Hotel Room Pricing In The Indian Market
Raj Kapoor
27 January 2018
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hotel.df<-read.csv(paste("Cities42.csv",sep=""))
View(hotel.df)
attach(hotel.df)#Removing the repeated date by gsub command
hotel.df$Date<-gsub("18-Dec-16", "Dec 18 2016", hotel.df$Date)
hotel.df$Date<-gsub("21-Dec-16", "Dec 21 2016", hotel.df$Date)
hotel.df$Date<-gsub("24-Dec-16", "Dec 24 2016", hotel.df$Date)
hotel.df$Date<-gsub("25-Dec-16", "Dec 25 2016", hotel.df$Date)
hotel.df$Date<-gsub("28-Dec-16", "Dec 28 2016", hotel.df$Date)
hotel.df$Date<-gsub("31-Dec-16", "Dec 31 2016", hotel.df$Date)
hotel.df$Date<-gsub("4-Jan-17", "Jan 04 2017", hotel.df$Date)
hotel.df$Date<-gsub("4-Jan-16", "Jan 04 2017", hotel.df$Date)
hotel.df$Date<-gsub("8-Jan-16", "Jan 08 2017", hotel.df$Date)
hotel.df$Date<-gsub("8-Jan-17", "Jan 08 2017", hotel.df$Date)
hotel.df$Date<-gsub("Jan 4 2017", "Jan 04 2017", hotel.df$Date)
hotel.df$Date<-gsub("Jan 8 2017", "Jan 08 2017", hotel.df$Date)#Checking the dates
table(hotel.df$Date)##
## Dec 18 2016 Dec 21 2016 Dec 24 2016 Dec 25 2016 Dec 28 2016 Dec 31 2016
## 1652 1655 1655 1655 1655 1655
## Jan 04 2017 Jan 08 2017
## 1652 1653#Changing dates to factors for labelling
hotel.df$Date<-factor(hotel.df$Date)
is.factor(hotel.df$Date)## [1] TRUE#Checking the labelling
levels(hotel.df$Date)## [1] "Dec 18 2016" "Dec 21 2016" "Dec 24 2016" "Dec 25 2016" "Dec 28 2016"
## [6] "Dec 31 2016" "Jan 04 2017" "Jan 08 2017"#Analyzing the summary of the data and describing the variables
library(psych)
describe(hotel.df)## vars n mean sd median trimmed
## X 1 13232 6616.50 3819.89 6616.5 6616.50
## CityName* 2 13232 18.07 11.72 16.0 17.29
## Population 3 13232 4416836.87 4258386.00 3046163.0 4040816.22
## CityRank 4 13232 14.83 13.51 9.0 13.30
## IsMetroCity 5 13232 0.28 0.45 0.0 0.23
## IsTouristDestination 6 13232 0.70 0.46 1.0 0.75
## IsWeekend 7 13232 0.62 0.48 1.0 0.65
## IsNewYearEve 8 13232 0.12 0.33 0.0 0.03
## Date* 9 13232 4.50 2.29 4.0 4.50
## HotelName* 10 13232 841.19 488.16 827.0 841.18
## RoomRent 11 13232 5473.99 7333.12 4000.0 4383.33
## StarRating 12 13232 3.46 0.76 3.0 3.40
## Airport 13 13232 21.16 22.76 15.0 16.39
## HotelAddress* 14 13232 1202.53 582.17 1261.0 1233.25
## HotelPincode 15 13232 397430.26 259837.50 395003.0 388540.47
## HotelDescription* 16 13224 581.34 363.26 567.0 575.37
## FreeWifi 17 13232 0.93 0.26 1.0 1.00
## FreeBreakfast 18 13232 0.65 0.48 1.0 0.69
## HotelCapacity 19 13232 62.51 76.66 34.0 46.03
## HasSwimmingPool 20 13232 0.36 0.48 0.0 0.32
## mad min max range skew
## X 4904.44 1.0 13232 13231.0 0.00
## CityName* 11.86 1.0 42 41.0 0.48
## Population 3846498.95 8096.0 12442373 12434277.0 0.68
## CityRank 11.86 0.0 44 44.0 0.69
## IsMetroCity 0.00 0.0 1 1.0 0.96
## IsTouristDestination 0.00 0.0 1 1.0 -0.86
## IsWeekend 0.00 0.0 1 1.0 -0.51
## IsNewYearEve 0.00 0.0 1 1.0 2.28
## Date* 2.97 1.0 8 7.0 0.00
## HotelName* 641.97 1.0 1670 1669.0 0.01
## RoomRent 2653.85 299.0 322500 322201.0 16.75
## StarRating 0.74 0.0 5 5.0 0.48
## Airport 11.12 0.2 124 123.8 2.73
## HotelAddress* 668.65 1.0 2108 2107.0 -0.37
## HotelPincode 257975.37 100025.0 7000157 6900132.0 9.99
## HotelDescription* 472.95 1.0 1226 1225.0 0.11
## FreeWifi 0.00 0.0 1 1.0 -3.25
## FreeBreakfast 0.00 0.0 1 1.0 -0.62
## HotelCapacity 28.17 0.0 600 600.0 2.95
## HasSwimmingPool 0.00 0.0 1 1.0 0.60
## kurtosis se
## X -1.20 33.21
## CityName* -0.88 0.10
## Population -1.08 37019.65
## CityRank -0.76 0.12
## IsMetroCity -1.08 0.00
## IsTouristDestination -1.26 0.00
## IsWeekend -1.74 0.00
## IsNewYearEve 3.18 0.00
## Date* -1.24 0.02
## HotelName* -1.25 4.24
## RoomRent 582.06 63.75
## StarRating 0.25 0.01
## Airport 7.89 0.20
## HotelAddress* -0.88 5.06
## HotelPincode 249.76 2258.86
## HotelDescription* -1.25 3.16
## FreeWifi 8.57 0.00
## FreeBreakfast -1.61 0.00
## HotelCapacity 11.39 0.67
## HasSwimmingPool -1.64 0.00summary(hotel.df)## X CityName Population CityRank
## Min. : 1 Delhi :2048 Min. : 8096 Min. : 0.00
## 1st Qu.: 3309 Jaipur : 768 1st Qu.: 744983 1st Qu.: 2.00
## Median : 6616 Mumbai : 712 Median : 3046163 Median : 9.00
## Mean : 6616 Bangalore: 656 Mean : 4416837 Mean :14.83
## 3rd Qu.: 9924 Goa : 624 3rd Qu.: 8443675 3rd Qu.:24.00
## Max. :13232 Kochi : 608 Max. :12442373 Max. :44.00
## (Other) :7816
## IsMetroCity IsTouristDestination IsWeekend IsNewYearEve
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000 Median :1.0000 Median :0.0000
## Mean :0.2842 Mean :0.6972 Mean :0.6228 Mean :0.1244
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
##
## Date HotelName RoomRent
## Dec 21 2016:1655 Vivanta by Taj : 32 Min. : 299
## Dec 24 2016:1655 Goldfinch Hotel : 24 1st Qu.: 2436
## Dec 25 2016:1655 OYO Rooms : 24 Median : 4000
## Dec 28 2016:1655 The Gordon House Hotel: 24 Mean : 5474
## Dec 31 2016:1655 Apnayt Villa : 16 3rd Qu.: 6299
## Jan 08 2017:1653 Bentleys Hotel Colaba : 16 Max. :322500
## (Other) :3304 (Other) :13096
## StarRating Airport
## Min. :0.000 Min. : 0.20
## 1st Qu.:3.000 1st Qu.: 8.40
## Median :3.000 Median : 15.00
## Mean :3.459 Mean : 21.16
## 3rd Qu.:4.000 3rd Qu.: 24.00
## Max. :5.000 Max. :124.00
##
## HotelAddress
## The Mall, Shimla : 32
## #2-91/14/8, White Fields, Kondapur, Hitech City, Hyderabad, 500084 India: 16
## 121, City Terrace, Walchand Hirachand Marg, Mumbai, Maharashtra : 16
## 14-4507/9, Balmatta Road, Near Jyothi Circle, Hampankatta : 16
## 144/7, Rajiv Gandi Salai (OMR), Kottivakkam, Chennai, Tamil Nadu : 16
## 17, Oliver Road, Colaba, Mumbai, Maharashtra : 16
## (Other) :13120
## HotelPincode HotelDescription FreeWifi FreeBreakfast
## Min. : 100025 3 : 120 Min. :0.0000 Min. :0.0000
## 1st Qu.: 221001 Abc : 112 1st Qu.:1.0000 1st Qu.:0.0000
## Median : 395003 3-star hotel: 104 Median :1.0000 Median :1.0000
## Mean : 397430 3.5 : 88 Mean :0.9259 Mean :0.6491
## 3rd Qu.: 570001 4 : 72 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :7000157 (Other) :12728 Max. :1.0000 Max. :1.0000
## NA's : 8
## HotelCapacity HasSwimmingPool
## Min. : 0.00 Min. :0.0000
## 1st Qu.: 16.00 1st Qu.:0.0000
## Median : 34.00 Median :0.0000
## Mean : 62.51 Mean :0.3558
## 3rd Qu.: 75.00 3rd Qu.:1.0000
## Max. :600.00 Max. :1.0000
## #Taking Y = RoomRent, identifying the most relevent predictor variables by correlation corrgram
#Corrgram
library(corrgram)
corrgram(hotel.df, order=TRUE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of Hotel data")
##through corrgram HasSwimming, StarRating, HotelCapital are very well correlated to RoomRent
##so we can take them as predictors##Visualizing data for Y as Room rent and X1,X2,X3 as HasSwimmingPool, StarRating and HotelCapacity respectively
#Table for HasSwimmingPool
table(hotel.df$HasSwimmingPool)##
## 0 1
## 8524 4708Swim<-table(hotel.df$HasSwimmingPool)
barplot(Swim,main="Barrplot of Hotel Swimming Pool")
#Table for StarRating
table(hotel.df$StarRating)##
## 0 1 2 2.5 3 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 4.1
## 16 8 440 632 5953 8 16 8 1752 8 24 16 32 2463 24
## 4.3 4.4 4.5 4.7 4.8 5
## 16 8 376 8 16 1408starRating<-table(hotel.df$StarRating)
barplot(starRating,main = "Barrplot for Star Rating")
#BoxPlot for HotelCapacity
boxplot(hotel.df$HotelCapacity, main="Boxplot for Hotel Capacity",horizontal = TRUE)
#Scatterplot pair wise for predictor variable
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logit#StarRating Vs RoomRent
scatterplot(hotel.df$StarRating,hotel.df$RoomRent,main="RoomRent of Hotels with StarRating",ylab = "RoomRent in INR", xlab="Star rating out of 5",cex=1.1)
#RoomRent Vs HotelCapacity
scatterplot(hotel.df$RoomRent,hotel.df$HotelCapacity,main="RoomRent of Hotels with Hotel capacity",ylab = "Hotel Capacity in rooms", xlab="RoomRent in INR",cex=1.1)
#RoomRent Vs HasSwimmingPool
plot(jitter(hotel.df$RoomRent),jitter(hotel.df$HasSwimmingPool),main="RoomRent of Hotels with HasSwimmingPool",ylab = "Has Swimmng Pool ", xlab="RoomRent",cex=1.1)
library(lattice)
bwplot(HasSwimmingPool~RoomRent, data = hotel.df,main="RoomRent of Hotels with HasSwimmingPool",ylab = "Has Swimmng Pool ", xlab="RoomRent" )
#Scatterplot matrix
scatterplotMatrix(
hotel.df[
,c("RoomRent","HasSwimmingPool","StarRating", "HotelCapacity")],
spread=FALSE, smoother.args=list(lty=2),
main="Scatter Plot Matrix", diagonal = "histogram")## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit smooth
#Corrgram of Y, x1, x2, x3
library(corrgram)
xyz<-data.frame(hotel.df$RoomRent, hotel.df$HasSwimmingPool, hotel.df$HotelCapacity, hotel.df$StarRating)
corrgram(xyz, order=TRUE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of Hotel Prices In India")
#Variance-Covariance Matrix for Y, x1, x2, x3
x<-hotel.df[,c("HasSwimmingPool","StarRating", "HotelCapacity")]
y<-hotel.df[,c("RoomRent")]
cor(x,y)## [,1]
## HasSwimmingPool 0.3116577
## StarRating 0.3693734
## HotelCapacity 0.1578733cov(x,y)## [,1]
## HasSwimmingPool 1094.202
## StarRating 2048.375
## HotelCapacity 88753.413var(x,y)## [,1]
## HasSwimmingPool 1094.202
## StarRating 2048.375
## HotelCapacity 88753.413#Forming a variable which is having RoomRent less than 1 lakh because the outliers effect the average
RoomRent1.df <-hotel.df[which(hotel.df$RoomRent<100000),]#Comparing other factors and their pattern using other trends with roomrent
#Analyzing IsWeekeng effect on RoomRent
table(hotel.df$IsWeekend)##
## 0 1
## 4991 8241table1<-table(hotel.df$IsWeekend)
barplot(table1, main="Distribution of Weekend", xlab="Not weekend(0)" )
#Effect of Isweekend on RoomRent
iw= aggregate(RoomRent ~ IsWeekend, data=hotel.df,mean)
iw## IsWeekend RoomRent
## 1 0 5430.835
## 2 1 5500.129boxplot(RoomRent~hotel.df$IsWeekend,data=hotel.df, main="Room rent vs. IsWeekend", ylab="Not weekend(0) weekend(1)", xlab="Room Rent in rupees ", col=c("red","blue"),horizontal=TRUE)
#Comapring RoomRent on different dates
table(hotel.df$Date)##
## Dec 18 2016 Dec 21 2016 Dec 24 2016 Dec 25 2016 Dec 28 2016 Dec 31 2016
## 1652 1655 1655 1655 1655 1655
## Jan 04 2017 Jan 08 2017
## 1652 1653library(lattice)
histogram(~Date, data = hotel.df, main="Distribution of Dates", xlab = "Differnt of Dates", col="Blue")
#Effect of different dates on RoomRent
d = aggregate(RoomRent ~ Date, data = hotel.df,mean)
d## Date RoomRent
## 1 Dec 18 2016 4896.402
## 2 Dec 21 2016 5085.315
## 3 Dec 24 2016 5543.236
## 4 Dec 25 2016 5464.143
## 5 Dec 28 2016 5593.924
## 6 Dec 31 2016 6191.776
## 7 Jan 04 2017 5674.062
## 8 Jan 08 2017 5342.234boxplot(RoomRent~Date,data=hotel.df, main="Room rent vs. Date", ylab="Different Dates", xlab="Room Rent in rupees ", col=c("red","blue","green","yellow"),horizontal=TRUE)
#Analyzing IsMetroCity effect on RoomRent
table(hotel.df$IsMetroCity)##
## 0 1
## 9472 3760table1<-table(hotel.df$IsMetroCity)
barplot(table1, main="Distribution of IsMetroCity", xlab="Not a Metro city(0) Is a Metro City(1)", col="blue")
#Effect of IsMetroCity on RoomRent
imc = aggregate(RoomRent ~ IsMetroCity, data = hotel.df, mean)
imc## IsMetroCity RoomRent
## 1 0 5782.794
## 2 1 4696.073boxplot(RoomRent~IsMetroCity,data=hotel.df, main="Room rent vs. IsMetroCity", ylab="Metro city(1) or not(0)", xlab="Room Rent in rupees ", col=c("red","blue","green","yellow"),horizontal=TRUE)
#Analyzing IsTouristDestination effect on RoomRent
table(hotel.df$IsTouristDestination)##
## 0 1
## 4007 9225table1<-table(hotel.df$IsTouristDestination)
barplot(table1, main="Distribution of IsToursitDestination", xlab="Not a Tourist Destination(0) Is a Tourist Destination(1)", col="yellow")
#Effect of IsTouristDestination on RoomRent
itd = aggregate(RoomRent ~ IsTouristDestination, data = hotel.df, mean)
itd## IsTouristDestination RoomRent
## 1 0 4111.003
## 2 1 6066.024boxplot(RoomRent~IsMetroCity,data=hotel.df, main="Room rent vs. IsMetroCity", ylab="Metro city(1) or not(0)", xlab="Room Rent in rupees ", col=c("red","blue","green","yellow"),horizontal=TRUE)
#Analyzing Airport distance from hotel effects in what way on RoomRent
summary(hotel.df$Airport)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 0.20 8.40 15.00 21.16 24.00 124.00boxplot(hotel.df$Airport, main="Boxplot of Airport",xlab= "Distance of airport from hotel(Km)" ,col="green",horizontal = TRUE)
#Effect of Airport distance on RoomRent
scatterplot(hotel.df$Airport,hotel.df$RoomRent, main="Room rent vs. Airport distance", xlab="Airport distance(km)", ylab="Room Rent in rupees ",cex=1.1)
#Analyzing FreeWifi Vs RoomRent
table(hotel.df$FreeWifi)##
## 0 1
## 981 12251fw<-table(hotel.df$FreeWifi)
barplot(fw, main="Borplot of FreeWifi",xlab= "FreeWifi" ,col="red")
#Effect of FreeWifi on RoomRent
fw = aggregate(RoomRent ~ FreeWifi, data = hotel.df, mean)
fw## FreeWifi RoomRent
## 1 0 5380.004
## 2 1 5481.518##With extreme outliers of roomrent
boxplot(RoomRent~FreeWifi,data=hotel.df, main="Room rent vs. FreeWifi", ylab="Free Wifi available(1)", xlab="Room Rent in rupees ", col=c("red","blue","green","yellow"),horizontal=TRUE)
##Without extreme outliers of roomrent
boxplot(RoomRent~FreeWifi,data=RoomRent1.df, main="Room rent vs. FreeWifi", ylab="Free Wifi available(1)", xlab="Room Rent in rupees ", col=c("red","blue","green","yellow"),horizontal=TRUE)
#Analyzing FreeBreakfast Vs RoomRent
table(hotel.df$FreeWifi)##
## 0 1
## 981 12251fw<-table(hotel.df$FreeBreakfast)
barplot(fw, main="Borplot of FreeBreakfast",xlab= "FreeWifi" ,col="red")
#Effect of FreeBreakfast on RoomRent
fb = aggregate(RoomRent ~ FreeBreakfast, data =hotel.df, mean)
fb1 = aggregate(RoomRent ~ FreeBreakfast, data =RoomRent1.df, mean)
fb## FreeBreakfast RoomRent
## 1 0 5573.790
## 2 1 5420.044fb1## FreeBreakfast RoomRent
## 1 0 5341.260
## 2 1 5420.044##With extreme outliers of roomrent
boxplot(RoomRent~FreeBreakfast,data=hotel.df, main="Room rent vs. FreeBreakfast", ylab="Free Breakfast available(1)", xlab="Room Rent in rupees ", col=c("green","yellow"),horizontal=TRUE)
##Without extreme outliers of roomrent
boxplot(RoomRent~FreeBreakfast,data=RoomRent1.df, main="Room rent vs. FreeBreakfast", ylab="Free Breakfast available(1)", xlab="Room Rent in rupees ", col=c("green","yellow"),horizontal=TRUE)
hotel.df<-read.csv(paste("Cities42.csv",sep=""))
View(hotel.df)
attach(hotel.df)## The following objects are masked from hotel.df (pos = 7):
##
## Airport, CityName, CityRank, Date, FreeBreakfast, FreeWifi,
## HasSwimmingPool, HotelAddress, HotelCapacity,
## HotelDescription, HotelName, HotelPincode, IsMetroCity,
## IsNewYearEve, IsTouristDestination, IsWeekend, Population,
## RoomRent, StarRating, X#sumary statistics
library(psych)
describe(hotel.df)## vars n mean sd median trimmed
## X 1 13232 6616.50 3819.89 6616.5 6616.50
## CityName* 2 13232 18.07 11.72 16.0 17.29
## Population 3 13232 4416836.87 4258386.00 3046163.0 4040816.22
## CityRank 4 13232 14.83 13.51 9.0 13.30
## IsMetroCity 5 13232 0.28 0.45 0.0 0.23
## IsTouristDestination 6 13232 0.70 0.46 1.0 0.75
## IsWeekend 7 13232 0.62 0.48 1.0 0.65
## IsNewYearEve 8 13232 0.12 0.33 0.0 0.03
## Date* 9 13232 14.26 2.82 14.0 14.39
## HotelName* 10 13232 841.19 488.16 827.0 841.18
## RoomRent 11 13232 5473.99 7333.12 4000.0 4383.33
## StarRating 12 13232 3.46 0.76 3.0 3.40
## Airport 13 13232 21.16 22.76 15.0 16.39
## HotelAddress* 14 13232 1202.53 582.17 1261.0 1233.25
## HotelPincode 15 13232 397430.26 259837.50 395003.0 388540.47
## HotelDescription* 16 13224 581.34 363.26 567.0 575.37
## FreeWifi 17 13232 0.93 0.26 1.0 1.00
## FreeBreakfast 18 13232 0.65 0.48 1.0 0.69
## HotelCapacity 19 13232 62.51 76.66 34.0 46.03
## HasSwimmingPool 20 13232 0.36 0.48 0.0 0.32
## mad min max range skew
## X 4904.44 1.0 13232 13231.0 0.00
## CityName* 11.86 1.0 42 41.0 0.48
## Population 3846498.95 8096.0 12442373 12434277.0 0.68
## CityRank 11.86 0.0 44 44.0 0.69
## IsMetroCity 0.00 0.0 1 1.0 0.96
## IsTouristDestination 0.00 0.0 1 1.0 -0.86
## IsWeekend 0.00 0.0 1 1.0 -0.51
## IsNewYearEve 0.00 0.0 1 1.0 2.28
## Date* 2.97 1.0 20 19.0 -1.05
## HotelName* 641.97 1.0 1670 1669.0 0.01
## RoomRent 2653.85 299.0 322500 322201.0 16.75
## StarRating 0.74 0.0 5 5.0 0.48
## Airport 11.12 0.2 124 123.8 2.73
## HotelAddress* 668.65 1.0 2108 2107.0 -0.37
## HotelPincode 257975.37 100025.0 7000157 6900132.0 9.99
## HotelDescription* 472.95 1.0 1226 1225.0 0.11
## FreeWifi 0.00 0.0 1 1.0 -3.25
## FreeBreakfast 0.00 0.0 1 1.0 -0.62
## HotelCapacity 28.17 0.0 600 600.0 2.95
## HasSwimmingPool 0.00 0.0 1 1.0 0.60
## kurtosis se
## X -1.20 33.21
## CityName* -0.88 0.10
## Population -1.08 37019.65
## CityRank -0.76 0.12
## IsMetroCity -1.08 0.00
## IsTouristDestination -1.26 0.00
## IsWeekend -1.74 0.00
## IsNewYearEve 3.18 0.00
## Date* 2.93 0.02
## HotelName* -1.25 4.24
## RoomRent 582.06 63.75
## StarRating 0.25 0.01
## Airport 7.89 0.20
## HotelAddress* -0.88 5.06
## HotelPincode 249.76 2258.86
## HotelDescription* -1.25 3.16
## FreeWifi 8.57 0.00
## FreeBreakfast -1.61 0.00
## HotelCapacity 11.39 0.67
## HasSwimmingPool -1.64 0.00#visualizing plots of y=f(x1,x2,x3,,,)
plot(~StarRating+RoomRent,main="Room rent vs Star Rating")
tablerent<-table(StarRating)
tablerent## StarRating
## 0 1 2 2.5 3 3.2 3.3 3.4 3.5 3.6 3.7 3.8 3.9 4 4.1
## 16 8 440 632 5953 8 16 8 1752 8 24 16 32 2463 24
## 4.3 4.4 4.5 4.7 4.8 5
## 16 8 376 8 16 1408library(lattice)
histogram(~StarRating,type="percent",data=hotel.df)
mytable3<-xtabs(~RoomRent+StarRating,data=hotel.df)
View(mytable3)
boxplot(RoomRent+HotelCapacity,data=hotel.df,xlab="rent",ylab="hotel capacity",main="Boxplot showing distribution of hotel capacity vs rent",horizontal=TRUE)
plot(~HotelCapacity+RoomRent,main="Room rent vs Hotel Capacity")
abline(0,1)
mytable<-table(IsTouristDestination)
View(mytable)
library(lattice)
histogram(~IsTouristDestination,type="percent",data=hotel.df)
plot(~IsTouristDestination+RoomRent,main="Room rent vs Is Tourist Destination")
library(Hmisc)## Loading required package: survival## Loading required package: Formula## Loading required package: ggplot2##
## Attaching package: 'ggplot2'## The following objects are masked from 'package:psych':
##
## %+%, alpha##
## Attaching package: 'Hmisc'## The following object is masked from 'package:psych':
##
## describe## The following objects are masked from 'package:base':
##
## format.pval, units#correlational Matrix of variables
corMatrix<-rcorr(as.matrix(hotel.df[,c('RoomRent','IsTouristDestination','HotelCapacity','StarRating')]))
corMatrix## RoomRent IsTouristDestination HotelCapacity
## RoomRent 1.00 0.12 0.16
## IsTouristDestination 0.12 1.00 -0.09
## HotelCapacity 0.16 -0.09 1.00
## StarRating 0.37 -0.04 0.64
## StarRating
## RoomRent 0.37
## IsTouristDestination -0.04
## HotelCapacity 0.64
## StarRating 1.00
##
## n= 13232
##
##
## P
## RoomRent IsTouristDestination HotelCapacity
## RoomRent 0 0
## IsTouristDestination 0 0
## HotelCapacity 0 0
## StarRating 0 0 0
## StarRating
## RoomRent 0
## IsTouristDestination 0
## HotelCapacity 0
## StarRatinglibrary(car)
library(corrgram)
corrgram(hotel.df[,c('RoomRent','IsTouristDestination','HotelCapacity','StarRating')],lower.panel=panel.pts, upper.panel=panel.pie,
diag.panel=panel.minmax, text.panel=panel.txt)
scatterplotMatrix(~RoomRent++IsMetroCity+StarRating+HotelCapacity,data=hotel.df)
#splitting it into training set and test set
library(caTools)
set.seed(123)
split = sample.split(RoomRent, SplitRatio = 0.8)
training_set = subset(hotel.df, split == TRUE)
test_set = subset(hotel.df, split == FALSE)
View(training_set)
View(test_set)
#creating a regressor of statistically significant parameters
fit1<-lm(formula=RoomRent~IsTouristDestination+HotelCapacity+StarRating, data =training_set)
summary(fit1)##
## Call:
## lm(formula = RoomRent ~ IsTouristDestination + HotelCapacity +
## StarRating, data = training_set)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11023 -2483 -867 1003 309647
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -10728.762 380.686 -28.18 <2e-16 ***
## IsTouristDestination 2110.171 148.595 14.20 <2e-16 ***
## HotelCapacity -11.503 1.139 -10.10 <2e-16 ***
## StarRating 4476.120 116.171 38.53 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7045 on 10767 degrees of freedom
## Multiple R-squared: 0.1578, Adjusted R-squared: 0.1576
## F-statistic: 672.5 on 3 and 10767 DF, p-value: < 2.2e-16library(car)
plot(HasSwimmingPool,RoomRent)
aggregate(cbind(IsTouristDestination,HasSwimmingPool,IsWeekend,IsNewYearEve,FreeWifi,FreeBreakfast) ~RoomRent,
data =hotel.df, mean)## RoomRent IsTouristDestination HasSwimmingPool IsWeekend IsNewYearEve
## 1 299 1.00000000 0.00000000 0.5000000 0.00000000
## 2 324 1.00000000 0.00000000 1.0000000 0.00000000
## 3 349 1.00000000 0.00000000 0.6666667 0.16666667
## 4 491 1.00000000 0.00000000 1.0000000 0.00000000
## 5 499 1.00000000 0.00000000 1.0000000 0.00000000
## 6 500 1.00000000 0.00000000 0.5000000 0.00000000
## 7 504 1.00000000 0.00000000 0.3333333 0.00000000
## 8 540 1.00000000 0.00000000 0.4000000 0.20000000
## 9 560 1.00000000 0.00000000 0.7500000 0.25000000
## 10 605 1.00000000 0.00000000 0.5000000 0.00000000
## 11 613 1.00000000 0.00000000 0.5000000 0.00000000
## 12 623 1.00000000 0.00000000 1.0000000 0.00000000
## 13 642 1.00000000 0.00000000 0.6666667 0.00000000
## 14 650 1.00000000 0.00000000 1.0000000 1.00000000
## 15 655 1.00000000 0.00000000 0.3333333 0.00000000
## 16 679 0.00000000 1.00000000 0.6666667 0.16666667
## 17 698 1.00000000 0.00000000 0.5000000 0.50000000
## 18 727 1.00000000 0.00000000 0.5000000 0.00000000
## 19 766 0.00000000 0.00000000 0.6666667 0.16666667
## 20 780 1.00000000 0.00000000 0.7500000 0.25000000
## 21 796 1.00000000 0.00000000 1.0000000 0.00000000
## 22 798 1.00000000 0.00000000 0.5714286 0.14285714
## 23 799 1.00000000 0.00000000 0.6666667 0.00000000
## 24 801 1.00000000 0.00000000 0.5000000 0.00000000
## 25 824 1.00000000 0.00000000 0.6666667 0.00000000
## 26 834 1.00000000 0.00000000 0.5000000 0.00000000
## 27 844 1.00000000 0.00000000 0.6250000 0.12500000
## 28 850 1.00000000 0.00000000 0.8181818 0.18181818
## 29 851 1.00000000 0.00000000 0.6666667 0.00000000
## 30 852 0.00000000 0.00000000 0.5000000 0.50000000
## 31 874 1.00000000 0.00000000 0.5000000 0.00000000
## 32 877 1.00000000 0.00000000 0.5000000 0.50000000
## 33 880 1.00000000 0.00000000 1.0000000 0.00000000
## 34 896 1.00000000 0.00000000 1.0000000 0.00000000
## 35 897 0.00000000 0.00000000 0.5000000 0.00000000
## 36 909 1.00000000 0.00000000 0.7500000 0.25000000
## 37 915 0.00000000 0.00000000 0.6000000 0.20000000
## 38 921 1.00000000 0.00000000 0.6666667 0.00000000
## 39 922 0.00000000 0.00000000 0.5000000 0.00000000
## 40 931 1.00000000 0.00000000 1.0000000 0.00000000
## 41 937 1.00000000 0.00000000 0.5714286 0.14285714
## 42 950 1.00000000 0.00000000 0.5000000 0.00000000
## 43 953 1.00000000 0.00000000 0.2500000 0.00000000
## 44 968 0.00000000 0.00000000 0.6000000 0.00000000
## 45 971 1.00000000 0.00000000 0.7500000 0.25000000
## 46 984 1.00000000 0.00000000 1.0000000 0.00000000
## 47 990 1.00000000 0.00000000 1.0000000 0.00000000
## 48 995 1.00000000 0.00000000 0.5714286 0.14285714
## 49 999 1.00000000 0.00000000 0.6363636 0.09090909
## 50 1000 1.00000000 0.44444444 0.5555556 0.00000000
## 51 1001 1.00000000 0.00000000 1.0000000 0.00000000
## 52 1013 1.00000000 0.00000000 0.6000000 0.10000000
## 53 1021 0.25000000 0.00000000 0.5000000 0.00000000
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## 55 1042 1.00000000 0.00000000 0.6666667 0.00000000
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## 59 1061 0.00000000 0.00000000 0.5714286 0.14285714
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## 62 1067 1.00000000 0.00000000 0.6666667 0.00000000
## 63 1073 1.00000000 0.00000000 1.0000000 0.00000000
## 64 1080 1.00000000 0.77777778 0.5555556 0.11111111
## 65 1083 1.00000000 0.00000000 0.5000000 0.00000000
## 66 1087 0.00000000 0.00000000 0.5714286 0.00000000
## 67 1100 1.00000000 0.00000000 0.6666667 0.16666667
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## 69 1106 1.00000000 0.00000000 0.6666667 0.16666667
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## 73 1130 1.00000000 0.00000000 1.0000000 0.00000000
## 74 1145 1.00000000 0.00000000 1.0000000 0.00000000
## 75 1150 0.83333333 0.00000000 0.6666667 0.16666667
## 76 1158 1.00000000 0.00000000 0.6000000 0.20000000
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## 129 1359 0.66666667 0.00000000 0.6666667 0.11111111
## 130 1360 0.00000000 0.00000000 1.0000000 0.00000000
## 131 1361 1.00000000 0.00000000 0.5714286 0.14285714
## 132 1362 0.34375000 0.00000000 0.6250000 0.06250000
## 133 1376 1.00000000 0.00000000 0.5000000 0.00000000
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## 142 1404 0.07692308 0.00000000 0.6153846 0.07692308
## 143 1410 0.66666667 0.00000000 0.6666667 0.11111111
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## 210 1601 1.00000000 0.00000000 0.5555556 0.11111111
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## 1930 11700 1.00000000 1.00000000 1.0000000 0.00000000
## 1931 11772 1.00000000 0.00000000 0.6000000 0.20000000
## 1932 11809 1.00000000 0.00000000 0.6666667 0.33333333
## 1933 11813 1.00000000 1.00000000 1.0000000 1.00000000
## 1934 11850 0.66666667 0.33333333 0.6666667 0.33333333
## 1935 11899 1.00000000 1.00000000 1.0000000 1.00000000
## 1936 11900 1.00000000 0.00000000 1.0000000 0.00000000
## 1937 11921 1.00000000 0.00000000 0.5714286 0.14285714
## 1938 11999 1.00000000 0.40000000 0.6000000 0.26666667
## 1939 12000 0.78571429 0.66071429 0.5714286 0.16071429
## 1940 12027 1.00000000 0.00000000 0.0000000 0.00000000
## 1941 12089 1.00000000 1.00000000 0.0000000 0.00000000
## 1942 12091 1.00000000 0.00000000 0.6666667 0.33333333
## 1943 12134 1.00000000 0.66666667 0.3333333 0.00000000
## 1944 12150 0.88888889 0.33333333 0.5555556 0.11111111
## 1945 12185 1.00000000 0.00000000 1.0000000 0.00000000
## 1946 12199 1.00000000 1.00000000 1.0000000 1.00000000
## 1947 12211 1.00000000 0.00000000 0.7500000 0.25000000
## 1948 12225 1.00000000 1.00000000 1.0000000 0.00000000
## 1949 12252 1.00000000 1.00000000 1.0000000 0.00000000
## 1950 12285 1.00000000 1.00000000 0.5000000 0.00000000
## 1951 12326 1.00000000 1.00000000 0.6666667 0.00000000
## 1952 12350 1.00000000 1.00000000 1.0000000 0.00000000
## 1953 12375 1.00000000 1.00000000 1.0000000 0.00000000
## 1954 12420 0.00000000 0.00000000 0.0000000 0.00000000
## 1955 12442 1.00000000 1.00000000 1.0000000 0.00000000
## 1956 12500 0.88461538 0.88461538 0.6153846 0.15384615
## 1957 12549 1.00000000 1.00000000 0.7500000 0.25000000
## 1958 12554 1.00000000 1.00000000 0.0000000 0.00000000
## 1959 12562 1.00000000 1.00000000 0.0000000 0.00000000
## 1960 12599 1.00000000 0.00000000 1.0000000 1.00000000
## 1961 12600 0.33333333 1.00000000 0.5000000 0.00000000
## 1962 12666 1.00000000 0.00000000 0.7500000 0.25000000
## 1963 12700 1.00000000 1.00000000 0.7500000 0.25000000
## 1964 12750 0.00000000 1.00000000 1.0000000 0.00000000
## 1965 12760 1.00000000 1.00000000 0.0000000 0.00000000
## 1966 12773 1.00000000 1.00000000 0.5000000 0.00000000
## 1967 12864 1.00000000 1.00000000 0.5000000 0.50000000
## 1968 12879 1.00000000 1.00000000 1.0000000 0.00000000
## 1969 12880 1.00000000 1.00000000 0.0000000 0.00000000
## 1970 12882 1.00000000 0.00000000 0.0000000 0.00000000
## 1971 12950 1.00000000 1.00000000 0.0000000 0.00000000
## 1972 13000 0.71428571 1.00000000 0.3571429 0.07142857
## 1973 13242 1.00000000 1.00000000 1.0000000 0.00000000
## 1974 13250 0.50000000 1.00000000 0.5000000 0.50000000
## 1975 13384 1.00000000 0.00000000 0.6666667 0.33333333
## 1976 13400 1.00000000 1.00000000 1.0000000 1.00000000
## 1977 13484 1.00000000 1.00000000 0.5000000 0.00000000
## 1978 13499 1.00000000 1.00000000 1.0000000 0.00000000
## 1979 13500 0.86956522 0.56521739 0.7391304 0.08695652
## 1980 13505 1.00000000 1.00000000 0.5000000 0.50000000
## 1981 13518 1.00000000 0.00000000 0.6666667 0.16666667
## 1982 13533 1.00000000 1.00000000 1.0000000 1.00000000
## 1983 13590 1.00000000 1.00000000 1.0000000 0.00000000
## 1984 13600 1.00000000 0.81250000 0.6875000 0.12500000
## 1985 13613 1.00000000 1.00000000 0.3333333 0.00000000
## 1986 13624 1.00000000 0.00000000 0.5000000 0.00000000
## 1987 13725 1.00000000 1.00000000 1.0000000 0.00000000
## 1988 13800 1.00000000 1.00000000 1.0000000 0.00000000
## 1989 13949 1.00000000 1.00000000 0.6000000 0.20000000
## 1990 13950 1.00000000 1.00000000 1.0000000 0.00000000
## 1991 13999 1.00000000 1.00000000 0.5000000 0.00000000
## 1992 14000 1.00000000 0.90909091 0.5454545 0.27272727
## 1993 14030 1.00000000 1.00000000 1.0000000 0.00000000
## 1994 14357 1.00000000 1.00000000 0.0000000 0.00000000
## 1995 14400 1.00000000 1.00000000 0.0000000 0.00000000
## 1996 14450 1.00000000 1.00000000 0.5000000 0.00000000
## 1997 14476 1.00000000 0.00000000 1.0000000 0.33333333
## 1998 14499 1.00000000 0.00000000 1.0000000 0.00000000
## 1999 14500 0.94117647 1.00000000 0.5294118 0.05882353
## 2000 14725 1.00000000 1.00000000 1.0000000 0.00000000
## 2001 14732 1.00000000 1.00000000 1.0000000 0.00000000
## 2002 14850 1.00000000 1.00000000 0.6250000 0.12500000
## 2003 14875 1.00000000 1.00000000 1.0000000 0.00000000
## 2004 14999 1.00000000 0.50000000 0.5000000 0.50000000
## 2005 15000 1.00000000 1.00000000 0.6190476 0.19047619
## 2006 15010 1.00000000 1.00000000 0.6666667 0.00000000
## 2007 15073 1.00000000 1.00000000 0.7500000 0.25000000
## 2008 15115 1.00000000 0.00000000 0.5000000 0.00000000
## 2009 15138 1.00000000 1.00000000 0.8000000 0.20000000
## 2010 15188 1.00000000 1.00000000 1.0000000 0.00000000
## 2011 15210 1.00000000 0.00000000 0.5714286 0.14285714
## 2012 15300 1.00000000 1.00000000 0.6000000 0.00000000
## 2013 15400 1.00000000 0.00000000 0.0000000 0.00000000
## 2014 15450 1.00000000 1.00000000 1.0000000 0.00000000
## 2015 15464 1.00000000 1.00000000 0.0000000 0.00000000
## 2016 15500 1.00000000 0.38888889 0.6111111 0.22222222
## 2017 15525 1.00000000 1.00000000 0.0000000 0.00000000
## 2018 15750 1.00000000 0.80000000 0.8000000 0.20000000
## 2019 15800 1.00000000 1.00000000 1.0000000 0.00000000
## 2020 15883 1.00000000 1.00000000 1.0000000 1.00000000
## 2021 15998 1.00000000 1.00000000 0.0000000 0.00000000
## 2022 15999 1.00000000 1.00000000 0.0000000 0.00000000
## 2023 16000 0.92857143 1.00000000 0.7142857 0.21428571
## 2024 16261 1.00000000 1.00000000 0.5000000 0.25000000
## 2025 16300 1.00000000 1.00000000 0.5000000 0.00000000
## 2026 16499 1.00000000 1.00000000 0.5000000 0.50000000
## 2027 16500 1.00000000 1.00000000 0.6000000 0.20000000
## 2028 16750 1.00000000 1.00000000 1.0000000 0.33333333
## 2029 16892 1.00000000 1.00000000 1.0000000 0.33333333
## 2030 16954 1.00000000 1.00000000 0.5000000 0.00000000
## 2031 16999 1.00000000 1.00000000 1.0000000 0.00000000
## 2032 17000 1.00000000 0.33333333 0.8333333 0.50000000
## 2033 17099 0.00000000 1.00000000 0.0000000 0.00000000
## 2034 17200 1.00000000 1.00000000 0.3333333 0.00000000
## 2035 17250 1.00000000 1.00000000 1.0000000 0.00000000
## 2036 17280 1.00000000 1.00000000 1.0000000 0.50000000
## 2037 17415 1.00000000 1.00000000 0.0000000 0.00000000
## 2038 17499 1.00000000 1.00000000 1.0000000 0.00000000
## 2039 17500 1.00000000 0.20000000 0.8000000 0.20000000
## 2040 17523 1.00000000 1.00000000 1.0000000 1.00000000
## 2041 17999 1.00000000 0.00000000 0.6000000 0.20000000
## 2042 18000 1.00000000 0.82352941 0.5882353 0.05882353
## 2043 18220 1.00000000 1.00000000 0.5000000 0.50000000
## 2044 18319 1.00000000 1.00000000 1.0000000 0.00000000
## 2045 18450 1.00000000 0.00000000 1.0000000 1.00000000
## 2046 18499 1.00000000 1.00000000 0.6666667 0.33333333
## 2047 18500 0.00000000 1.00000000 1.0000000 1.00000000
## 2048 18525 1.00000000 1.00000000 0.0000000 0.00000000
## 2049 18700 1.00000000 1.00000000 0.6666667 0.33333333
## 2050 18800 1.00000000 1.00000000 1.0000000 1.00000000
## 2051 18847 1.00000000 1.00000000 1.0000000 0.00000000
## 2052 18899 1.00000000 1.00000000 1.0000000 0.50000000
## 2053 18900 1.00000000 0.00000000 1.0000000 0.00000000
## 2054 18947 0.00000000 0.00000000 0.3333333 0.00000000
## 2055 19000 1.00000000 0.66666667 0.6666667 0.11111111
## 2056 19200 1.00000000 1.00000000 0.5000000 0.00000000
## 2057 19350 1.00000000 1.00000000 0.7142857 0.14285714
## 2058 19450 1.00000000 1.00000000 0.0000000 0.00000000
## 2059 19500 1.00000000 1.00000000 1.0000000 0.00000000
## 2060 19759 1.00000000 1.00000000 1.0000000 0.00000000
## 2061 19950 1.00000000 1.00000000 0.5000000 0.25000000
## 2062 19979 1.00000000 1.00000000 0.6000000 0.20000000
## 2063 19999 1.00000000 0.77777778 0.6666667 0.44444444
## 2064 20000 1.00000000 1.00000000 0.6521739 0.17391304
## 2065 20084 1.00000000 1.00000000 0.5714286 0.14285714
## 2066 20224 0.00000000 0.00000000 0.7500000 0.00000000
## 2067 20250 1.00000000 1.00000000 1.0000000 0.00000000
## 2068 20425 1.00000000 1.00000000 0.0000000 0.00000000
## 2069 20444 1.00000000 1.00000000 0.0000000 0.00000000
## 2070 20500 1.00000000 1.00000000 1.0000000 0.50000000
## 2071 20700 1.00000000 1.00000000 1.0000000 0.50000000
## 2072 21000 1.00000000 1.00000000 0.6363636 0.18181818
## 2073 21060 1.00000000 1.00000000 1.0000000 0.00000000
## 2074 21201 1.00000000 1.00000000 1.0000000 0.50000000
## 2075 21499 1.00000000 1.00000000 1.0000000 1.00000000
## 2076 21500 1.00000000 1.00000000 0.6000000 0.20000000
## 2077 21907 1.00000000 1.00000000 0.5000000 0.00000000
## 2078 21999 1.00000000 0.00000000 1.0000000 0.00000000
## 2079 22000 1.00000000 1.00000000 0.7777778 0.33333333
## 2080 22072 1.00000000 1.00000000 0.0000000 0.00000000
## 2081 22325 1.00000000 1.00000000 1.0000000 0.00000000
## 2082 22500 1.00000000 1.00000000 0.8000000 0.20000000
## 2083 22650 1.00000000 0.00000000 0.0000000 0.00000000
## 2084 22689 1.00000000 1.00000000 1.0000000 0.00000000
## 2085 22744 1.00000000 1.00000000 0.5000000 0.50000000
## 2086 22998 1.00000000 1.00000000 1.0000000 1.00000000
## 2087 23000 1.00000000 1.00000000 0.6666667 0.00000000
## 2088 23100 1.00000000 1.00000000 1.0000000 0.00000000
## 2089 23352 1.00000000 1.00000000 0.7500000 0.25000000
## 2090 23400 1.00000000 1.00000000 0.5714286 0.14285714
## 2091 23418 0.00000000 0.00000000 0.5000000 0.00000000
## 2092 24000 1.00000000 1.00000000 0.8750000 0.25000000
## 2093 24205 1.00000000 1.00000000 1.0000000 0.00000000
## 2094 24378 1.00000000 1.00000000 1.0000000 1.00000000
## 2095 24437 1.00000000 1.00000000 0.6000000 0.20000000
## 2096 24500 1.00000000 1.00000000 0.0000000 0.00000000
## 2097 24999 1.00000000 0.00000000 0.5000000 0.25000000
## 2098 25000 1.00000000 1.00000000 0.8181818 0.45454545
## 2099 25200 1.00000000 1.00000000 0.7500000 0.25000000
## 2100 25502 1.00000000 1.00000000 0.0000000 0.00000000
## 2101 26000 1.00000000 1.00000000 1.0000000 0.50000000
## 2102 27000 1.00000000 0.00000000 1.0000000 0.33333333
## 2103 27500 1.00000000 1.00000000 1.0000000 1.00000000
## 2104 28000 1.00000000 1.00000000 1.0000000 1.00000000
## 2105 28235 1.00000000 1.00000000 0.0000000 0.00000000
## 2106 30000 0.60000000 1.00000000 0.8000000 0.20000000
## 2107 30152 1.00000000 1.00000000 0.6666667 0.33333333
## 2108 31200 1.00000000 1.00000000 1.0000000 0.00000000
## 2109 31712 1.00000000 1.00000000 0.6000000 0.00000000
## 2110 32000 1.00000000 1.00000000 0.6000000 0.00000000
## 2111 32500 1.00000000 1.00000000 0.6666667 0.16666667
## 2112 32769 1.00000000 1.00000000 0.5000000 0.00000000
## 2113 33200 1.00000000 1.00000000 0.6666667 0.33333333
## 2114 33500 0.50000000 1.00000000 1.0000000 0.25000000
## 2115 33900 1.00000000 0.00000000 0.0000000 0.00000000
## 2116 34000 1.00000000 1.00000000 0.4000000 0.20000000
## 2117 34500 1.00000000 1.00000000 1.0000000 0.50000000
## 2118 35236 1.00000000 1.00000000 0.6666667 0.33333333
## 2119 35460 1.00000000 1.00000000 1.0000000 0.00000000
## 2120 35530 1.00000000 1.00000000 1.0000000 0.00000000
## 2121 37500 1.00000000 1.00000000 0.0000000 0.00000000
## 2122 37920 1.00000000 1.00000000 1.0000000 0.00000000
## 2123 38000 1.00000000 1.00000000 1.0000000 1.00000000
## 2124 38500 1.00000000 1.00000000 1.0000000 0.00000000
## 2125 39500 1.00000000 1.00000000 0.6666667 0.33333333
## 2126 39805 1.00000000 1.00000000 0.0000000 0.00000000
## 2127 40000 0.00000000 1.00000000 1.0000000 0.33333333
## 2128 41000 1.00000000 1.00000000 1.0000000 1.00000000
## 2129 41850 1.00000000 1.00000000 1.0000000 0.00000000
## 2130 41999 1.00000000 1.00000000 0.0000000 0.00000000
## 2131 43500 1.00000000 1.00000000 1.0000000 1.00000000
## 2132 44000 1.00000000 1.00000000 1.0000000 0.00000000
## 2133 45000 1.00000000 1.00000000 0.5000000 0.50000000
## 2134 45300 1.00000000 1.00000000 0.3333333 0.33333333
## 2135 46000 1.00000000 1.00000000 0.6666667 0.00000000
## 2136 46350 1.00000000 1.00000000 1.0000000 0.00000000
## 2137 46500 1.00000000 1.00000000 0.0000000 0.00000000
## 2138 48000 1.00000000 1.00000000 0.5000000 0.00000000
## 2139 48500 1.00000000 1.00000000 0.5714286 0.14285714
## 2140 50000 1.00000000 1.00000000 0.0000000 0.00000000
## 2141 50280 1.00000000 1.00000000 0.6000000 0.20000000
## 2142 51000 1.00000000 1.00000000 1.0000000 0.00000000
## 2143 51765 1.00000000 1.00000000 0.6000000 0.40000000
## 2144 52000 1.00000000 1.00000000 0.6666667 0.33333333
## 2145 53000 1.00000000 1.00000000 0.3333333 0.33333333
## 2146 53920 1.00000000 1.00000000 1.0000000 0.00000000
## 2147 55000 1.00000000 1.00000000 0.5714286 0.28571429
## 2148 56775 0.00000000 1.00000000 1.0000000 0.00000000
## 2149 67000 1.00000000 1.00000000 0.6000000 0.20000000
## 2150 67400 1.00000000 1.00000000 0.0000000 0.00000000
## 2151 68500 1.00000000 1.00000000 0.5000000 0.25000000
## 2152 71900 1.00000000 1.00000000 0.0000000 0.00000000
## 2153 75000 1.00000000 1.00000000 0.6666667 0.00000000
## 2154 137500 1.00000000 1.00000000 1.0000000 0.00000000
## 2155 161920 1.00000000 1.00000000 0.5000000 0.50000000
## 2156 322500 1.00000000 1.00000000 0.5000000 0.00000000
## FreeWifi FreeBreakfast
## 1 1.0000000 0.00000000
## 2 1.0000000 0.00000000
## 3 1.0000000 0.00000000
## 4 1.0000000 0.00000000
## 5 1.0000000 0.00000000
## 6 1.0000000 0.00000000
## 7 1.0000000 0.00000000
## 8 1.0000000 1.00000000
## 9 1.0000000 0.00000000
## 10 1.0000000 1.00000000
## 11 1.0000000 1.00000000
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## 15 1.0000000 0.00000000
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## 20 1.0000000 1.00000000
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## 27 1.0000000 0.75000000
## 28 1.0000000 0.00000000
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## 51 1.0000000 0.00000000
## 52 1.0000000 0.20000000
## 53 1.0000000 1.00000000
## 54 1.0000000 0.00000000
## 55 1.0000000 0.00000000
## 56 1.0000000 1.00000000
## 57 1.0000000 0.00000000
## 58 1.0000000 0.50000000
## 59 1.0000000 1.00000000
## 60 1.0000000 1.00000000
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## 62 1.0000000 1.00000000
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## 64 1.0000000 0.88888889
## 65 1.0000000 1.00000000
## 66 1.0000000 0.00000000
## 67 1.0000000 0.66666667
## 68 1.0000000 0.00000000
## 69 1.0000000 1.00000000
## 70 1.0000000 1.00000000
## 71 1.0000000 1.00000000
## 72 1.0000000 0.00000000
## 73 1.0000000 1.00000000
## 74 1.0000000 0.00000000
## 75 1.0000000 1.00000000
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## 78 1.0000000 0.00000000
## 79 1.0000000 0.00000000
## 80 1.0000000 1.00000000
## 81 1.0000000 0.00000000
## 82 1.0000000 1.00000000
## 83 1.0000000 1.00000000
## 84 0.6666667 0.66666667
## 85 1.0000000 0.04347826
## 86 1.0000000 0.00000000
## 87 1.0000000 0.00000000
## 88 1.0000000 0.00000000
## 89 1.0000000 0.50000000
## 90 1.0000000 0.00000000
## 91 0.3333333 1.00000000
## 92 1.0000000 1.00000000
## 93 1.0000000 1.00000000
## 94 1.0000000 1.00000000
## 95 1.0000000 1.00000000
## 96 1.0000000 0.50000000
## 97 1.0000000 0.00000000
## 98 1.0000000 1.00000000
## 99 1.0000000 0.00000000
## 100 1.0000000 1.00000000
## 101 1.0000000 1.00000000
## 102 1.0000000 0.00000000
## 103 0.2500000 0.25000000
## 104 1.0000000 1.00000000
## 105 1.0000000 1.00000000
## 106 1.0000000 0.00000000
## 107 1.0000000 1.00000000
## 108 1.0000000 0.00000000
## 109 1.0000000 1.00000000
## 110 1.0000000 1.00000000
## 111 1.0000000 1.00000000
## 112 0.7777778 0.33333333
## 113 1.0000000 0.00000000
## 114 1.0000000 0.00000000
## 115 1.0000000 1.00000000
## 116 1.0000000 0.00000000
## 117 1.0000000 0.00000000
## 118 1.0000000 0.46153846
## 119 1.0000000 1.00000000
## 120 1.0000000 1.00000000
## 121 1.0000000 0.54545455
## 122 1.0000000 1.00000000
## 123 1.0000000 1.00000000
## 124 1.0000000 1.00000000
## 125 1.0000000 1.00000000
## 126 0.0000000 0.00000000
## 127 1.0000000 0.00000000
## 128 1.0000000 1.00000000
## 129 1.0000000 0.00000000
## 130 1.0000000 1.00000000
## 131 1.0000000 1.00000000
## 132 1.0000000 0.78125000
## 133 1.0000000 0.50000000
## 134 1.0000000 1.00000000
## 135 1.0000000 0.00000000
## 136 1.0000000 1.00000000
## 137 1.0000000 0.50000000
## 138 1.0000000 1.00000000
## 139 1.0000000 0.00000000
## 140 1.0000000 0.00000000
## 141 1.0000000 0.00000000
## 142 1.0000000 1.00000000
## 143 1.0000000 0.00000000
## 144 1.0000000 0.00000000
## 145 1.0000000 1.00000000
## 146 0.0000000 1.00000000
## 147 1.0000000 0.50000000
## 148 1.0000000 0.00000000
## 149 1.0000000 1.00000000
## 150 1.0000000 1.00000000
## 151 1.0000000 1.00000000
## 152 1.0000000 1.00000000
## 153 1.0000000 1.00000000
## 154 1.0000000 0.00000000
## 155 1.0000000 0.92307692
## 156 1.0000000 0.00000000
## 157 1.0000000 0.00000000
## 158 1.0000000 0.00000000
## 159 1.0000000 1.00000000
## 160 1.0000000 0.00000000
## 161 0.0000000 1.00000000
## 162 1.0000000 1.00000000
## 163 1.0000000 1.00000000
## 164 1.0000000 1.00000000
## 165 1.0000000 0.00000000
## 166 1.0000000 0.00000000
## 167 1.0000000 0.00000000
## 168 1.0000000 1.00000000
## 169 1.0000000 0.82142857
## 170 1.0000000 0.40000000
## 171 1.0000000 0.00000000
## 172 1.0000000 1.00000000
## 173 1.0000000 1.00000000
## 174 1.0000000 1.00000000
## 175 1.0000000 0.00000000
## 176 1.0000000 0.34375000
## 177 1.0000000 0.57142857
## 178 1.0000000 0.00000000
## 179 1.0000000 0.00000000
## 180 1.0000000 1.00000000
## 181 1.0000000 0.00000000
## 182 1.0000000 1.00000000
## 183 1.0000000 0.50000000
## 184 1.0000000 0.00000000
## 185 1.0000000 0.00000000
## 186 1.0000000 1.00000000
## 187 1.0000000 1.00000000
## 188 1.0000000 1.00000000
## 189 1.0000000 1.00000000
## 190 1.0000000 1.00000000
## 191 1.0000000 1.00000000
## 192 1.0000000 1.00000000
## 193 1.0000000 0.66666667
## 194 1.0000000 1.00000000
## 195 1.0000000 0.00000000
## 196 1.0000000 0.00000000
## 197 1.0000000 0.00000000
## 198 1.0000000 1.00000000
## 199 1.0000000 0.75000000
## 200 1.0000000 1.00000000
## 201 0.8333333 0.40000000
## 202 1.0000000 1.00000000
## 203 1.0000000 1.00000000
## 204 1.0000000 1.00000000
## 205 1.0000000 0.00000000
## 206 1.0000000 0.00000000
## 207 1.0000000 1.00000000
## 208 1.0000000 0.66666667
## 209 1.0000000 0.25000000
## 210 1.0000000 0.55555556
## 211 1.0000000 0.00000000
## 212 1.0000000 1.00000000
## 213 1.0000000 0.33333333
## 214 1.0000000 0.00000000
## 215 1.0000000 1.00000000
## 216 1.0000000 0.96969697
## 217 1.0000000 0.50000000
## 218 1.0000000 1.00000000
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## 229 1.0000000 0.33333333
## 230 0.8181818 0.00000000
## 231 1.0000000 0.00000000
## 232 1.0000000 1.00000000
## 233 1.0000000 1.00000000
## 234 1.0000000 1.00000000
## 235 1.0000000 1.00000000
## 236 1.0000000 0.77500000
## 237 1.0000000 1.00000000
## 238 1.0000000 0.00000000
## 239 1.0000000 1.00000000
## 240 1.0000000 0.00000000
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## 244 1.0000000 1.00000000
## 245 1.0000000 1.00000000
## 246 1.0000000 1.00000000
## 247 1.0000000 1.00000000
## 248 1.0000000 0.77777778
## 249 1.0000000 0.03030303
## 250 1.0000000 0.85416667
## 251 1.0000000 0.00000000
## 252 1.0000000 1.00000000
## 253 1.0000000 1.00000000
## 254 1.0000000 1.00000000
## 255 1.0000000 1.00000000
## 256 1.0000000 0.36363636
## 257 1.0000000 1.00000000
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## 260 1.0000000 1.00000000
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## 263 1.0000000 1.00000000
## 264 1.0000000 0.50000000
## 265 1.0000000 1.00000000
## 266 1.0000000 0.64102564
## 267 1.0000000 0.00000000
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## 270 1.0000000 0.77777778
## 271 1.0000000 1.00000000
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## 276 1.0000000 0.75000000
## 277 1.0000000 0.00000000
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## 279 1.0000000 1.00000000
## 280 1.0000000 1.00000000
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## 282 1.0000000 0.00000000
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## 284 1.0000000 1.00000000
## 285 1.0000000 0.81481481
## 286 1.0000000 0.83333333
## 287 1.0000000 0.42857143
## 288 0.7000000 0.67500000
## 289 1.0000000 1.00000000
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## 300 1.0000000 0.00000000
## 301 0.9756098 0.97560976
## 302 1.0000000 0.75000000
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## 1980 1.0000000 0.00000000
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## 2000 1.0000000 1.00000000
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## 2006 1.0000000 1.00000000
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## 2011 1.0000000 1.00000000
## 2012 1.0000000 1.00000000
## 2013 1.0000000 1.00000000
## 2014 1.0000000 1.00000000
## 2015 1.0000000 0.00000000
## 2016 0.9444444 0.83333333
## 2017 1.0000000 1.00000000
## 2018 1.0000000 0.00000000
## 2019 1.0000000 1.00000000
## 2020 1.0000000 0.00000000
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## 2154 1.0000000 0.00000000
## 2155 1.0000000 0.00000000
## 2156 1.0000000 0.00000000#creating a regressor of internal factors
fitinternal<-lm(formula=RoomRent~HotelCapacity+HasSwimmingPool+FreeWifi+FreeBreakfast, data =training_set)
summary(fitinternal)##
## Call:
## lm(formula = RoomRent ~ HotelCapacity + HasSwimmingPool + FreeWifi +
## FreeBreakfast, data = training_set)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8043 -2286 -1135 775 313879
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 3469.0712 274.6649 12.630 <2e-16 ***
## HotelCapacity -0.1026 1.0575 -0.097 0.923
## HasSwimmingPool 4892.9420 171.1561 28.588 <2e-16 ***
## FreeWifi 267.1570 272.2799 0.981 0.327
## FreeBreakfast 100.4281 150.2517 0.668 0.504
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7312 on 10766 degrees of freedom
## Multiple R-squared: 0.09281, Adjusted R-squared: 0.09248
## F-statistic: 275.4 on 4 and 10766 DF, p-value: < 2.2e-16#creating a regressor of external fators
fitexternal<-lm(formula=RoomRent~IsTouristDestination+IsWeekend+IsNewYearEve+IsMetroCity, data=training_set)
summary(fitexternal)##
## Call:
## lm(formula = RoomRent ~ IsTouristDestination + IsWeekend + IsNewYearEve +
## IsMetroCity, data = training_set)
##
## Residuals:
## Min 1Q Median 3Q Max
## -6922 -2936 -1321 1033 315968
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 4321.48 166.58 25.943 < 2e-16 ***
## IsTouristDestination 2244.22 161.87 13.864 < 2e-16 ***
## IsWeekend -33.82 157.85 -0.214 0.83035
## IsNewYearEve 739.33 232.03 3.186 0.00144 **
## IsMetroCity -1519.15 164.25 -9.249 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 7589 on 10766 degrees of freedom
## Multiple R-squared: 0.02274, Adjusted R-squared: 0.02237
## F-statistic: 62.62 on 4 and 10766 DF, p-value: < 2.2e-16#creating a regressor of optimum factors
fitoptimal<-lm(formula=RoomRent~IsTouristDestination+HasSwimmingPool+IsNewYearEve+IsMetroCity+StarRating+HotelCapacity, data=training_set)
summary(fitoptimal)##
## Call:
## lm(formula = RoomRent ~ IsTouristDestination + HasSwimmingPool +
## IsNewYearEve + IsMetroCity + StarRating + HotelCapacity,
## data = training_set)
##
## Residuals:
## Min 1Q Median 3Q Max
## -11995 -2373 -711 1049 308998
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -8615.856 409.016 -21.065 < 2e-16 ***
## IsTouristDestination 2269.917 150.651 15.067 < 2e-16 ***
## HasSwimmingPool 2112.919 183.645 11.505 < 2e-16 ***
## IsNewYearEve 702.754 203.505 3.453 0.000556 ***
## IsMetroCity -1660.269 154.920 -10.717 < 2e-16 ***
## StarRating 3730.666 128.298 29.078 < 2e-16 ***
## HotelCapacity -11.630 1.175 -9.894 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 6955 on 10764 degrees of freedom
## Multiple R-squared: 0.1795, Adjusted R-squared: 0.179
## F-statistic: 392.4 on 6 and 10764 DF, p-value: < 2.2e-16#running ttests on some factors
t.test(RoomRent,IsTouristDestination)##
## Welch Two Sample t-test
##
## data: RoomRent and IsTouristDestination
## t = 85.856, df = 13231, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 5348.337 5598.253
## sample estimates:
## mean of x mean of y
## 5473.9918380 0.6971735t.test(RoomRent,HasSwimmingPool)##
## Welch Two Sample t-test
##
## data: RoomRent and HasSwimmingPool
## t = 85.862, df = 13231, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 5348.678 5598.594
## sample estimates:
## mean of x mean of y
## 5473.9918380 0.3558041t.test(RoomRent,Population)##
## Welch Two Sample t-test
##
## data: RoomRent and Population
## t = -119.16, df = 13231, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -4483927 -4338799
## sample estimates:
## mean of x mean of y
## 5473.992 4416836.872t.test(RoomRent,HotelPincode)##
## Welch Two Sample t-test
##
## data: RoomRent and HotelPincode
## t = -173.45, df = 13252, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -396385.7 -387526.8
## sample estimates:
## mean of x mean of y
## 5473.992 397430.265t.test(RoomRent,Airport)##
## Welch Two Sample t-test
##
## data: RoomRent and Airport
## t = 85.535, df = 13231, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 5327.875 5577.792
## sample estimates:
## mean of x mean of y
## 5473.99184 21.15874Note that the echo = FALSE parameter was added to the code chunk to prevent printing of the R code that generated the plot.