Hotel Room Pricing In The Indian Market
Palash Badjatya
January 27, 2018
Project Title : Hotel Room Pricing In The Indian Market
Name : Palash Badjatya
E-mail : palash.badjatya01@gmail.com
College : Acropolis Technical Campus
Introduction
This project is about the hotel room pricing in the indian market over different cities. It tells you about what affects the pricing system of a hotel room. Like whether the city is a tourist place or whetherit is a weekend that affets the pricing of a hotel room.There are many other things which also affects the pricing of a hotel room like breakfast, swimming pool, any special occasion(like new years eve), etc.
Overview of the study
The objective of this project is to identify the factors that matter the most. This dataset consistof data from different hoels located in different cities.
Data
The data cosist of many variables like the city name, population of that city, whether the city is tourist place or not, whether the city is metro city or not, what is the room rent, is it a 5star hotel or not, hotel adress and description, whether there is a near long weekend, what is capacity of the hotel, free breakfast and wifi is included or not, is there any swimming pool for guests or not
hotels.df <- read.csv(paste("Cities42.csv", sep=""))
attach(hotels.df)
head(hotels.df)## CityName Population CityRank IsMetroCity IsTouristDestination IsWeekend
## 1 Mumbai 12442373 0 1 1 1
## 2 Mumbai 12442373 0 1 1 0
## 3 Mumbai 12442373 0 1 1 1
## 4 Mumbai 12442373 0 1 1 1
## 5 Mumbai 12442373 0 1 1 0
## 6 Mumbai 12442373 0 1 1 1
## IsNewYearEve Date HotelName RoomRent StarRating Airport
## 1 0 Dec 18 2016 Vivanta by Taj 12375 5 21
## 2 0 Dec 21 2016 Vivanta by Taj 10250 5 21
## 3 0 Dec 24 2016 Vivanta by Taj 9900 5 21
## 4 0 Dec 25 2016 Vivanta by Taj 10350 5 21
## 5 0 Dec 28 2016 Vivanta by Taj 12000 5 21
## 6 1 Dec 31 2016 Vivanta by Taj 11475 5 21
## HotelAddress HotelPincode
## 1 90 Cuffe Parade, Colaba, Mumbai, Maharashtra 400005
## 2 91 Cuffe Parade, Colaba, Mumbai, Maharashtra 400006
## 3 92 Cuffe Parade, Colaba, Mumbai, Maharashtra 400007
## 4 93 Cuffe Parade, Colaba, Mumbai, Maharashtra 400008
## 5 94 Cuffe Parade, Colaba, Mumbai, Maharashtra 400009
## 6 95 Cuffe Parade, Colaba, Mumbai, Maharashtra 400010
## HotelDescription FreeWifi FreeBreakfast
## 1 Luxury hotel with spa, near Gateway of India 1 0
## 2 Luxury hotel with spa, near Gateway of India 1 0
## 3 Luxury hotel with spa, near Gateway of India 1 0
## 4 Luxury hotel with spa, near Gateway of India 1 0
## 5 Luxury hotel with spa, near Gateway of India 1 0
## 6 Luxury hotel with spa, near Gateway of India 1 0
## HotelCapacity HasSwimmingPool
## 1 287 1
## 2 287 1
## 3 287 1
## 4 287 1
## 5 287 1
## 6 287 1dim(hotels.df)## [1] 13232 19library(psych)
describe(hotels.df)## vars n mean sd median trimmed
## CityName* 1 13232 18.07 11.72 16 17.29
## Population 2 13232 4416836.87 4258386.00 3046163 4040816.22
## CityRank 3 13232 14.83 13.51 9 13.30
## IsMetroCity 4 13232 0.28 0.45 0 0.23
## IsTouristDestination 5 13232 0.70 0.46 1 0.75
## IsWeekend 6 13232 0.62 0.48 1 0.65
## IsNewYearEve 7 13232 0.12 0.33 0 0.03
## Date* 8 13232 14.30 2.69 14 14.39
## HotelName* 9 13232 841.19 488.16 827 841.18
## RoomRent 10 13232 5473.99 7333.12 4000 4383.33
## StarRating 11 13232 3.46 0.76 3 3.40
## Airport 12 13232 21.16 22.76 15 16.39
## HotelAddress* 13 13232 1202.53 582.17 1261 1233.25
## HotelPincode 14 13232 397430.26 259837.50 395003 388540.47
## HotelDescription* 15 13224 581.34 363.26 567 575.37
## FreeWifi 16 13232 0.93 0.26 1 1.00
## FreeBreakfast 17 13232 0.65 0.48 1 0.69
## HotelCapacity 18 13232 62.51 76.66 34 46.03
## HasSwimmingPool 19 13232 0.36 0.48 0 0.32
## mad min max range skew
## 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 -0.77
## 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
## 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* 1.92 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 0.00One way contingency table
mytable <- with(hotels.df, table(StarRating))
mytable## 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 1408mytable1 <- with(hotels.df, table(IsMetroCity))
mytable1## IsMetroCity
## 0 1
## 9472 3760mytable2 <- with(hotels.df, table(FreeBreakfast))
mytable2## FreeBreakfast
## 0 1
## 4643 8589mytable3 <- with(hotels.df, table(CityRank))
mytable3## CityRank
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## 712 2048 656 416 536 424 512 80 600 768 32 128 16 136 160
## 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
## 432 448 624 128 264 40 224 336 392 48 160 120 272 104 456
## 32 33 34 35 36 37 38 39 40 42 43 44
## 48 56 280 64 136 88 128 136 264 144 328 288mytable4 <- with(hotels.df, table(IsTouristDestination))
mytable4## IsTouristDestination
## 0 1
## 4007 9225Two way contingency table
mytable <- xtabs(~ FreeBreakfast+StarRating, data=hotels.df)
mytable## StarRating
## FreeBreakfast 0 1 2 2.5 3 3.2 3.3 3.4 3.5 3.6 3.7 3.8
## 0 16 0 216 296 1789 0 8 0 661 8 0 8
## 1 0 8 224 336 4164 8 8 8 1091 0 24 8
## StarRating
## FreeBreakfast 3.9 4 4.1 4.3 4.4 4.5 4.7 4.8 5
## 0 16 783 0 16 0 224 8 0 594
## 1 16 1680 24 0 8 152 0 16 814mytable1 <- xtabs(~ IsMetroCity+StarRating, data=hotels.df)
mytable1## StarRating
## IsMetroCity 0 1 2 2.5 3 3.2 3.3 3.4 3.5 3.6 3.7 3.8
## 0 16 8 344 456 4336 8 16 8 1312 0 24 16
## 1 0 0 96 176 1617 0 0 0 440 8 0 0
## StarRating
## IsMetroCity 3.9 4 4.1 4.3 4.4 4.5 4.7 4.8 5
## 0 32 1696 24 16 8 288 8 16 840
## 1 0 767 0 0 0 88 0 0 568mytable2 <- xtabs(~ IsMetroCity+IsTouristDestination, data=hotels.df)
mytable2## IsTouristDestination
## IsMetroCity 0 1
## 0 3352 6120
## 1 655 3105Boxplots
boxplot(hotels.df$CityRank , horizontal =TRUE,main="Rank of the cities",col = "lightblue" )
boxplot(hotels.df$Population , horizontal =TRUE, main="Population",col = "yellow" )
boxplot(hotels.df$StarRating ~ hotels.df$FreeBreakfast, horizontal=TRUE,
ylab="breakfast avalability", xlab="Star ratings", las=1,
main="Analysis of star rating and breakfast avalability",
col=c("pink","yellow")
)
boxplot(hotels.df$RoomRent ~ hotels.df$IsMetroCity, horizontal=TRUE,
ylab="City(metro=1,other=0)", xlab="Room rent", las=1,
main="Analysis of type of city and room rent of hotels",
col=c("red","blue")
)
Histograms
hist(hotels.df$RoomRent,
main="Analysis of room rents of hotels",
xlab="Rents of room", ylab="Relative frequency",
breaks=30, col="lightblue", freq=FALSE)
hist(hotels.df$StarRating,
main="Analysis of star ratings of hotels",
xlab="Star ratings", ylab="Relative frequency",
breaks=30, col="red", freq=FALSE)
hist(hotels.df$Population, main= "Population" ,xlab="Population" ,col = "peachpuff")
hist(hotels.df$HotelCapacity, main = "Capacity of hotels", xlab = "Hotel Capacity", col = "blue")
Scatterplots
library(car) ##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(RoomRent~StarRating, data=hotels.df,
spread=FALSE, smoother.args=list(lty=2),
main="Scatter plot of Star Rating vs Room rent",
ylab="Room Rent",
xlab="Star Rating")
scatterplotMatrix(formula = ~ RoomRent + IsWeekend + IsNewYearEve +Airport , data = hotels.df, pch = 16)## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit smooth
## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit smooth
library(car)
scatterplot(x = hotels.df$Population , y = hotels.df$CityRank, main="Population Vs City Rank " , xlab="Population", ylab="City rank")
####Correlation Matrix
cor(hotels.df[, c(2,3,4,5,6,7,10,11,18)])## Population CityRank IsMetroCity
## Population 1.0000000000 -0.8353204432 0.7712260105
## CityRank -0.8353204432 1.0000000000 -0.5643937903
## IsMetroCity 0.7712260105 -0.5643937903 1.0000000000
## IsTouristDestination -0.0482029722 0.2807134520 0.1763717063
## IsWeekend 0.0115926802 -0.0072564766 0.0018118005
## IsNewYearEve 0.0007332482 -0.0006326444 0.0006464753
## RoomRent -0.0887280632 0.0939855292 -0.0668397705
## StarRating 0.1341365933 -0.1333810133 0.0776028661
## HotelCapacity 0.2599830516 -0.2561197059 0.1871502153
## IsTouristDestination IsWeekend IsNewYearEve
## Population -0.048202972 0.011592680 0.0007332482
## CityRank 0.280713452 -0.007256477 -0.0006326444
## IsMetroCity 0.176371706 0.001811801 0.0006464753
## IsTouristDestination 1.000000000 -0.019481101 -0.0022663884
## IsWeekend -0.019481101 1.000000000 0.2923820508
## IsNewYearEve -0.002266388 0.292382051 1.0000000000
## RoomRent 0.122502963 0.004580134 0.0384912269
## StarRating -0.040554998 0.006378436 0.0023608970
## HotelCapacity -0.094356091 0.006306507 0.0013526790
## RoomRent StarRating HotelCapacity
## Population -0.088728063 0.134136593 0.259983052
## CityRank 0.093985529 -0.133381013 -0.256119706
## IsMetroCity -0.066839771 0.077602866 0.187150215
## IsTouristDestination 0.122502963 -0.040554998 -0.094356091
## IsWeekend 0.004580134 0.006378436 0.006306507
## IsNewYearEve 0.038491227 0.002360897 0.001352679
## RoomRent 1.000000000 0.369373425 0.157873308
## StarRating 0.369373425 1.000000000 0.637430337
## HotelCapacity 0.157873308 0.637430337 1.000000000Corrgrams
library(corrgram)
corrgram(hotels.df, lower.panel = panel.shade, upper.panel = panel.pie, text.panel = panel.txt, main = "Corrgram of all variables")
Correlation tests
cor.test(hotels.df$RoomRent, hotels.df$StarRating)##
## Pearson's product-moment correlation
##
## data: hotels.df$RoomRent and hotels.df$StarRating
## t = 45.719, df = 13230, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.3545660 0.3839956
## sample estimates:
## cor
## 0.3693734cor.test(hotels.df$RoomRent, hotels.df$IsMetroCity)##
## Pearson's product-moment correlation
##
## data: hotels.df$RoomRent and hotels.df$IsMetroCity
## t = -7.7053, df = 13230, p-value = 1.399e-14
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08378329 -0.04985761
## sample estimates:
## cor
## -0.06683977cor.test(hotels.df$RoomRent, hotels.df$CityRank)##
## Pearson's product-moment correlation
##
## data: hotels.df$RoomRent and hotels.df$CityRank
## t = 10.858, df = 13230, p-value < 2.2e-16
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.07707001 0.11084696
## sample estimates:
## cor
## 0.09398553cor.test(hotels.df$RoomRent, hotels.df$IsNewYearEve)##
## Pearson's product-moment correlation
##
## data: hotels.df$RoomRent and hotels.df$IsNewYearEve
## t = 4.4306, df = 13230, p-value = 9.472e-06
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.02146637 0.05549377
## sample estimates:
## cor
## 0.03849123T test
Null Hypothesis - Their is no Difference between the Room Rent on new year’s eve and on other days
t.test(hotels.df$RoomRent ~ hotels.df$IsNewYearEve)##
## Welch Two Sample t-test
##
## data: hotels.df$RoomRent by hotels.df$IsNewYearEve
## t = -4.1793, df = 2065, p-value = 3.046e-05
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -1256.5297 -453.9099
## sample estimates:
## mean in group 0 mean in group 1
## 5367.606 6222.826P-Value = 3.046e-05 (<0.05) Which is small enough for Rejecting the Null Hupothesis. Hence there is significant difference between the Room Rent on new year’s eve and on other days
Null Hypothesis - Their is no Difference between the Room Rent where wifi is free and other rooms.
t.test(hotels.df$RoomRent ~ hotels.df$FreeWifi)##
## Welch Two Sample t-test
##
## data: hotels.df$RoomRent by hotels.df$FreeWifi
## t = -0.76847, df = 1804.7, p-value = 0.4423
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -360.5977 157.5701
## sample estimates:
## mean in group 0 mean in group 1
## 5380.004 5481.518As we can see the P-Value = 0.44 (>0.05) , We Fail To reject the Null Hypothesis. It Shows that Their is No Significant Difference Between the Room Rent where wifi is free and other rooms. Null Hypothesis: Their is no difference in the means of room Rent where free Breakfast is available or not