makati City
## 3 6300002 Heat - Edsa Shangri-La 162 Mandaluyong City
## 4 6318506 Ooma 162 Mandaluyong City
## 5 6314302 Sambo Kojin 162 Mandaluyong City
## Address
## 1 Third Floor, Century City Mall, Kalayaan Avenue, Poblacion, Makati City
## 2 Little Tokyo, 2277 Chino Roces Avenue, Legaspi Village, Makati City
## 3 Edsa Shangri-La, 1 Garden Way, Ortigas, Mandaluyong City
## 4 Third Floor, Mega Fashion Hall, SM Megamall, Ortigas, Mandaluyong City
## 5 Third Floor, Mega Atrium, SM Megamall, Ortigas, Mandaluyong City
## Locality
## 1 Century City Mall, Poblacion, Makati City
## 2 Little Tokyo, Legaspi Village, Makati City
## 3 Edsa Shangri-La, Ortigas, Mandaluyong City
## 4 SM Megamall, Ortigas, Mandaluyong City
## 5 SM Megamall, Ortigas, Mandaluyong City
## Locality.Verbose Longitude
## 1 Century City Mall, Poblacion, Makati City, Makati City 121.0275
## 2 Little Tokyo, Legaspi Village, Makati City, Makati City 121.0141
## 3 Edsa Shangri-La, Ortigas, Mandaluyong City, Mandaluyong City 121.0568
## 4 SM Megamall, Ortigas, Mandaluyong City, Mandaluyong City 121.0565
## 5 SM Megamall, Ortigas, Mandaluyong City, Mandaluyong City 121.0575
## Latitude Cuisines Average.Cost.for.two
## 1 14.56544 French, Japanese, Desserts 1100
## 2 14.55371 Japanese 1200
## 3 14.58140 Seafood, Asian, Filipino, Indian 4000
## 4 14.58532 Japanese, Sushi 1500
## 5 14.58445 Japanese, Korean 1500
## Currency Has.Table.booking Has.Online.delivery Is.delivering.now
## 1 Botswana Pula(P) Yes No No
## 2 Botswana Pula(P) Yes No No
## 3 Botswana Pula(P) Yes No No
## 4 Botswana Pula(P) No No No
## 5 Botswana Pula(P) Yes No No
## Switch.to.order.menu Price.range Aggregate.rating Rating.color Rating.text
## 1 No 3 4.8 Dark Green Excellent
## 2 No 3 4.5 Dark Green Excellent
## 3 No 4 4.4 Green Very Good
## 4 No 4 4.9 Dark Green Excellent
## 5 No 4 4.8 Dark Green Excellent
## Votes
## 1 314
## 2 591
## 3 270
## 4 365
## 5 229## Restaurant.ID Restaurant.Name Country.Code City
## Min. : 53 Length:9551 Min. : 1.00 Length:9551
## 1st Qu.: 301962 Class :character 1st Qu.: 1.00 Class :character
## Median : 6004089 Mode :character Median : 1.00 Mode :character
## Mean : 9051128 Mean : 18.37
## 3rd Qu.:18352292 3rd Qu.: 1.00
## Max. :18500652 Max. :216.00
## Address Locality Locality.Verbose Longitude
## Length:9551 Length:9551 Length:9551 Min. :-157.95
## Class :character Class :character Class :character 1st Qu.: 77.08
## Mode :character Mode :character Mode :character Median : 77.19
## Mean : 64.13
## 3rd Qu.: 77.28
## Max. : 174.83
## Latitude Cuisines Average.Cost.for.two Currency
## Min. :-41.33 Length:9551 Min. : 0 Length:9551
## 1st Qu.: 28.48 Class :character 1st Qu.: 250 Class :character
## Median : 28.57 Mode :character Median : 400 Mode :character
## Mean : 25.85 Mean : 1199
## 3rd Qu.: 28.64 3rd Qu.: 700
## Max. : 55.98 Max. :800000
## Has.Table.booking Has.Online.delivery Is.delivering.now Switch.to.order.menu
## Length:9551 Length:9551 Length:9551 Length:9551
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Price.range Aggregate.rating Rating.color Rating.text
## Min. :1.000 Min. :0.000 Length:9551 Length:9551
## 1st Qu.:1.000 1st Qu.:2.500 Class :character Class :character
## Median :2.000 Median :3.200 Mode :character Mode :character
## Mean :1.805 Mean :2.666
## 3rd Qu.:2.000 3rd Qu.:3.700
## Max. :4.000 Max. :4.900
## Votes
## Min. : 0.0
## 1st Qu.: 5.0
## Median : 31.0
## Mean : 156.9
## 3rd Qu.: 131.0
## Max. :10934.0## [1] 9checking for missing rows and removing them
# Checking for missing rows in each columns
na_location <- which(is.na(cognifyz)|cognifyz=="", arr.ind = TRUE)
na_location## row col
## [1,] 85 10
## [2,] 88 10
## [3,] 95 10
## [4,] 298 10
## [5,] 329 10
## [6,] 347 10
## [7,] 369 10
## [8,] 419 10
## [9,] 456 10#I removed rows with missing data since the rows are not to much
cognifyz_1 <- cognifyz[!is.na(cognifyz$Cuisines) & cognifyz$Cuisines != "", ]
head(cognifyz_1,3)## Restaurant.ID Restaurant.Name Country.Code City
## 1 6317637 Le Petit Souffle 162 Makati City
## 2 6304287 Izakaya Kikufuji 162 Makati City
## 3 6300002 Heat - Edsa Shangri-La 162 Mandaluyong City
## Address
## 1 Third Floor, Century City Mall, Kalayaan Avenue, Poblacion, Makati City
## 2 Little Tokyo, 2277 Chino Roces Avenue, Legaspi Village, Makati City
## 3 Edsa Shangri-La, 1 Garden Way, Ortigas, Mandaluyong City
## Locality
## 1 Century City Mall, Poblacion, Makati City
## 2 Little Tokyo, Legaspi Village, Makati City
## 3 Edsa Shangri-La, Ortigas, Mandaluyong City
## Locality.Verbose Longitude
## 1 Century City Mall, Poblacion, Makati City, Makati City 121.0275
## 2 Little Tokyo, Legaspi Village, Makati City, Makati City 121.0141
## 3 Edsa Shangri-La, Ortigas, Mandaluyong City, Mandaluyong City 121.0568
## Latitude Cuisines Average.Cost.for.two
## 1 14.56544 French, Japanese, Desserts 1100
## 2 14.55371 Japanese 1200
## 3 14.58140 Seafood, Asian, Filipino, Indian 4000
## Currency Has.Table.booking Has.Online.delivery Is.delivering.now
## 1 Botswana Pula(P) Yes No No
## 2 Botswana Pula(P) Yes No No
## 3 Botswana Pula(P) Yes No No
## Switch.to.order.menu Price.range Aggregate.rating Rating.color Rating.text
## 1 No 3 4.8 Dark Green Excellent
## 2 No 3 4.5 Dark Green Excellent
## 3 No 4 4.4 Green Very Good
## Votes
## 1 314
## 2 591
## 3 270Determining the three most common cuisines
#Level 1 Task 1: Determine the top three most common cuisines in the data set
top3Cuisines <- cognifyz_1 %>%
count(Cuisines) %>%
arrange(desc(n)) %>%
head(3)
print(top3Cuisines)## Cuisines n
## 1 North Indian 936
## 2 North Indian, Chinese 511
## 3 Chinese 354calculate percentage of retaurants that serve each of the top cuicines
#Calculate the percentage of restaurants that serve each of the top cuisines.
#the first step is to get the total no of restaurants
total_no_restaurants <- nrow(cognifyz_1)
total_no_restaurants## [1] 9542#counting number of entries in the cuisines column
no_ofentries_cuisine <- cognifyz_1 %>%
count(Cuisines)
head(no_ofentries_cuisine,)## Cuisines n
## 1 Afghani 4
## 2 Afghani, Mughlai, Chinese 1
## 3 Afghani, North Indian 1
## 4 Afghani, North Indian, Pakistani, Arabian 1
## 5 African 1
## 6 African, Portuguese 1#get the top 3 cuisines
top3cuisines <-no_ofentries_cuisine %>%
arrange(desc(n))%>%
head(3)
print(top3cuisines)## Cuisines n
## 1 North Indian 936
## 2 North Indian, Chinese 511
## 3 Chinese 354#percentage of restaurants that serve each of the top cuisines
top3cuisinesPER <- top3cuisines %>%
mutate(percentage = (n / total_no_restaurants) * 100)
print(top3cuisinesPER)## Cuisines n percentage
## 1 North Indian 936 9.809264
## 2 North Indian, Chinese 511 5.355271
## 3 Chinese 354 3.709914#level 1 task 2
#Identify the city with the highest number of restaurants in the data set.
# first step is to Count the number of restaurants in each city
city_counts <- cognifyz_1 %>%
count(City) %>%
arrange(desc(n))
#Displaying the city with the highest number of restaurants
higest_no_res <- city_counts[1,]
print(higest_no_res)## City n
## 1 New Delhi 5473# Calculate the average rating for restaurants in each city
ave_ratings <- cognifyz_1 %>%
group_by(City) %>%
summarise(avg_rating = mean(Aggregate.rating)) %>%
arrange(desc(avg_rating))
print(ave_ratings)## # A tibble: 140 × 2
## City avg_rating
## <chr> <dbl>
## 1 Inner City 4.9
## 2 Quezon City 4.8
## 3 Makati City 4.65
## 4 Pasig City 4.63
## 5 Mandaluyong City 4.62
## 6 Beechworth 4.6
## 7 London 4.54
## 8 Taguig City 4.53
## 9 Lincoln 4.5
## 10 Secunderabad 4.5
## # ℹ 130 more rows#Determine the city with the highest average rating
city_highest_rating <- ave_ratings[1, ]
print(city_highest_rating)## # A tibble: 1 × 2
## City avg_rating
## <chr> <dbl>
## 1 Inner City 4.9#Level 1 task 3 Price Range Distribution
#Create a histogram or bar chart to visualize the distribution of price ranges among the restaurants.
ggplot(cognifyz_1, aes(x = Price.range)) +
geom_bar(fill = "skyblue", color = "black") +
#geom_histogram(bindwidth = 0.5, boundary = 0) +
labs(
title = "Distribution of Price Ranges Among Restaurants",
x = "Price Range",
y = "Count"
) +
theme_minimal()
#Calculate the percentage of restaurants in each price range category.
percentage_restaurants <- cognifyz_1 %>%
group_by(Price.range) %>%
summarise(count = n()) %>%
mutate(percentage = (count / sum(count)) * 100)
print(percentage_restaurants)## # A tibble: 4 × 3
## Price.range count percentage
## <int> <int> <dbl>
## 1 1 4438 46.5
## 2 2 3113 32.6
## 3 3 1405 14.7
## 4 4 586 6.14# #so i decided to plot the graph of the percentage of restaurants in each category
# ggplot(percentage_restaurants, aes(x = `Price.range`, y = percentage)) +
# geom_bar(stat = "identity", fill = "skyblue", color = "black") +
# labs(
# title = "Percentage of Restaurants in Each Price Range category",
# x = "Price Range",
# y = "Percentage (%)"
# ) +
# theme_minimal()
#level 1 Task 4
#Task: Online Delivery
#Determine the percentage of restaurants that offer online delivery.
online_delivery_percentage <- cognifyz_1 %>%
summarise(
total_restaurants = n(),
num_online_delivery = sum(Has.Online.delivery == "Yes", na.rm = TRUE)
) %>%
mutate(percentage_online_delivery = (num_online_delivery / total_restaurants) * 100)
print(online_delivery_percentage)## total_restaurants num_online_delivery percentage_online_delivery
## 1 9542 2451 25.68644#Compare the average ratings of restaurants with and without online delivery
ave_ratings_online_delivery <- cognifyz_1 %>%
group_by(Has.Online.delivery) %>%
summarise(avg_rating = mean(Aggregate.rating, na.rm = TRUE))
print(ave_ratings_online_delivery)## # A tibble: 2 × 2
## Has.Online.delivery avg_rating
## <chr> <dbl>
## 1 No 2.46
## 2 Yes 3.25#Creating a bar chart to compare this ratings
#i did this out of curiosity
ggplot(ave_ratings_online_delivery, aes(x = Has.Online.delivery, y = avg_rating, fill = Has.Online.delivery)) +
geom_bar(stat = "identity") +
labs(
title = "Average Ratings Based on Online Delivery",
x = "Online Delivery",
y = "Average Rating"
) +
theme_minimal()
#setting the working directory
setwd("C:/Users/User/Desktop/GIT presentation")
cognifyz_1 <- read.csv("Cleaned_Dataset.csv")
view(cognifyz_1)
#level 1 task 1
#Analyze the text reviews to identify the most common positive and negative keywords.
# Convert cognifyz_1 into a tidy format (word tokens)
review_words <-cognifyz_1 %>%
unnest_tokens(word, Rating.text) %>%
anti_join(stop_words) ## Joining with `by = join_by(word)`## Restaurant.ID Restaurant.Name Country.Code
## 1 6317637 Le Petit Souffle 162
## 2 6304287 Izakaya Kikufuji 162
## 3 6318506 Ooma 162
## 4 6314302 Sambo Kojin 162
## 5 6300010 Spiral - Sofitel Philippine Plaza Manila 162
## 6 6314987 Locavore 162
## City
## 1 Makati City
## 2 Makati City
## 3 Mandaluyong City
## 4 Mandaluyong City
## 5 Pasay City
## 6 Pasig City
## Address
## 1 Third Floor, Century City Mall, Kalayaan Avenue, Poblacion, Makati City
## 2 Little Tokyo, 2277 Chino Roces Avenue, Legaspi Village, Makati City
## 3 Third Floor, Mega Fashion Hall, SM Megamall, Ortigas, Mandaluyong City
## 4 Third Floor, Mega Atrium, SM Megamall, Ortigas, Mandaluyong City
## 5 Plaza Level, Sofitel Philippine Plaza Manila, CCP Complex, Pasay City
## 6 Brixton Technology Center, 10 Brixton Street, Kapitolyo, Pasig City
## Locality
## 1 Century City Mall, Poblacion, Makati City
## 2 Little Tokyo, Legaspi Village, Makati City
## 3 SM Megamall, Ortigas, Mandaluyong City
## 4 SM Megamall, Ortigas, Mandaluyong City
## 5 Sofitel Philippine Plaza Manila, Pasay City
## 6 Kapitolyo
## Locality.Verbose Longitude Latitude
## 1 Century City Mall, Poblacion, Makati City, Makati City 121.0275 14.56544
## 2 Little Tokyo, Legaspi Village, Makati City, Makati City 121.0141 14.55371
## 3 SM Megamall, Ortigas, Mandaluyong City, Mandaluyong City 121.0565 14.58532
## 4 SM Megamall, Ortigas, Mandaluyong City, Mandaluyong City 121.0575 14.58445
## 5 Sofitel Philippine Plaza Manila, Pasay City, Pasay City 120.9801 14.55299
## 6 Kapitolyo, Pasig City 121.0565 14.57204
## Cuisines Average.Cost.for.two Currency
## 1 French, Japanese, Desserts 1100 Botswana Pula(P)
## 2 Japanese 1200 Botswana Pula(P)
## 3 Japanese, Sushi 1500 Botswana Pula(P)
## 4 Japanese, Korean 1500 Botswana Pula(P)
## 5 European, Asian, Indian 6000 Botswana Pula(P)
## 6 Filipino 1100 Botswana Pula(P)
## Has.Table.booking Has.Online.delivery Is.delivering.now Switch.to.order.menu
## 1 Yes No No No
## 2 Yes No No No
## 3 No No No No
## 4 Yes No No No
## 5 Yes No No No
## 6 Yes No No No
## Price.range Aggregate.rating Rating.color Votes word
## 1 3 4.8 Dark Green 314 excellent
## 2 3 4.5 Dark Green 591 excellent
## 3 4 4.9 Dark Green 365 excellent
## 4 4 4.8 Dark Green 229 excellent
## 5 4 4.9 Dark Green 621 excellent
## 6 3 4.8 Dark Green 532 excellent# Load sentiment lexicon (Bing)
bing_lexicon <- get_sentiments("bing")
# Match words with sentiment lexicon
review_sentiment <- review_words %>%
inner_join(bing_lexicon, by = "word")
# Count most common positive and negative words
top_words <- review_sentiment %>%
count(word, sentiment, sort = TRUE)
# View top words
head(top_words)## word sentiment n
## 1 excellent positive 300
## 2 poor negative 186# Plot top 10 positive & negative words
top_words %>%
group_by(sentiment) %>%
top_n(10) %>%
ungroup() %>%
ggplot(aes(x = reorder(word, n), y = n, fill = sentiment)) +
geom_col(show.legend = FALSE) +
facet_wrap(~sentiment, scales = "free_y") +
coord_flip() +
labs(title = "Most Common Positive & Negative Words", x = "Word", y = "Count") +
theme_minimal()## Selecting by n
#Calculate the average length of reviews and explore if there is a relationship between review length and rating.
# Compute review length (word count)
reviews <- cognifyz_1 %>%
mutate(review_length = str_count(Rating.text, "\\w+"))
head(reviews)## Restaurant.ID Restaurant.Name Country.Code City
## 1 6317637 Le Petit Souffle 162 Makati City
## 2 6304287 Izakaya Kikufuji 162 Makati City
## 3 6300002 Heat - Edsa Shangri-La 162 Mandaluyong City
## 4 6318506 Ooma 162 Mandaluyong City
## 5 6314302 Sambo Kojin 162 Mandaluyong City
## 6 18189371 Din Tai Fung 162 Mandaluyong City
## Address
## 1 Third Floor, Century City Mall, Kalayaan Avenue, Poblacion, Makati City
## 2 Little Tokyo, 2277 Chino Roces Avenue, Legaspi Village, Makati City
## 3 Edsa Shangri-La, 1 Garden Way, Ortigas, Mandaluyong City
## 4 Third Floor, Mega Fashion Hall, SM Megamall, Ortigas, Mandaluyong City
## 5 Third Floor, Mega Atrium, SM Megamall, Ortigas, Mandaluyong City
## 6 Ground Floor, Mega Fashion Hall, SM Megamall, Ortigas, Mandaluyong City
## Locality
## 1 Century City Mall, Poblacion, Makati City
## 2 Little Tokyo, Legaspi Village, Makati City
## 3 Edsa Shangri-La, Ortigas, Mandaluyong City
## 4 SM Megamall, Ortigas, Mandaluyong City
## 5 SM Megamall, Ortigas, Mandaluyong City
## 6 SM Megamall, Ortigas, Mandaluyong City
## Locality.Verbose Longitude
## 1 Century City Mall, Poblacion, Makati City, Makati City 121.0275
## 2 Little Tokyo, Legaspi Village, Makati City, Makati City 121.0141
## 3 Edsa Shangri-La, Ortigas, Mandaluyong City, Mandaluyong City 121.0568
## 4 SM Megamall, Ortigas, Mandaluyong City, Mandaluyong City 121.0565
## 5 SM Megamall, Ortigas, Mandaluyong City, Mandaluyong City 121.0575
## 6 SM Megamall, Ortigas, Mandaluyong City, Mandaluyong City 121.0563
## Latitude Cuisines Average.Cost.for.two
## 1 14.56544 French, Japanese, Desserts 1100
## 2 14.55371 Japanese 1200
## 3 14.58140 Seafood, Asian, Filipino, Indian 4000
## 4 14.58532 Japanese, Sushi 1500
## 5 14.58445 Japanese, Korean 1500
## 6 14.58376 Chinese 1000
## Currency Has.Table.booking Has.Online.delivery Is.delivering.now
## 1 Botswana Pula(P) Yes No No
## 2 Botswana Pula(P) Yes No No
## 3 Botswana Pula(P) Yes No No
## 4 Botswana Pula(P) No No No
## 5 Botswana Pula(P) Yes No No
## 6 Botswana Pula(P) No No No
## Switch.to.order.menu Price.range Aggregate.rating Rating.color Rating.text
## 1 No 3 4.8 Dark Green Excellent
## 2 No 3 4.5 Dark Green Excellent
## 3 No 4 4.4 Green Very Good
## 4 No 4 4.9 Dark Green Excellent
## 5 No 4 4.8 Dark Green Excellent
## 6 No 3 4.4 Green Very Good
## Votes review_length
## 1 314 1
## 2 591 1
## 3 270 2
## 4 365 1
## 5 229 1
## 6 336 2## [1] "Restaurant.ID" "Restaurant.Name" "Country.Code"
## [4] "City" "Address" "Locality"
## [7] "Locality.Verbose" "Longitude" "Latitude"
## [10] "Cuisines" "Average.Cost.for.two" "Currency"
## [13] "Has.Table.booking" "Has.Online.delivery" "Is.delivering.now"
## [16] "Switch.to.order.menu" "Price.range" "Aggregate.rating"
## [19] "Rating.color" "Rating.text" "Votes"
## [22] "review_length"# Calculate average length
avg_length <- mean(reviews$review_length, na.rm = TRUE)
# Print result
print(paste("Average review length:", round(avg_length, 2)))## [1] "Average review length: 1.34"# Scatter plot: Review Length vs Aggregate Rating
ggplot(reviews, aes(x = review_length, y =Aggregate.rating)) +
geom_point(alpha = 0.5, color = "blue") +
geom_smooth(method = "lm", color = "red") + # Add trendline
labs(title = "Review Length vs Rating", x = "Review Length (words)", y = "Aggregate Rating") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
#task 2
#Identify the restaurants with the highest and lowest number of votes.
#restaurants with highest votes
top_restaurants <- cognifyz_1%>%
arrange(desc(Votes))
head(top_restaurants, 5) # Top 5 restaurants with the highest votes## Restaurant.ID Restaurant.Name Country.Code City
## 1 51705 Toit 1 Bangalore
## 2 51040 Truffles 1 Bangalore
## 3 308322 Hauz Khas Social 1 New Delhi
## 4 20404 Peter Cat 1 Kolkata
## 5 56618 AB's - Absolute Barbecues 1 Bangalore
## Address
## 1 298, Namma Metro Pillar 62, 100 Feet Road, Indiranagar, Bangalore
## 2 28, 4th 'B' Cross, Koramangala 5th Block, Bangalore
## 3 9-A & 12, Hauz Khas Village, New Delhi
## 4 18A, Park Street, Park Street Area, Kolkata
## 5 90/4, 3rd Floor, Outer Ring Road, Munnekollaly Village, Marathahalli, Bangalore
## Locality Locality.Verbose Longitude Latitude
## 1 Indiranagar Indiranagar, Bangalore 77.64071 12.97917
## 2 Koramangala 5th Block Koramangala 5th Block, Bangalore 77.61429 12.93330
## 3 Hauz Khas Village Hauz Khas Village, New Delhi 77.19447 28.55429
## 4 Park Street Area Park Street Area, Kolkata 88.35289 22.55267
## 5 Marathahalli Marathahalli, Bangalore 77.69939 12.94993
## Cuisines Average.Cost.for.two
## 1 Italian, American, Pizza 2000
## 2 American, Burger, Cafe 800
## 3 Continental, American, Asian, North Indian 1600
## 4 Continental, North Indian 1000
## 5 European, Mediterranean, North Indian 1400
## Currency Has.Table.booking Has.Online.delivery Is.delivering.now
## 1 Indian Rupees(Rs.) No No No
## 2 Indian Rupees(Rs.) No Yes No
## 3 Indian Rupees(Rs.) Yes Yes No
## 4 Indian Rupees(Rs.) No Yes No
## 5 Indian Rupees(Rs.) No No No
## Switch.to.order.menu Price.range Aggregate.rating Rating.color Rating.text
## 1 No 4 4.8 Dark Green Excellent
## 2 No 2 4.7 Dark Green Excellent
## 3 No 3 4.3 Green Very Good
## 4 No 3 4.3 Green Very Good
## 5 No 3 4.6 Dark Green Excellent
## Votes
## 1 10934
## 2 9667
## 3 7931
## 4 7574
## 5 6907#restaurants with lowest votes
bottom_restaurants <- cognifyz_1 %>% arrange(Votes)
head(bottom_restaurants, 5) # Top 5 restaurants with the lowest votes## Restaurant.ID Restaurant.Name Country.Code City
## 1 6710645 Cantinho da Gula 30 S��o Paulo
## 2 18433852 The Chaiwalas 1 Faridabad
## 3 18465871 Fusion Food Corner 1 Faridabad
## 4 18472646 Punjabi Rasoi 1 Faridabad
## 5 18471268 Baskin Robbin 1 Faridabad
## Address
## 1 Rua Pedroso Alvarenga, 522, Itaim Bibi, S��o Paulo, SP
## 2 Sector 21 A, Asian Hospital, Badhkal Chowk, Badkal Lake, Faridabad
## 3 158/7, Opposite DDA Flat, Pul Pehlad Pur, Charmwood Village, Faridabad
## 4 1, Deepak Complex, Eros Charmwood Village, Faridabad
## 5 Ground Floor, Crown Interiorz Mall, Sector 35, Faridabad
## Locality
## 1 Itaim Bibi
## 2 Badkal Lake
## 3 Charmwood Village
## 4 Charmwood Village
## 5 Crown Interiorz Mall, Sector 35, Faridabad
## Locality.Verbose Longitude Latitude
## 1 Itaim Bibi, S��o Paulo -46.67567 -23.58100
## 2 Badkal Lake, Faridabad 77.30009 28.42628
## 3 Charmwood Village, Faridabad 0.00000 0.00000
## 4 Charmwood Village, Faridabad 77.29243 28.49233
## 5 Crown Interiorz Mall, Sector 35, Faridabad, Faridabad 77.30745 28.46959
## Cuisines Average.Cost.for.two Currency
## 1 Brazilian 55 Brazilian Real(R$)
## 2 Cafe 300 Indian Rupees(Rs.)
## 3 North Indian, Chinese 300 Indian Rupees(Rs.)
## 4 North Indian 400 Indian Rupees(Rs.)
## 5 Desserts 300 Indian Rupees(Rs.)
## Has.Table.booking Has.Online.delivery Is.delivering.now Switch.to.order.menu
## 1 No No No No
## 2 No No No No
## 3 No No No No
## 4 No No No No
## 5 No No No No
## Price.range Aggregate.rating Rating.color Rating.text Votes
## 1 2 0 White Not rated 0
## 2 1 0 White Not rated 0
## 3 1 0 White Not rated 0
## 4 1 0 White Not rated 0
## 5 1 0 White Not rated 0#Analyze if there is a correlation between the number of votes and the rating of a restaurant.
# Group by restaurant name and compute total votes and average rating
votes_summary <- cognifyz_1 %>%
group_by(Restaurant.Name) %>%
summarise(
total_votes = sum(Votes), # or use n() if each row represents one vote
avg_rating = mean(Aggregate.rating, na.rm = TRUE)
)
# Highest total votes
top_restaurants <- votes_summary %>% arrange(desc(total_votes))
head(top_restaurants, 5)## # A tibble: 5 × 3
## Restaurant.Name total_votes avg_rating
## <chr> <int> <dbl>
## 1 Barbeque Nation 28142 4.35
## 2 AB's - Absolute Barbecues 13400 4.82
## 3 Toit 10934 4.8
## 4 Big Chill 10853 4.47
## 5 Farzi Cafe 10098 4.37# Lowest total votes
bottom_restaurants <- votes_summary %>% arrange(total_votes)
head(bottom_restaurants, 5)## # A tibble: 5 × 3
## Restaurant.Name total_votes avg_rating
## <chr> <int> <dbl>
## 1 #hashtag 0 0
## 2 13 Cafe 0 0
## 3 3x Cafe 0 0
## 4 44 Grills 0 0
## 5 6 Packs Momos 0 0# If using the aggregated data
correlation <- cor(votes_summary$total_votes, votes_summary$avg_rating, use = "complete.obs")
print(paste("Correlation between votes and rating:", round(correlation, 2)))## [1] "Correlation between votes and rating: 0.24"# Scatter plot with trendline using ggplot2
ggplot(votes_summary, aes(x = total_votes, y = avg_rating)) +
geom_point(color = "blue", alpha = 0.6) +
geom_smooth(method = "lm", color = "red") +
labs(title = "Relationship Between Votes and Rating",
x = "Total Votes",
y = "Average Rating") +
theme_minimal()## `geom_smooth()` using formula = 'y ~ x'
#task 3
#Analyze if there is a relationship between the
#price range and the availability of online
#delivery and table booking.
# Create a contingency table
online_delivery_table <- table(cognifyz_1$Price.range, cognifyz_1$Has.Online.delivery)
print(online_delivery_table)##
## No Yes
## 1 3737 701
## 2 1827 1286
## 3 994 411
## 4 533 53# Create a contingency table for table booking
table_booking_table <- table(cognifyz_1$Price.range, cognifyz_1$Has.Table.booking)
print(table_booking_table)##
## No Yes
## 1 4437 1
## 2 2874 239
## 3 761 644
## 4 312 274# Plot online delivery vs. price range
ggplot(cognifyz_1, aes(x = Price.range, fill = Has.Online.delivery)) +
geom_bar(position = "fill") + # "fill" shows proportions
labs(title = "Proportion of Restaurants Offering Online Delivery by Price Range",
x = "Price Range",
y = "Proportion",
fill = "Online Delivery") +
theme_minimal()
# Plot table booking vs. price range
ggplot(cognifyz_1, aes(x = Price.range, fill = Has.Table.booking)) +
geom_bar(position = "fill") +
labs(title = "Proportion of Restaurants Offering Table Booking by Price Range",
x = "Price Range",
y = "Proportion",
fill = "Table Booking") +
theme_minimal()
The Oral Cancer Prediction Data set contains patient data used to predict the likelihood of developing oral cancer. It typically includes features like age, gender, lifestyle habits (such as tobacco or alcohol use), and possibly clinical indicators.
Aim
The aim of this project was to
Build a machine learning model to predict oral cancer diagnosis based on various patient and behavioral attributes (such as age, tobacco use, country, etc.). (to do later)
understanding key factors that contribute to the likelihood of developing oral cancer.
Research Questions
Objectives
# Load necessary libraries
library(tidyverse)
library(ggplot2)
library(dplyr)
library(caret)
library(corrplot)
library(ggpubr)
library(skimr)## ID Country Age Gender Tobacco.Use Alcohol.Consumption HPV.Infection
## 1 1 Italy 36 Female Yes Yes Yes
## 2 2 Japan 64 Male Yes Yes Yes
## 3 3 UK 37 Female No Yes No
## 4 4 Sri Lanka 55 Male Yes Yes No
## 5 5 South Africa 68 Male No No No
## 6 6 Taiwan 70 Male Yes No Yes
## Betel.Quid.Use Chronic.Sun.Exposure Poor.Oral.Hygiene
## 1 No No Yes
## 2 No Yes Yes
## 3 No Yes Yes
## 4 Yes No Yes
## 5 No No Yes
## 6 Yes No Yes
## Diet..Fruits...Vegetables.Intake. Family.History.of.Cancer
## 1 Low No
## 2 High No
## 3 Moderate No
## 4 Moderate No
## 5 High No
## 6 Moderate Yes
## Compromised.Immune.System Oral.Lesions Unexplained.Bleeding
## 1 No No No
## 2 No No Yes
## 3 No No No
## 4 No Yes No
## 5 No No No
## 6 No Yes Yes
## Difficulty.Swallowing White.or.Red.Patches.in.Mouth Tumor.Size..cm.
## 1 No No 0.000000
## 2 No No 1.782186
## 3 No Yes 3.523895
## 4 No No 0.000000
## 5 No No 2.834789
## 6 No No 1.692675
## Cancer.Stage Treatment.Type Survival.Rate..5.Year.... Cost.of.Treatment..USD.
## 1 0 No Treatment 100.00000 0.00
## 2 1 No Treatment 83.34010 77772.50
## 3 2 Surgery 63.22287 101164.50
## 4 0 No Treatment 100.00000 0.00
## 5 3 No Treatment 44.29320 45354.75
## 6 2 Surgery 67.40727 96504.00
## Economic.Burden..Lost.Workdays.per.Year. Early.Diagnosis
## 1 0 No
## 2 177 No
## 3 130 Yes
## 4 0 Yes
## 5 52 No
## 6 91 Yes
## Oral.Cancer..Diagnosis.
## 1 No
## 2 Yes
## 3 Yes
## 4 No
## 5 Yes
## 6 Yes## ID Country Age Gender
## Min. : 1 Length:84922 Min. : 15.00 Length:84922
## 1st Qu.:21231 Class :character 1st Qu.: 48.00 Class :character
## Median :42462 Mode :character Median : 55.00 Mode :character
## Mean :42462 Mean : 54.51
## 3rd Qu.:63692 3rd Qu.: 61.00
## Max. :84922 Max. :101.00
## Tobacco.Use Alcohol.Consumption HPV.Infection Betel.Quid.Use
## Length:84922 Length:84922 Length:84922 Length:84922
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
## Chronic.Sun.Exposure Poor.Oral.Hygiene Diet..Fruits...Vegetables.Intake.
## Length:84922 Length:84922 Length:84922
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## Family.History.of.Cancer Compromised.Immune.System Oral.Lesions
## Length:84922 Length:84922 Length:84922
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## Unexplained.Bleeding Difficulty.Swallowing White.or.Red.Patches.in.Mouth
## Length:84922 Length:84922 Length:84922
## Class :character Class :character Class :character
## Mode :character Mode :character Mode :character
##
##
##
## Tumor.Size..cm. Cancer.Stage Treatment.Type Survival.Rate..5.Year....
## Min. :0.000 Min. :0.000 Length:84922 Min. : 10.00
## 1st Qu.:0.000 1st Qu.:0.000 Class :character 1st Qu.: 65.23
## Median :0.000 Median :0.000 Mode :character Median :100.00
## Mean :1.747 Mean :1.119 Mean : 79.50
## 3rd Qu.:3.480 3rd Qu.:2.000 3rd Qu.:100.00
## Max. :6.000 Max. :4.000 Max. :100.00
## Cost.of.Treatment..USD. Economic.Burden..Lost.Workdays.per.Year.
## Min. : 0 Min. : 0.00
## 1st Qu.: 0 1st Qu.: 0.00
## Median : 0 Median : 0.00
## Mean : 39110 Mean : 52.03
## 3rd Qu.: 76468 3rd Qu.:104.00
## Max. :159988 Max. :179.00
## Early.Diagnosis Oral.Cancer..Diagnosis.
## Length:84922 Length:84922
## Class :character Class :character
## Mode :character Mode :character
##
##
## ## [1] 0## [1] 0#I have been able to clarify that there are no missing or empty columns in this data set
# View data set structure
str(cancer)## 'data.frame': 84922 obs. of 25 variables:
## $ ID : int 1 2 3 4 5 6 7 8 9 10 ...
## $ Country : chr "Italy" "Japan" "UK" "Sri Lanka" ...
## $ Age : int 36 64 37 55 68 70 41 53 62 50 ...
## $ Gender : chr "Female" "Male" "Female" "Male" ...
## $ Tobacco.Use : chr "Yes" "Yes" "No" "Yes" ...
## $ Alcohol.Consumption : chr "Yes" "Yes" "Yes" "Yes" ...
## $ HPV.Infection : chr "Yes" "Yes" "No" "No" ...
## $ Betel.Quid.Use : chr "No" "No" "No" "Yes" ...
## $ Chronic.Sun.Exposure : chr "No" "Yes" "Yes" "No" ...
## $ Poor.Oral.Hygiene : chr "Yes" "Yes" "Yes" "Yes" ...
## $ Diet..Fruits...Vegetables.Intake. : chr "Low" "High" "Moderate" "Moderate" ...
## $ Family.History.of.Cancer : chr "No" "No" "No" "No" ...
## $ Compromised.Immune.System : chr "No" "No" "No" "No" ...
## $ Oral.Lesions : chr "No" "No" "No" "Yes" ...
## $ Unexplained.Bleeding : chr "No" "Yes" "No" "No" ...
## $ Difficulty.Swallowing : chr "No" "No" "No" "No" ...
## $ White.or.Red.Patches.in.Mouth : chr "No" "No" "Yes" "No" ...
## $ Tumor.Size..cm. : num 0 1.78 3.52 0 2.83 ...
## $ Cancer.Stage : int 0 1 2 0 3 2 1 0 3 3 ...
## $ Treatment.Type : chr "No Treatment" "No Treatment" "Surgery" "No Treatment" ...
## $ Survival.Rate..5.Year.... : num 100 83.3 63.2 100 44.3 ...
## $ Cost.of.Treatment..USD. : num 0 77773 101165 0 45355 ...
## $ Economic.Burden..Lost.Workdays.per.Year.: int 0 177 130 0 52 91 105 0 136 82 ...
## $ Early.Diagnosis : chr "No" "No" "Yes" "Yes" ...
## $ Oral.Cancer..Diagnosis. : chr "No" "Yes" "Yes" "No" ...| Name | cancer |
| Number of rows | 84922 |
| Number of columns | 25 |
| _______________________ | |
| Column type frequency: | |
| character | 18 |
| numeric | 7 |
| ________________________ | |
| Group variables | None |
Variable type: character
| skim_variable | n_missing | complete_rate | min | max | empty | n_unique | whitespace |
|---|---|---|---|---|---|---|---|
| Country | 0 | 1 | 2 | 12 | 0 | 17 | 0 |
| Gender | 0 | 1 | 4 | 6 | 0 | 2 | 0 |
| Tobacco.Use | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Alcohol.Consumption | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| HPV.Infection | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Betel.Quid.Use | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Chronic.Sun.Exposure | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Poor.Oral.Hygiene | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Diet..Fruits…Vegetables.Intake. | 0 | 1 | 3 | 8 | 0 | 3 | 0 |
| Family.History.of.Cancer | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Compromised.Immune.System | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Oral.Lesions | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Unexplained.Bleeding | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Difficulty.Swallowing | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| White.or.Red.Patches.in.Mouth | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Treatment.Type | 0 | 1 | 7 | 16 | 0 | 5 | 0 |
| Early.Diagnosis | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
| Oral.Cancer..Diagnosis. | 0 | 1 | 2 | 3 | 0 | 2 | 0 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| ID | 0 | 1 | 42461.50 | 24515.01 | 1 | 21231.25 | 42461.5 | 63691.75 | 84922 | ▇▇▇▇▇ |
| Age | 0 | 1 | 54.51 | 10.01 | 15 | 48.00 | 55.0 | 61.00 | 101 | ▁▅▇▂▁ |
| Tumor.Size..cm. | 0 | 1 | 1.75 | 2.03 | 0 | 0.00 | 0.0 | 3.48 | 6 | ▇▂▂▂▂ |
| Cancer.Stage | 0 | 1 | 1.12 | 1.34 | 0 | 0.00 | 0.0 | 2.00 | 4 | ▇▂▂▂▁ |
| Survival.Rate..5.Year…. | 0 | 1 | 79.50 | 26.48 | 10 | 65.23 | 100.0 | 100.00 | 100 | ▁▂▁▂▇ |
| Cost.of.Treatment..USD. | 0 | 1 | 39109.88 | 44710.69 | 0 | 0.00 | 0.0 | 76468.44 | 159988 | ▇▂▃▂▁ |
| Economic.Burden..Lost.Workdays.per.Year. | 0 | 1 | 52.03 | 60.39 | 0 | 0.00 | 0.0 | 104.00 | 179 | ▇▂▂▂▂ |
# Check class distribution of the target variable
table(cancer$Diagnosis) # Assuming "Diagnosis" is the target variable## < table of extent 0 >## numeric(0)#age range that gets affected more by oral cancer
ggplot(cancer, aes(x = Age)) +
geom_histogram(fill = "blue",binwidth =5, bins = 30, alpha = 0.7, color = 'black') +
theme_minimal() +
scale_x_continuous(breaks = seq(min(cancer$Age), max(cancer$Age), by = 5)) +
labs(title = "Age Distribution of Patients", x = "Age", y = "Count")
#rate at which oral cancer is caused by smoking tobacco
ggplot(cancer, aes(x = Tobacco.Use, fill = Oral.Cancer..Diagnosis.)) +
geom_bar(position = "dodge",) +
theme_minimal() +
labs(title = "Effect of Tobacco Use on Oral Cancer", x = "Tobacco Use", y = "Proportion")
#number of countrys affected by oraal cancer
ggplot(cancer, aes(x = Country, fill = Oral.Cancer..Diagnosis.)) +
geom_bar() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
labs(title = "Oral Cancer Cases by Country", x = "Country", y = "Count")
# #building a model for this dataset
# # Convert categorical variables to factors
# cancer$Oral.Cancer..Diagnosis. <- as.factor(cancer$Oral.Cancer..Diagnosis.)
# cancer <- cancer %>% mutate_if(is.character, as.factor)
#
# # Split data into training and test sets (80/20 split)
# set.seed(123)
# trainIndex <- createDataPartition(cancer$Oral.Cancer..Diagnosis., p = 0.8, list = FALSE)
# trainData <- cancer[trainIndex, ]
# testData <- cancer[-trainIndex, ]
# head(trainIndex)
#
# # Train model
# rf_model <- randomForest(Oral.Cancer..Diagnosis. ~ ., data = trainData, ntree = 100, mtry = 3, importance = TRUE)
#
# # Print model summary
# print(rf_model)
#
# # Feature importance
# varImpPlot(rf_model)
#
# # Predictions on test set
# rf_predictions <- predict(rf_model, testData)
#
# # Confusion Matrix
# confusionMatrix(rf_predictions, testData$Oral.Cancer..Diagnosis.)
#