Data Analytics of the Airline Industry.
Sree Gowri Addepalli
January 20, 2018
This is a mini project which in detail tries to understand the Airline Industry with the help of data And fundamentally tries to research on this Question: What factors explain the difference in price between an economy ticket and a premium-economy airline ticket?
Premium Economy Vs Economy Ticket Pricing by Airlines: Premium Economy is found mostly on international flights and, compared to standard Economy, offers about 5-7 inches of extra legroom as well as additional amenities, which can include: 1-2 extra inches of seat width. 2-3 extra inches of seat recline. Adjustable headrests, legrests, or lumbar support.
The following data analysis shall help understand the relative pricing between an economy ticket and premium economy airline ticket.
#Read the data into R
setwd("C:/Users/GOWRI/Desktop/iim_internship/Week_3/Mini_Project")
Airlines <- read.csv(file="SixAirlinesDataV2.csv",head=TRUE,sep=",")
View(Airlines)# Summarize the data to understand the mean, median, standard deviation of each variable in the Airline Dataset.
library(psych)
summaryOfAirlines <- describe(Airlines)
AirlineSum <- summaryOfAirlines[ c(3,4,5,8,9)]
AirlineSum## mean sd median min max
## Airline* 3.01 1.65 2.00 1.00 6.00
## Aircraft* 1.67 0.47 2.00 1.00 2.00
## FlightDuration 7.58 3.54 7.79 1.25 14.66
## TravelMonth* 2.56 1.17 3.00 1.00 4.00
## IsInternational* 1.91 0.28 2.00 1.00 2.00
## SeatsEconomy 202.31 76.37 185.00 78.00 389.00
## SeatsPremium 33.65 13.26 36.00 8.00 66.00
## PitchEconomy 31.22 0.66 31.00 30.00 33.00
## PitchPremium 37.91 1.31 38.00 34.00 40.00
## WidthEconomy 17.84 0.56 18.00 17.00 19.00
## WidthPremium 19.47 1.10 19.00 17.00 21.00
## PriceEconomy 1327.08 988.27 1242.00 65.00 3593.00
## PricePremium 1845.26 1288.14 1737.00 86.00 7414.00
## PriceRelative 0.49 0.45 0.36 0.02 1.89
## SeatsTotal 235.96 85.29 227.00 98.00 441.00
## PitchDifference 6.69 1.76 7.00 2.00 10.00
## WidthDifference 1.63 1.19 1.00 0.00 4.00
## PercentPremiumSeats 14.65 4.84 13.21 4.71 24.69summary(Airlines)## Airline Aircraft FlightDuration TravelMonth
## AirFrance: 74 AirBus:151 Min. : 1.250 Aug:127
## British :175 Boeing:307 1st Qu.: 4.260 Jul: 75
## Delta : 46 Median : 7.790 Oct:127
## Jet : 61 Mean : 7.578 Sep:129
## Singapore: 40 3rd Qu.:10.620
## Virgin : 62 Max. :14.660
## IsInternational SeatsEconomy SeatsPremium PitchEconomy
## Domestic : 40 Min. : 78.0 Min. : 8.00 Min. :30.00
## International:418 1st Qu.:133.0 1st Qu.:21.00 1st Qu.:31.00
## Median :185.0 Median :36.00 Median :31.00
## Mean :202.3 Mean :33.65 Mean :31.22
## 3rd Qu.:243.0 3rd Qu.:40.00 3rd Qu.:32.00
## Max. :389.0 Max. :66.00 Max. :33.00
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## Min. :34.00 Min. :17.00 Min. :17.00 Min. : 65
## 1st Qu.:38.00 1st Qu.:18.00 1st Qu.:19.00 1st Qu.: 413
## Median :38.00 Median :18.00 Median :19.00 Median :1242
## Mean :37.91 Mean :17.84 Mean :19.47 Mean :1327
## 3rd Qu.:38.00 3rd Qu.:18.00 3rd Qu.:21.00 3rd Qu.:1909
## Max. :40.00 Max. :19.00 Max. :21.00 Max. :3593
## PricePremium PriceRelative SeatsTotal PitchDifference
## Min. : 86.0 Min. :0.0200 Min. : 98 Min. : 2.000
## 1st Qu.: 528.8 1st Qu.:0.1000 1st Qu.:166 1st Qu.: 6.000
## Median :1737.0 Median :0.3650 Median :227 Median : 7.000
## Mean :1845.3 Mean :0.4872 Mean :236 Mean : 6.688
## 3rd Qu.:2989.0 3rd Qu.:0.7400 3rd Qu.:279 3rd Qu.: 7.000
## Max. :7414.0 Max. :1.8900 Max. :441 Max. :10.000
## WidthDifference PercentPremiumSeats
## Min. :0.000 Min. : 4.71
## 1st Qu.:1.000 1st Qu.:12.28
## Median :1.000 Median :13.21
## Mean :1.633 Mean :14.65
## 3rd Qu.:3.000 3rd Qu.:15.36
## Max. :4.000 Max. :24.69To understand the factors affecting the difference in pricing of economy and premium economy, as relative pricing is the difference between the prices of economy and premium economy, we shall visualize the factors affecting it.
# Effect of Airline on relative pricing.
library(lattice)
boxplot(Airlines$PriceRelative ~Airlines$Airline ,
xlab = "Airline", ylab ="Relative Pricing", main = "Effect of Airlines on relative pricing.")
# Here, we can see that Airfrance and Delta do not have a major difference in the pricing relatively to it's premium economy and economy class while Jet Airways has the maximum difference and Airline being a strong factor.# Effect of Aircraft on relative pricing.
library(lattice)
boxplot(Airlines$PriceRelative ~Airlines$Aircraft ,
xlab = "Aircraft", ylab ="Relative Pricing", main = "Effect of Aircraft on relative pricing.")
# Here, we can see that Airbus and Boeing do not have a major difference in the pricing relatively to it's premium economy and economy class which shows statistically less significant on relative pricing.# Effect of Flight Duration on relative pricing.
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(Airlines$PriceRelative~Airlines$FlightDuration,spread=FALSE, smoother.args=list(lty=2), pch=19,
main="Scatterplot of Relative pricing vs. Flight Duration ",
xlab="Flight Duration",
ylab="Relative Pricing")
# Here, we can see that there is a positive correlation between relative pricing and the flight duration, which says as the duration increases, the difference in the prices too increases.# Effect of Travel month on relative pricing.
library(lattice)
boxplot(Airlines$PriceRelative ~Airlines$TravelMonth ,
xlab = "Travel Month", ylab ="Relative Pricing", main = "Effect of Travel Month on relative pricing.")
# Here, we can see that travelling months don't play a significant role in the prices of economy and premium economy tickets.# Effect of Is International on relative pricing.
library(lattice)
boxplot(Airlines$PriceRelative ~Airlines$IsInternational ,
xlab = "Is International", ylab ="Relative Pricing", main = "Effect of is International on relative pricing.")
# Here, we can see that is International plays a significant role in the prices of economy and premium economy tickets, where the difference maximum being in International flights rather than domestic flights.# Effect of pitch on Relative pricing.
library(car)
scatterplotMatrix(formula = ~ Airlines$PriceRelative + Airlines$PitchEconomy + Airlines$PitchPremium + Airlines$PitchDifference , cex=0.6,
data=Airlines, main = " Effect of pitch on relative pricing.")## Warning in smoother(x, y, col = col[2], log.x = FALSE, log.y = FALSE,
## spread = spread, : could not fit positive part of the spread
# We can see that Pitch economy has a negative correlation, while pitch premium and pitch difference have a positive correlation with relative pricing.# Effect of width on Relative pricing.
library(car)
scatterplotMatrix(formula = ~ Airlines$PriceRelative + Airlines$WidthEconomy + Airlines$WidthPremium + Airlines$WidthDifference , cex=0.6,
data=Airlines, main = "Effect of width on relative pricing." )
# We can see that width economy has a linear correlation, while width premium and width difference have a positive correlation with relative pricing.# Effect of seats on Relative pricing.
library(car)
scatterplotMatrix(formula = ~ Airlines$PriceRelative + Airlines$SeatsEconomy + Airlines$SeatsPremium + Airlines$SeatsTotal , cex=0.6,
data=Airlines, main = "Effect of seats on relative pricing." )
# We can see that seats economy and seats total have a linear correlation, while seats premium has a negative correlation with relative pricing.library(car)
scatterplot(Airlines$PriceRelative~Airlines$PercentPremiumSeats,spread=FALSE, smoother.args=list(lty=2), pch=19,
main="Scatterplot of Relative price vs Percent of Premium seats ",
xlab="Percent of Premium seats",
ylab="Relative price")
# We can see that Percent Premium Seats has a negative correlation with relative pricing.# Effect of variables on relative pricing.
# Draw a Corrgram
library("corrgram") ## Warning: replacing previous import by 'magrittr::%>%' when loading
## 'dendextend'corrgram(Airlines, order=FALSE, lower.panel=panel.shade,
upper.panel=panel.pie, text.panel=panel.txt,
main="Corrgram of Airlines variables")
# Create a Variance-Covariance Matrix.
# creating a subset of numeric values.
Airline <- Airlines[,c(3,6:18)]
covAirline <-cov(Airline)
covAirline## FlightDuration SeatsEconomy SeatsPremium
## FlightDuration 12.5462183 52.9194291 7.57372426
## SeatsEconomy 52.9194291 5832.9154300 633.07060954
## SeatsPremium 7.5737243 633.0706095 175.86521648
## PitchEconomy 0.6817421 7.2117665 -0.29725856
## PitchPremium 0.4477835 11.9637325 0.08508595
## WidthEconomy 0.9014224 15.9105138 3.36977440
## WidthPremium 0.4019845 8.5832800 -0.03954019
## PriceEconomy 1983.5401655 9673.7944684 1489.38359627
## PricePremium 2959.9783043 17413.2541733 3717.36428960
## PriceRelative 0.1932368 0.1361699 -0.58078765
## SeatsTotal 60.4931534 6465.9860396 808.93582602
## PitchDifference -0.2339587 4.7519660 0.38234451
## WidthDifference -0.4994380 -7.3272338 -3.40931459
## PercentPremiumSeats 1.0379912 -122.3914537 31.14753127
## PitchEconomy PitchPremium WidthEconomy WidthPremium
## FlightDuration 0.6817421 0.44778348 0.90142242 0.40198446
## SeatsEconomy 7.2117665 11.96373253 15.91051379 8.58327998
## SeatsPremium -0.2972586 0.08508595 3.36977440 -0.03954019
## PitchEconomy 0.4292471 -0.47398546 0.10756500 -0.38766208
## PitchPremium -0.4739855 1.72639580 -0.01739081 1.08157435
## WidthEconomy 0.1075650 -0.01739081 0.31081765 0.05010845
## WidthPremium -0.3876621 1.08157435 0.05010845 1.20378776
## PriceEconomy 238.7031905 65.42513354 37.46095191 -61.85450011
## PricePremium 190.8517195 149.85356368 108.11611707 90.47997668
## PriceRelative -0.1248808 0.24719874 -0.01104335 0.24928593
## SeatsTotal 6.9145079 12.04881848 19.28028819 8.54373979
## PitchDifference -0.9032326 2.20038126 -0.12495581 1.46923643
## WidthDifference -0.4952271 1.09896515 -0.26070920 1.15367930
## PercentPremiumSeats -0.3261739 -1.11655834 0.61321816 -0.97393787
## PriceEconomy PricePremium PriceRelative
## FlightDuration 1983.54017 2959.97830 0.19323683
## SeatsEconomy 9673.79447 17413.25417 0.13616991
## SeatsPremium 1489.38360 3717.36429 -0.58078765
## PitchEconomy 238.70319 190.85172 -0.12488080
## PitchPremium 65.42513 149.85356 0.24719874
## WidthEconomy 37.46095 108.11612 -0.01104335
## WidthPremium -61.85450 90.47998 0.24928593
## PriceEconomy 976684.06198 1147494.76801 -128.49991725
## PricePremium 1147494.76801 1659293.11947 18.48428836
## PriceRelative -128.49992 18.48429 0.20302893
## SeatsTotal 11163.17806 21130.61846 -0.44461774
## PitchDifference -173.27806 -40.99816 0.37207954
## WidthDifference -99.31545 -17.63614 0.26032928
## PercentPremiumSeats 312.61077 726.01582 -0.35252750
## SeatsTotal PitchDifference WidthDifference
## FlightDuration 60.4931534 -0.2339587 -0.4994380
## SeatsEconomy 6465.9860396 4.7519660 -7.3272338
## SeatsPremium 808.9358260 0.3823445 -3.4093146
## PitchEconomy 6.9145079 -0.9032326 -0.4952271
## PitchPremium 12.0488185 2.2003813 1.0989652
## WidthEconomy 19.2802882 -0.1249558 -0.2607092
## WidthPremium 8.5437398 1.4692364 1.1536793
## PriceEconomy 11163.1780647 -173.2780570 -99.3154520
## PricePremium 21130.6184629 -40.9981558 -17.6361404
## PriceRelative -0.4446177 0.3720795 0.2603293
## SeatsTotal 7274.9218656 5.1343105 -10.7365484
## PitchDifference 5.1343105 3.1036138 1.5941922
## WidthDifference -10.7365484 1.5941922 1.4143885
## PercentPremiumSeats -91.2439224 -0.7903844 -1.5871560
## PercentPremiumSeats
## FlightDuration 1.0379912
## SeatsEconomy -122.3914537
## SeatsPremium 31.1475313
## PitchEconomy -0.3261739
## PitchPremium -1.1165583
## WidthEconomy 0.6132182
## WidthPremium -0.9739379
## PriceEconomy 312.6107669
## PricePremium 726.0158229
## PriceRelative -0.3525275
## SeatsTotal -91.2439224
## PitchDifference -0.7903844
## WidthDifference -1.5871560
## PercentPremiumSeats 23.4493343#Transform covariance to correlation matrix
covCorrAirline <- cov2cor(covAirline)
covCorrAirline## FlightDuration SeatsEconomy SeatsPremium PitchEconomy
## FlightDuration 1.00000000 0.195621187 0.161236400 0.29377174
## SeatsEconomy 0.19562119 1.000000000 0.625056587 0.14412692
## SeatsPremium 0.16123640 0.625056587 1.000000000 -0.03421296
## PitchEconomy 0.29377174 0.144126924 -0.034212963 1.00000000
## PitchPremium 0.09621471 0.119221250 0.004883123 -0.55060624
## WidthEconomy 0.45647720 0.373670252 0.455782883 0.29448586
## WidthPremium 0.10343747 0.102431959 -0.002717527 -0.53929285
## PriceEconomy 0.56664039 0.128167220 0.113642176 0.36866123
## PricePremium 0.64873981 0.177000928 0.217612376 0.22614179
## PriceRelative 0.12107501 0.003956939 -0.097196009 -0.42302204
## SeatsTotal 0.20023299 0.992607966 0.715171053 0.12373524
## PitchDifference -0.03749288 0.035318044 0.016365566 -0.78254993
## WidthDifference -0.11856070 -0.080670148 -0.216168666 -0.63557430
## PercentPremiumSeats 0.06051625 -0.330935223 0.485029771 -0.10280880
## PitchPremium WidthEconomy WidthPremium PriceEconomy
## FlightDuration 0.096214708 0.45647720 0.103437469 0.56664039
## SeatsEconomy 0.119221250 0.37367025 0.102431959 0.12816722
## SeatsPremium 0.004883123 0.45578288 -0.002717527 0.11364218
## PitchEconomy -0.550606241 0.29448586 -0.539292852 0.36866123
## PitchPremium 1.000000000 -0.02374087 0.750259029 0.05038455
## WidthEconomy -0.023740873 1.00000000 0.081918728 0.06799061
## WidthPremium 0.750259029 0.08191873 1.000000000 -0.05704522
## PriceEconomy 0.050384550 0.06799061 -0.057045224 1.00000000
## PricePremium 0.088539147 0.15054837 0.064020043 0.90138870
## PriceRelative 0.417539056 -0.04396116 0.504247591 -0.28856711
## SeatsTotal 0.107512784 0.40545860 0.091297500 0.13243313
## PitchDifference 0.950591466 -0.12722421 0.760121272 -0.09952511
## WidthDifference 0.703281797 -0.39320512 0.884149655 -0.08449975
## PercentPremiumSeats -0.175487414 0.22714172 -0.183312058 0.06532232
## PricePremium PriceRelative SeatsTotal PitchDifference
## FlightDuration 0.64873981 0.121075014 0.20023299 -0.03749288
## SeatsEconomy 0.17700093 0.003956939 0.99260797 0.03531804
## SeatsPremium 0.21761238 -0.097196009 0.71517105 0.01636557
## PitchEconomy 0.22614179 -0.423022038 0.12373524 -0.78254993
## PitchPremium 0.08853915 0.417539056 0.10751278 0.95059147
## WidthEconomy 0.15054837 -0.043961160 0.40545860 -0.12722421
## WidthPremium 0.06402004 0.504247591 0.09129750 0.76012127
## PriceEconomy 0.90138870 -0.288567110 0.13243313 -0.09952511
## PricePremium 1.00000000 0.031846537 0.19232533 -0.01806629
## PriceRelative 0.03184654 1.000000000 -0.01156894 0.46873025
## SeatsTotal 0.19232533 -0.011568942 1.00000000 0.03416915
## PitchDifference -0.01806629 0.468730249 0.03416915 1.00000000
## WidthDifference -0.01151218 0.485802437 -0.10584398 0.76089108
## PercentPremiumSeats 0.11639097 -0.161565556 -0.22091465 -0.09264869
## WidthDifference PercentPremiumSeats
## FlightDuration -0.11856070 0.06051625
## SeatsEconomy -0.08067015 -0.33093522
## SeatsPremium -0.21616867 0.48502977
## PitchEconomy -0.63557430 -0.10280880
## PitchPremium 0.70328180 -0.17548741
## WidthEconomy -0.39320512 0.22714172
## WidthPremium 0.88414965 -0.18331206
## PriceEconomy -0.08449975 0.06532232
## PricePremium -0.01151218 0.11639097
## PriceRelative 0.48580244 -0.16156556
## SeatsTotal -0.10584398 -0.22091465
## PitchDifference 0.76089108 -0.09264869
## WidthDifference 1.00000000 -0.27559416
## PercentPremiumSeats -0.27559416 1.00000000After analyzing the data, we can see that the following have a correlation with relative pricing: - Airline - Flightduration - IsInternational - Pitch Economy - Pitch Premium - Pitch Difference - Width Premium - Width Difference - Seats Premium
Factors that are positively correlated with PriceRelative are Airline, Flight duration, IsInternational, PitchPremium, Pitch Difference, WidthPremium, Width Difference and that are negatively correlated with PriceRelative, PitchEconomy, WidthEconomy.
library(car)
scatterplotMatrix(Airlines[,c("PriceRelative","Airline","FlightDuration","IsInternational","PitchEconomy","PitchPremium","PitchDifference","WidthPremium","WidthDifference","SeatsPremium")],
spread=FALSE, smoother.args=list(lty=2),
main="Scatter Plot Matrix")## 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
# Articulate a Hypothesis that you could test using a Regression Model. Run T-Tests appropriate, to test your Hypotheses. Fit a Linear Regression Model using lm().
# Null Hypothesis- "The relative price does not depend on IsInternational variable.
# T-Test to check correlation between PriceRelative and Airline.
t.test(Airlines$PriceRelative ~ Airlines$IsInternational)##
## Welch Two Sample t-test
##
## data: Airlines$PriceRelative by Airlines$IsInternational
## t = -19.451, df = 446.12, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.4855215 -0.3964139
## sample estimates:
## mean in group Domestic mean in group International
## 0.0847500 0.5257177# As p-value=2.2e-16(<0.05).We can easily reject the null hypothesis for IsInternational column.Therefore There is a strong correlation between Relative price and IsInternational.
# Null Hypothesis- "The relative price depends on the Aircraft variable".
# T-Test to check correlation between PriceRelative and Aircraft.
t.test(Airlines$PriceRelative ~ Airlines$Aircraft)##
## Welch Two Sample t-test
##
## data: Airlines$PriceRelative by Airlines$Aircraft
## t = -2.6145, df = 363.72, p-value = 0.009306
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -0.18934647 -0.02678486
## sample estimates:
## mean in group AirBus mean in group Boeing
## 0.4147682 0.5228339# As p-value=0.009306(<0.05).We can easily reject the null hypothesis for Aircraft column.Therefore there is no correlation between Relative price and Aircraft.Regression Model:
The linear model will be:
relative_price = x0 + Airline* x1 + Flightduration * x2 + IsInternational * x3 + Pitch Economy * x4 + Pitch Premium * x5 + Pitch Difference * x6 + Width Premium * x7 + Width Difference * x8 + Seats Premium * x9
# We have variables line Airline and IsInternational which have unique values and We have to convert them into factos.
# converting into integers
Airlines$Airline[Airlines$Res == 0] <- 'AirFrance'
Airlines$Airline[Airlines$Res == 1] <- 'British'
Airlines$Airline[Airlines$Res == 2] <- 'Delta'
Airlines$Airline[Airlines$Res == 3] <- 'Jet'
Airlines$Airline[Airlines$Res == 4] <- 'Singapore'
Airlines$Airline[Airlines$Res == 5] <- 'Virgin'
# convert Airline into factor variable
Airlines$Airline<- factor(Airlines$Airline)
# converting into integers
Airlines$IsInternational[Airlines$Res == 0] <- 'Domestic'
Airlines$IsInternational[Airlines$Res == 1] <- 'International'
# convert IsInternational into factor variable
Airlines$IsInternational<- factor(Airlines$IsInternational)
Airlinemodel <- lm(PriceRelative ~Airline
+FlightDuration
+IsInternational
+PitchEconomy
+PitchPremium
+WidthEconomy
+WidthPremium
, data=Airlines)
# Summary of the model.
summary(Airlinemodel)##
## Call:
## lm(formula = PriceRelative ~ Airline + FlightDuration + IsInternational +
## PitchEconomy + PitchPremium + WidthEconomy + WidthPremium,
## data = Airlines)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.8159 -0.1933 -0.0549 0.1009 1.4689
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.904967 4.976180 -0.182 0.855775
## AirlineBritish 0.250299 0.159931 1.565 0.118283
## AirlineDelta 0.061917 0.354717 0.175 0.861510
## AirlineJet 0.534838 0.140825 3.798 0.000166 ***
## AirlineSingapore 0.322987 0.204182 1.582 0.114390
## AirlineVirgin 0.417100 0.326186 1.279 0.201661
## FlightDuration 0.036153 0.006173 5.856 9.2e-09 ***
## IsInternationalInternational -0.406100 0.399055 -1.018 0.309394
## PitchEconomy -0.055148 0.133621 -0.413 0.680012
## PitchPremium 0.085155 0.151506 0.562 0.574361
## WidthEconomy -0.073197 0.078098 -0.937 0.349140
## WidthPremium 0.052925 0.222769 0.238 0.812319
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.36 on 446 degrees of freedom
## Multiple R-squared: 0.3772, Adjusted R-squared: 0.3618
## F-statistic: 24.55 on 11 and 446 DF, p-value: < 2.2e-16# The coeffecients of the model.
Airlinemodel$coefficients## (Intercept) AirlineBritish
## -0.90496697 0.25029934
## AirlineDelta AirlineJet
## 0.06191693 0.53483769
## AirlineSingapore AirlineVirgin
## 0.32298704 0.41709999
## FlightDuration IsInternationalInternational
## 0.03615307 -0.40609991
## PitchEconomy PitchPremium
## -0.05514750 0.08515457
## WidthEconomy WidthPremium
## -0.07319684 0.05292480# The predicted model.
fitted(Airlinemodel)## 1 2 3 4 5
## 0.5964366234 0.5964366234 0.5964366234 0.5964366234 0.4485705758
## 6 7 8 9 10
## 0.4485705758 0.4485705758 0.3885564831 0.3885564831 0.5693218225
## 11 12 13 14 15
## 0.5693218225 0.5693218225 0.5693218225 0.5722140679 0.5722140679
## 16 17 18 19 20
## 0.5722140679 0.4847236437 0.4847236437 0.4847236437 0.3975947501
## 21 22 23 24 25
## 0.3975947501 0.3975947501 0.3943409740 0.3943409740 0.3943409740
## 26 27 28 29 30
## 0.4699008858 0.4699008858 0.4699008858 0.3310731052 0.3310731052
## 31 32 33 34 35
## 0.3310731052 0.2920277919 0.2920277919 0.2920277919 0.2920277919
## 36 37 38 39 40
## 0.6416279582 0.6416279582 0.6416279582 0.2920277919 0.2920277919
## 41 42 43 44 45
## 0.2920277919 0.2920277919 0.3491496392 0.3491496392 0.3491496392
## 46 47 48 49 50
## 0.4518243519 0.4518243519 0.4518243519 0.6145131573 0.6145131573
## 51 52 53 54 55
## 0.6145131573 0.3885564831 0.5541375340 0.5541375340 0.5541375340
## 56 57 58 59 60
## 0.3733721946 0.3733721946 0.3733721946 0.6054748903 0.5892060098
## 61 62 63 64 65
## 0.6054748903 0.7154363330 0.7154363330 0.7154363330 0.7154363330
## 66 67 68 69 70
## 0.7454433794 0.7454433794 0.7454433794 0.7454433794 0.6821755106
## 71 72 73 74 75
## 0.6821755106 0.6821755106 0.7063980661 0.0522112890 0.0619726173
## 76 77 78 79 80
## 0.0522112890 0.0504036356 0.0619726173 0.0410038379 0.0608880253
## 81 82 83 84 85
## 0.0608880253 0.4004869955 0.4004869955 0.4004869955 0.4004869955
## 86 87 88 89 90
## 0.4276017964 0.4276017964 0.4276017964 0.4276017964 0.9539544255
## 91 92 93 94 95
## 0.9539544255 0.9539544255 0.9539544255 0.9539544255 0.9539544255
## 96 97 98 99 100
## 0.9539544255 0.9539544255 0.0968725799 0.5570297794 0.5570297794
## 101 102 103 104 105
## 0.5570297794 0.5570297794 0.5331687546 0.5331687546 0.5331687546
## 106 107 108 109 110
## 0.5331687546 0.6264436697 0.6264436697 0.6264436697 0.5570297794
## 111 112 113 114 115
## 0.5570297794 0.5570297794 0.3010660589 0.2829895250 0.2829895250
## 116 117 118 119 120
## 0.2406904356 0.2710590126 0.2710590126 0.2829895250 0.2497287025
## 121 122 123 124 125
## 0.2406904356 0.2406904356 0.2406904356 0.2710590126 0.2197216562
## 126 127 128 129 130
## 0.2829895250 0.3162503474 0.2406904356 0.3162503474 0.2197216562
## 131 132 133 134 135
## 0.2406904356 0.2829895250 0.3010660589 0.2558747241 0.3162503474
## 136 137 138 139 140
## 0.1987528768 0.3010660589 0.1987528768 0.2406904356 0.2197216562
## 141 142 143 144 145
## 0.2197216562 0.1987528768 0.1987528768 0.1987528768 0.2829895250
## 146 147 148 149 150
## 0.2558747241 0.2016451223 0.2016451223 0.1987528768 0.2406904356
## 151 152 153 154 155
## 0.2016451223 0.1642858197 0.1707933719 0.1642858197 0.1707933719
## 156 157 158 159 160
## 0.8329338036 0.8329338036 0.8329338036 0.8329338036 0.8629408499
## 161 162 163 164 165
## 0.8629408499 0.8629408499 0.8629408499 0.7844886927 0.7844886927
## 166 167 168 169 170
## 0.7844886927 0.7844886927 0.8177495151 0.8177495151 0.8177495151
## 171 172 173 174 175
## 0.8177495151 0.8025652266 0.8810173839 0.8148572697 0.8148572697
## 176 177 178 179 180
## 0.8148572697 0.8148572697 0.8810173839 0.8358260490 0.7031442900
## 181 182 183 184 185
## 0.7031442900 0.7031442900 0.7031442900 0.8810173839 0.3721837886
## 186 187 188 189 190
## 0.3721837886 0.4144828780 0.4144828780 0.4144828780 0.3721837886
## 191 192 193 194 195
## 0.6760294891 0.6760294891 0.6760294891 0.6760294891 0.6640989767
## 196 197 198 199 200
## 0.6640989767 0.6640989767 0.6640989767 0.8025652266 0.8025652266
## 201 202 203 204 205
## 0.8025652266 0.8358260490 0.8358260490 0.6941060230 0.6941060230
## 206 207 208 209 210
## 0.6941060230 0.6941060230 0.6821755106 0.7063980661 0.7063980661
## 211 212 213 214 215
## 0.7063980661 0.1915688465 0.1915688465 0.1915688465 0.1492697571
## 216 217 218 219 220
## 0.1492697571 0.1492697571 0.1492697571 0.1492697571 0.1492697571
## 221 222 223 224 225
## 0.1492697571 0.1492697571 0.1131166892 0.1131166892 0.1131166892
## 226 227 228 229 230
## 0.0950401553 0.0950401553 0.1702385364 0.1702385364 0.1702385364
## 231 232 233 234 235
## 0.1402314901 0.1799998648 0.1799998648 0.1825305795 0.1825305795
## 236 237 238 239 240
## 0.1792768034 0.1792768034 0.1825305795 0.1825305795 0.5299149785
## 241 242 243 244 245
## 0.5299149785 0.5299149785 0.5512452885 0.5512452885 0.5512452885
## 246 247 248 249 250
## 0.5118384445 0.5118384445 0.5118384445 0.5118384445 0.4637548643
## 251 252 253 254 255
## 0.4637548643 0.4637548643 0.5660680463 0.5660680463 0.5660680463
## 256 257 258 259 260
## 0.5660680463 0.4908696652 0.4908696652 0.4908696652 0.4756853767
## 261 262 263 264 265
## 0.4666471097 0.4666471097 0.4666471097 0.4156712840 0.4156712840
## 266 267 268 269 270
## 0.4156712840 0.4756853767 0.4095252625 0.4095252625 0.4095252625
## 271 272 273 274 275
## 0.5660680463 0.5660680463 0.5660680463 0.5660680463 0.4756853767
## 276 277 278 279 280
## 0.5541375340 0.5541375340 0.5541375340 0.4095252625 0.5541375340
## 281 282 283 284 285
## 0.0975956412 0.0965110492 0.1102492150 0.1109722764 0.1113338070
## 286 287 288 289 290
## 0.1679274299 0.1679274299 0.1679274299 0.1004878867 0.0965110492
## 291 292 293 294 295
## 0.1030186014 0.1679274299 0.0968725799 0.1019340094 0.0419199371
## 296 297 298 299 300
## 0.0133589931 0.0245664442 0.0650579005 0.0650579005 0.1102492150
## 301 302 303 304 305
## 0.0762653515 0.0082975636 0.0090206250 -0.0003791727 0.0632502471
## 306 307 308 309 310
## 0.0079360329 -0.0003791727 0.7264065414 0.7264065414 0.7264065414
## 311 312 313 314 315
## 0.7050762314 0.7050762314 0.7264065414 0.7050762314 0.6537188697
## 316 317 318 319 320
## 0.6537188697 0.6537188697 0.6537188697 0.5994892679 0.5994892679
## 321 322 323 324 325
## 0.5994892679 0.5423674207 0.5423674207 0.5423674207 0.6808336706
## 326 327 328 329 330
## 0.6808336706 0.6808336706 0.5000683313 0.5000683313 0.5000683313
## 331 332 333 334 335
## 0.2892959456 0.2892959456 0.2892959456 0.2892959456 0.6117813110
## 336 337 338 339 340
## 0.6117813110 0.6117813110 0.6117813110 0.2224666002 0.2224666002
## 341 342 343 344 345
## 0.2224666002 0.1924595538 0.1682369984 0.2376508887 0.2376508887
## 346 347 348 349 350
## 0.2376508887 0.1982440447 0.1682369984 0.1682369984 0.2647656896
## 351 352 353 354 355
## 0.2647656896 0.2647656896 0.2647656896 0.2014978208 0.2014978208
## 356 357 358 359 360
## 0.2014978208 0.2043900662 0.2615119135 0.2615119135 0.2615119135
## 361 362 363 364 365
## 0.3461100923 0.3461100923 0.3518945831 0.3518945831 0.3518945831
## 366 367 368 369 370
## 0.3518945831 0.6535584706 0.6535584706 0.6535584706 0.6354819366
## 371 372 373 374 375
## 0.6354819366 0.6354819366 0.4756853767 0.4756853767 0.4756853767
## 376 377 378 379 380
## 0.4999079322 0.4999079322 0.4999079322 0.9601004470 0.9601004470
## 381 382 383 384 385
## 0.9601004470 0.9601004470 0.9601004470 0.9929997388 0.9929997388
## 386 387 388 389 390
## 0.9901074933 0.9329856461 0.9329856461 0.9329856461 0.9387701370
## 391 392 393 394 395
## 0.9387701370 0.9387701370 0.9929997388 0.9601004470 0.9901074933
## 396 397 398 399 400
## 0.9929997388 0.9929997388 0.9329856461 0.9387701370 0.9991457603
## 401 402 403 404 405
## 0.9991457603 0.9991457603 0.9991457603 0.9601004470 0.9601004470
## 406 407 408 409 410
## 0.1711292438 0.1711292438 0.1711292438 0.1711292438 0.6327500903
## 411 412 413 414 415
## 0.6327500903 0.6327500903 0.6327500903 0.3735325938 0.3735325938
## 416 417 418 419 420
## 0.3735325938 0.3735325938 0.6085275349 0.6085275349 0.6085275349
## 421 422 423 424 425
## 0.6085275349 0.3858246368 0.3858246368 0.3858246368 0.3858246368
## 426 427 428 429 430
## 0.3181045840 0.3181045840 0.1192627108 0.1192627108 0.2335064052
## 431 432 433 434 435
## 0.2335064052 0.2335064052 0.1521620025 0.1554157786 0.1554157786
## 436 437 438 439 440
## 0.1554157786 0.2638749822 0.2638749822 0.3181045840 1.0472293406
## 441 442 443 444 445
## 0.9568466709 0.9568466709 1.0472293406 1.0472293406 1.0472293406
## 446 447 448 449 450
## 1.0472293406 1.0472293406 0.9568466709 0.9358778915 0.9358778915
## 451 452 453 454 455
## 0.9358778915 0.9568466709 0.9358778915 0.9358778915 0.9358778915
## 456 457 458
## 0.9358778915 0.9601004470 0.9358778915Inferences:
The Relative price difference between Economy and Premium Economy tickets is due to the following Factors as an outcome of the data analysis of Airline dataset:
a} Airline company: Lowest relative price difference in Airlines being AirFrance and Delta and highest being Jet Airlines.
b} International or Domestic flight: Domestic flights having low Difference in prices of tickets than International flights.
c} Flight duration: Increase in Flight Duration, increases the Relative price difference.
d} PitchEconomy: As the pitch of Economy class increases, the Relative Price reduces.
e} PitchPremium: As the pitch of Premium Economy class increases, the Relative Price difference increases.
f} PitchDifference: As difference in Pitch increases, the Relative Price difference increases.
g} width Difference: As difference in Width increases,the Relative Price difference increases.