Airnb Project
Surabhi Srivastava
26 January 2018
1- Read dataset in R and visualize length and breadth of dataset
setwd("C:/Users/SURABHI/Desktop/IIM INTERNSHIP")
hotel.df <- read.csv (paste("Airnb dataset.csv", sep=""))
View(hotel.df)
dim(hotel.df)## [1] 999 132- Create descriptive statistics of each variable
minimum of variable
min(hotel.df$reviews) ## [1] 0min(hotel.df$overall_satisfaction)## [1] NAmin(hotel.df$accommodates)## [1] 1min(hotel.df$bedrooms)## [1] NAmin(hotel.df$price)## [1] 23min(hotel.df$minstay)## [1] NAmaximum of variable
max(hotel.df$reviews)## [1] 199max(hotel.df$overall_satisfaction)## [1] NAmax(hotel.df$accommodates)## [1] 16max(hotel.df$bedrooms)## [1] NAmax(hotel.df$price)## [1] 9502max(hotel.df$minstay)## [1] NAmedian of variable
median(hotel.df$reviews)## [1] 2median(hotel.df$overall_satisfaction)## [1] NAmedian(hotel.df$accommodates)## [1] 4median(hotel.df$bedrooms)## [1] NAmedian(hotel.df$price)## [1] 103median(hotel.df$minstay)## [1] NAstandard deviation of variable
sd(hotel.df$reviews)## [1] 14.17979sd(hotel.df$overall_satisfaction)## [1] NAsd(hotel.df$accommodates)## [1] 2.120395sd(hotel.df$bedrooms)## [1] NAsd(hotel.df$price)## [1] 402.755sd(hotel.df$minstay)## [1] NA3- Create one way contingency table for categorical variables in dataset
mytable<- with(hotel.df, table(hotel.df$reviews))
mytable##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17
## 315 171 89 69 49 38 25 25 20 17 23 10 10 14 12 4 8 8
## 18 19 20 21 23 24 25 26 27 28 29 30 31 32 34 36 38 39
## 6 7 8 1 4 2 1 4 2 3 2 3 2 3 2 1 4 3
## 40 41 42 43 45 48 50 51 52 54 57 60 63 67 74 76 78 80
## 1 2 2 4 1 1 2 2 1 2 1 2 2 1 1 1 1 1
## 82 88 95 97 140 199
## 1 1 1 1 1 1mytable<- with(hotel.df, table(hotel.df$bedrooms))
mytable##
## 0 1 2 3 4 5 6 7 8
## 158 454 227 91 36 20 6 3 1mytable<- with(hotel.df, table(hotel.df$accommodates))
mytable##
## 1 2 3 4 5 6 7 8 9 10 11 12 14 16
## 10 258 93 342 61 145 14 39 6 16 3 7 3 2mytable<- with(hotel.df, table(hotel.df$minstay))
mytable##
## 1 2 3 4 5 6 7 8 9 10 12 14 15
## 220 158 195 99 83 35 130 2 1 2 2 7 1mytable<- with(hotel.df, table(hotel.df$overall_satisfaction))
mytable##
## 2 3 3.5 4 4.5 5
## 1 1 14 49 205 1534- Create two way contingency table for categorical variables in the dataset
mytable1<- xtabs(~price+bedrooms, data=hotel.df)
mytable1## bedrooms
## price 0 1 2 3 4 5 6 7 8
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## 4607 0 0 1 0 0 0 0 0 0
## 9502 0 0 0 0 1 0 0 0 0mytable1<- xtabs(~price+accommodates, data=hotel.df)
mytable1## accommodates
## price 1 2 3 4 5 6 7 8 9 10 11 12 14 16
## 23 3 0 0 1 0 0 0 0 0 0 0 0 0 0
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## 9502 0 0 0 0 0 0 0 1 0 0 0 0 0 0mytable1<- xtabs(~price+minstay, data=hotel.df)
mytable1## minstay
## price 1 2 3 4 5 6 7 8 9 10 12 14 15
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## 9502 1 0 0 0 0 0 0 0 0 0 0 0 0mytable1<-xtabs(~price+overall_satisfaction, data=hotel.df)
mytable1## overall_satisfaction
## price 2 3 3.5 4 4.5 5
## 23 0 0 1 0 1 1
## 29 0 0 0 2 0 0
## 30 0 0 0 1 0 0
## 31 0 0 0 0 2 0
## 32 0 0 0 2 1 0
## 33 0 0 0 0 1 0
## 34 0 0 0 0 1 0
## 35 0 0 0 0 4 0
## 36 0 0 0 0 0 1
## 37 0 0 1 0 0 0
## 38 0 0 0 0 1 1
## 40 0 0 0 3 4 1
## 41 0 0 0 0 1 0
## 43 0 0 0 1 1 0
## 45 0 0 0 0 1 2
## 46 0 0 1 0 8 2
## 47 0 0 0 0 2 0
## 48 0 0 0 1 3 0
## 49 0 0 1 1 2 0
## 50 0 0 0 1 0 1
## 52 0 0 0 1 4 2
## 53 0 0 0 1 1 0
## 55 0 0 0 0 1 0
## 56 0 0 0 1 1 0
## 57 0 0 0 0 5 3
## 58 0 0 1 1 13 11
## 59 0 0 0 0 1 0
## 60 0 0 0 0 1 1
## 61 0 0 0 1 1 0
## 62 0 0 0 1 2 0
## 63 0 0 1 2 4 2
## 64 0 0 0 0 1 1
## 65 0 0 0 1 1 0
## 66 0 0 0 1 3 0
## 67 0 0 0 0 1 2
## 68 0 0 0 1 3 3
## 69 0 0 0 4 10 5
## 70 0 0 0 0 1 0
## 71 0 0 0 1 1 1
## 72 0 0 0 0 1 0
## 74 0 0 0 1 0 2
## 75 0 0 0 0 6 4
## 79 0 0 0 0 1 1
## 80 0 0 0 0 7 9
## 83 0 0 0 0 2 0
## 84 0 0 0 0 1 0
## 85 0 0 0 0 2 1
## 87 0 0 1 0 1 8
## 89 0 0 0 0 0 1
## 90 1 1 0 0 0 0
## 91 0 0 0 0 5 2
## 92 0 0 0 2 13 8
## 93 0 0 1 1 4 4
## 94 0 0 0 0 1 0
## 98 0 0 0 1 8 4
## 101 0 0 0 0 1 0
## 102 0 0 0 1 4 0
## 103 0 0 0 0 0 7
## 104 0 0 1 1 5 4
## 106 0 0 0 0 1 1
## 107 0 0 1 0 1 0
## 109 0 0 0 0 1 1
## 110 0 0 0 0 1 0
## 113 0 0 1 0 0 1
## 114 0 0 0 0 5 1
## 115 0 0 0 0 9 5
## 117 0 0 0 0 0 1
## 119 0 0 0 0 1 0
## 121 0 0 0 0 0 1
## 123 0 0 0 0 0 1
## 127 0 0 0 2 2 3
## 130 0 0 0 0 1 0
## 132 0 0 0 0 0 1
## 133 0 0 0 2 0 0
## 138 0 0 0 1 5 4
## 139 0 0 0 0 2 0
## 145 0 0 0 0 0 1
## 149 0 0 0 1 3 4
## 150 0 0 1 0 0 0
## 153 0 0 0 1 0 1
## 156 0 0 0 0 1 0
## 161 0 0 0 0 0 1
## 162 0 0 0 1 4 2
## 167 0 0 0 0 1 0
## 173 0 0 0 3 2 3
## 184 0 0 0 0 0 2
## 185 0 0 0 0 1 0
## 190 0 0 1 0 0 0
## 196 0 0 0 0 2 1
## 207 0 0 0 0 0 1
## 208 0 0 0 0 0 1
## 218 0 0 0 0 1 0
## 225 0 0 0 0 1 1
## 230 0 0 0 1 2 0
## 231 0 0 0 0 1 1
## 253 0 0 0 0 1 1
## 265 0 0 0 0 0 1
## 277 0 0 0 0 0 1
## 287 0 0 1 0 0 2
## 288 0 0 0 0 0 1
## 300 0 0 0 1 0 0
## 305 0 0 0 0 1 0
## 330 0 0 0 0 0 1
## 345 0 0 0 0 2 0
## 346 0 0 0 0 0 2
## 369 0 0 0 0 1 0
## 403 0 0 0 1 0 2
## 404 0 0 0 0 1 0
## 432 0 0 0 0 0 1
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## 518 0 0 0 0 0 1
## 576 0 0 0 0 0 2
## 599 0 0 0 0 0 1
## 749 0 0 0 0 0 1
## 863 0 0 0 0 0 1
## 980 0 0 0 0 1 05- Draw a boxplot that belongs to your study
boxplot(hotel.df$price, xlab="price", ylab="", main="PRICE OF ROOMS", horizontal=TRUE)
boxplot(hotel.df$price~ hotel.df$bedrooms, xlab="price", ylab="No. of bedrooms", main="PRICE OF ROOMS", horizontal=TRUE, col=c("red", "blue", "green"))
boxplot(hotel.df$price~ hotel.df$reviews, xlab="price", ylab="Reviews", main="PRICE OF ROOMS", horizontal=TRUE, col=c("red", "blue", "green"))
boxplot(hotel.df$price~ hotel.df$overall_satisfaction, xlab="price", ylab="Overall Satisfaction", main="PRICE OF ROOMS", horizontal=TRUE, col=c("red", "blue", "green"))
boxplot(hotel.df$price~ hotel.df$accommodates, xlab="price", ylab="Accomodations", main="PRICE OF ROOMS", horizontal=TRUE, col=c("red", "blue", "green"))
6- Draw histogram for your suitable data fields
hist(hotel.df$price, main="PRICE FREQUENCY",xlab = "price of rooms",breaks = 25, xlim= c(0,3000), col="green")
7- Draw suitable plot for your data analysis.
plot(x= hotel.df$price, y=hotel.df$bedrooms, col="blue", main="bedrooms v/s price", xlab = "price", ylab = "number of bedrooms")
plot(x= hotel.df$price, y=hotel.df$overall_satisfaction, col="blue", main="overall satisfaction v/s price", xlab = "price", ylab = "Overall Satisfaction")
plot(x= hotel.df$price, y=hotel.df$minstay, col="blue", main="minstay v/s price", xlab = "price", ylab = "Minstay")
8- Create a correlation matrix
new_data<- hotel.df[,6:11]
r=cor(new_data)
r## reviews overall_satisfaction accommodates
## reviews 1.00000000 NA -0.1539512
## overall_satisfaction NA 1 NA
## accommodates -0.15395117 NA 1.0000000
## bedrooms NA NA NA
## price -0.09723858 NA 0.3146828
## minstay NA NA NA
## bedrooms price minstay
## reviews NA -0.09723858 NA
## overall_satisfaction NA NA NA
## accommodates NA 0.31468282 NA
## bedrooms 1 NA NA
## price NA 1.00000000 NA
## minstay NA NA 19- Visualize your correlation matrix using corrgram
library(corrgram)
corrgram(hotel.df, order = T, text.panel=panel.txt,
lower.panel = panel.shade,
upper.panel = panel.pie, main="Corrgram of all variables")
10- Create a scatterplot matrix for your dataset
library(car)
scatterplotMatrix(formula=~host_id+reviews+overall_satisfaction+price,cex=0.6,data = hotel.df,diagonal="histogram")
11- Run a suitable test to check your hypothesis for your suitable
Null Hypothesis - There is no correlation between the price and minstay
cor.test(hotel.df$price,hotel.df$minstay)##
## Pearson's product-moment correlation
##
## data: hotel.df$price and hotel.df$minstay
## t = 3.1192, df = 933, p-value = 0.001869
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.03772307 0.16463048
## sample estimates:
## cor
## 0.1015901Since p<0.05 hence we reject the null hypothesis.
12- Run a t-test to analyse your hypothesis
t.test(hotel.df$price,hotel.df$minstay)##
## Welch Two Sample t-test
##
## data: hotel.df$price and hotel.df$minstay
## t = 14.133, df = 998.07, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 155.0829 205.0945
## sample estimates:
## mean of x mean of y
## 183.547548 3.458824