“Airline Passenger Satisfaction Analysis”

Introduction

Airline passenger satisfaction plays a significant role in the success of an airline, as satisfied customers are more likely to become loyal customers and promote positive word-of-mouth recommendations. With the increasing competition in the airline industry, understanding the factors that influence passenger satisfaction is crucial for maintaining a competitive edge. By analyzing data related to various aspects of a passenger’s journey, from the booking process to in-flight services, we can gain insights into how airlines can improve customer experience and ensure higher satisfaction levels.

Problem statement

In today’s highly competitive airline industry, airlines must prioritize customer satisfaction to maintain loyalty and stay profitable. However, many airlines struggle to identify which specific factors contribute the most to overall satisfaction and how they can improve the customer experience. Examining various customer related factors, including service factors such as ease of online booking, flight distance, in-flight services to determine their impact on passenger satisfaction will lead to understanding these relationships and help airlines enhance their services and ensure higher satisfaction rates.

Project objective

The primary objective of this project is to use data analysis techniques to determine which factors are most strongly correlated with airline passenger satisfaction. Specifically, we will assess whether variables such as in flight wifi service, departure and arrival time, cleanliness and onboard services affect overall satisfaction. By identifying the key drivers of customer satisfaction, we aim to provide actionable recommendations for airlines to improve their services and enhance customer loyalty.

Method(s)

Load necessary libraries

library(readxl)
library(ggplot2)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(pscl)

## Classes and Methods for R originally developed in the
## Political Science Computational Laboratory
## Department of Political Science
## Stanford University (2002-2015),
## by and under the direction of Simon Jackman.
## hurdle and zeroinfl functions by Achim Zeileis.

Load the dataset

data <- read_excel("airline_passenger_satisfaction.xlsx")

Describing the variables

Dependent Variable:
Satisfaction ("Satisfied", "Neutral or Dissatisfied")
Converted to Binary:
1: Satisfied
0: Neutral or Dissatisfied

Independent Variables:
Departure.and.Arrival.Time.Convenience, Ease.of.Online.Booking, Check.in.Service, In-Flight Service and Comfort, On.board.Service, Online.Boarding, Seat.Comfort, Leg.Room.Service, Cleanliness, Food.and.Drink, In.flight.Service, In.flight.Wifi.Service,In.flight.Entertainment, Baggage.Handling, Gate.Location
(Excluded delays and other obvious variables from factors that will relate to dissatisfaction.)

Converting columns to factors to make the data clear and creat bar chart: R will treat these score variables as categorical rather than numeric data.

data$Satisfaction <- as.factor(data$Satisfaction)
data$Departure.and.Arrival.Time.Convenience<- as.factor(data$Departure.and.Arrival.Time.Convenience)
data$Ease.of.Online.Booking <- as.factor(data$Ease.of.Online.Booking)
data$Check.in.Service <- as.factor(data$Check.in.Service)
data$Online.Boarding <- as.factor(data$Ease.of.Online.Booking)
data$On.board.Service <- as.factor(data$On.board.Service)
data$Seat.Comfort <- as.factor(data$Seat.Comfort)
data$Leg.Room.Service <- as.factor(data$Leg.Room.Service)
data$Food.and.Drink <- as.factor(data$Food.and.Drink)
data$In.flight.Service <- as.factor(data$In.flight.Service)
data$In.flight.Wifi.Service <- as.factor(data$In.flight.Wifi.Service)
data$In.flight.Entertainment<- as.factor(data$In.flight.Entertainment)
data$Baggage.Handling <- as.factor(data$Baggage.Handling)
data$Gate.Location <- as.factor(data$Gate.Location)
data$Cleanliness <- as.factor(data$Cleanliness)

Sumarry stats

summary(data)

##        ID            Gender               Age        Customer Type     
##  Min.   :     1   Length:129880      Min.   : 7.00   Length:129880     
##  1st Qu.: 32471   Class :character   1st Qu.:27.00   Class :character  
##  Median : 64940   Mode  :character   Median :40.00   Mode  :character  
##  Mean   : 64940                      Mean   :39.43                     
##  3rd Qu.: 97410                      3rd Qu.:51.00                     
##  Max.   :129880                      Max.   :85.00                     
##                                                                        
##  Type of Travel        Class           Flight Distance Departure Delay  
##  Length:129880      Length:129880      Min.   :  31    Min.   :   0.00  
##  Class :character   Class :character   1st Qu.: 414    1st Qu.:   0.00  
##  Mode  :character   Mode  :character   Median : 844    Median :   0.00  
##                                        Mean   :1190    Mean   :  14.71  
##                                        3rd Qu.:1744    3rd Qu.:  12.00  
##                                        Max.   :4983    Max.   :1592.00  
##                                                                         
##  Arrival Delay     Departure.and.Arrival.Time.Convenience
##  Min.   :   0.00   0: 6681                               
##  1st Qu.:   0.00   1:19409                               
##  Median :   0.00   2:21534                               
##  Mean   :  15.09   3:22378                               
##  3rd Qu.:  13.00   4:31880                               
##  Max.   :1584.00   5:27998                               
##  NA's   :393                                             
##  Ease.of.Online.Booking Check.in.Service Online.Boarding Gate.Location
##  0: 5682                0:    1          0: 5682         0:    1      
##  1:21886                1:16108          1:21886         1:21991      
##  2:30051                2:16102          2:30051         2:24296      
##  3:30393                3:35453          3:30393         3:35717      
##  4:24444                4:36333          4:24444         4:30466      
##  5:17424                5:25883          5:17424         5:17409      
##                                                                       
##  On.board.Service Seat.Comfort Leg.Room.Service Cleanliness Food.and.Drink
##  0:    5          0:    1      0:  598          0:   14     0:  132       
##  1:14787          1:15108      1:12895          1:16729     1:16051       
##  2:18351          2:18529      2:24540          2:20113     2:27383       
##  3:28542          3:23328      3:25056          3:30639     3:27794       
##  4:38703          4:39756      4:35886          4:33969     4:30563       
##  5:29492          5:33158      5:30905          5:28416     5:27957       
##                                                                           
##  In.flight.Service In.flight.Wifi.Service In.flight.Entertainment
##  0:    5           0: 3916                0:   18                
##  1: 8862           1:22328                1:15675                
##  2:14308           2:32320                2:21968                
##  3:25316           3:32185                3:23884                
##  4:47323           4:24775                4:36791                
##  5:34066           5:14356                5:31544                
##                                                                  
##  Baggage.Handling                  Satisfaction  
##  1: 9028          Neutral or Dissatisfied:73452  
##  2:14362          Satisfied              :56428  
##  3:25851                                         
##  4:46761                                         
##  5:33878                                         
##                                                  
## 

Visualization: Satisfaction factors of categorical data (5 scores)

Filter data for satisfied passengers only

satisfied_data <- subset(data, Satisfaction == "Satisfied")

Create a bar chart of satisfaction scores for 14 factors

#Cleanliness
ggplot(data = satisfied_data, aes(x = factor(Cleanliness))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "Cleanliness Scores for Satisfied Passengers",
       x = "Cleanliness Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#Departure.and.Arrival.Time.Convenience
ggplot(data = satisfied_data, aes(x = factor(Departure.and.Arrival.Time.Convenience))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "Departure.and.Arrival.Time.Convenience Scores for Satisfied Passengers",
       x = "Departure.and.Arrival.Time.Convenience",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#Ease.of.Online.Booking
ggplot(data = satisfied_data, aes(x = factor(Ease.of.Online.Booking))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "Ease.of.Online.Booking Scores for Satisfied Passengers",
       x = "Ease.of.Online.Booking Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#Check.in.Service
ggplot(data = satisfied_data, aes(x = factor(Check.in.Service))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "Check.in.Service Scores for Satisfied Passengers",
       x = "Check.in.Service Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#Online.Boarding
ggplot(data = satisfied_data, aes(x = factor(Online.Boarding))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "Online.Boarding Scores for Satisfied Passengers",
       x = "Online.Boarding Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#On.board.Service
ggplot(data = satisfied_data, aes(x = factor(On.board.Service))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "On.board.Service Scores for Satisfied Passengers",
       x = "On.board.Service Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#Seat.Comfort
ggplot(data = satisfied_data, aes(x = factor(Seat.Comfort))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "Seat.Comfort Scores for Satisfied Passengers",
       x = "Seat.Comfort Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#Leg.Room.Service
ggplot(data = satisfied_data, aes(x = factor(Leg.Room.Service))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "Leg.Room.Service Scores for Satisfied Passengers",
       x = "Leg.Room.Service Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#Food.and.Drink
ggplot(data = satisfied_data, aes(x = factor(Food.and.Drink))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "Food.and.Drink Scores for Satisfied Passengers",
       x = "Food.and.Drink Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#In.flight.Service
ggplot(data = satisfied_data, aes(x = factor(In.flight.Service))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "In.flight.Service Scores for Satisfied Passengers",
       x = "In.flight.Service Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#In.flight.Wifi.Service
ggplot(data = satisfied_data, aes(x = factor(In.flight.Wifi.Service))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "In.flight.Wifi.Service Scores for Satisfied Passengers",
       x = "In.flight.Wifi.Service Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#In.flight.Entertainment
ggplot(data = satisfied_data, aes(x = factor(In.flight.Entertainment))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "In.flight.Entertainment Scores for Satisfied Passengers",
       x = "In.flight.Entertainment Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#Baggage.Handling 
ggplot(data = satisfied_data, aes(x = factor(Baggage.Handling))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "Baggage.Handling Scores for Satisfied Passengers",
       x = "Baggage.Handling Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

#Gate.Location 
ggplot(data = satisfied_data, aes(x = factor(Gate.Location))) +
  geom_bar(stat = "count", width = 0.5, fill = 'steelblue') +
  labs(title = "Gate.Location Scores for Satisfied Passengers",
       x = "Gate.Location Score",
       y = "Count") +
  theme_minimal() +
  coord_flip()

Interpretation: Factors that have high proportion of scores below 2, such as 'Departure.and.Arrival.Time.Convenience', 'Ease.of.Online.Booking', 'Online.Boarding', 'Gate.Location' are better to have improvement.

Convert Satisfaction to binary: 1 for “Satisfied”, 0 for “Neutral or Dissatisfied”

data$Satisfaction_Binary <- ifelse(data$Satisfaction == "Satisfied", 1, 0)

Convert factor columns to numeric

data$Departure.and.Arrival.Time.Convenience <- as.numeric(as.character(data$Departure.and.Arrival.Time.Convenience))
data$Ease.of.Online.Booking <- as.numeric(as.character(data$Ease.of.Online.Booking))
data$Check.in.Service <- as.numeric(as.character(data$Check.in.Service))
data$On.board.Service <- as.numeric(as.character(data$On.board.Service))
data$Online.Boarding <- as.numeric(as.character(data$Online.Boarding))
data$Seat.Comfort <- as.numeric(as.character(data$Seat.Comfort))
data$Leg.Room.Service <- as.numeric(as.character(data$Leg.Room.Service))
data$Cleanliness <- as.numeric(as.character(data$Cleanliness))
data$Food.and.Drink <- as.numeric(as.character(data$Food.and.Drink))
data$In.flight.Service <- as.numeric(as.character(data$In.flight.Service))
data$In.flight.Wifi.Service <- as.numeric(as.character(data$In.flight.Wifi.Service))
data$In.flight.Entertainment <- as.numeric(as.character(data$In.flight.Entertainment))
data$Baggage.Handling <- as.numeric(as.character(data$Baggage.Handling))

Calculate correlation between Satisfaction_Binary and Other factors

corr<-cor(data[, c("Departure.and.Arrival.Time.Convenience", "Ease.of.Online.Booking", "Check.in.Service", "On.board.Service","Online.Boarding","Seat.Comfort", "Leg.Room.Service", "Cleanliness", "Food.and.Drink", "In.flight.Service", "In.flight.Wifi.Service", "In.flight.Entertainment", "Baggage.Handling","Satisfaction_Binary")])
corr

##                                        Departure.and.Arrival.Time.Convenience
## Departure.and.Arrival.Time.Convenience                           1.0000000000
## Ease.of.Online.Booking                                           0.4376196545
## Check.in.Service                                                 0.0911317589
## On.board.Service                                                 0.0672969787
## Online.Boarding                                                  0.4376196545
## Seat.Comfort                                                     0.0086664448
## Leg.Room.Service                                                 0.0106171078
## Cleanliness                                                      0.0098620846
## Food.and.Drink                                                   0.0006866832
## In.flight.Service                                                0.0721948030
## In.flight.Wifi.Service                                           0.3449151814
## In.flight.Entertainment                                         -0.0083800141
## Baggage.Handling                                                 0.0708330359
## Satisfaction_Binary                                             -0.0542697105
##                                        Ease.of.Online.Booking Check.in.Service
## Departure.and.Arrival.Time.Convenience            0.437619655      0.091131759
## Ease.of.Online.Booking                            1.000000000      0.008819308
## Check.in.Service                                  0.008819308      1.000000000
## On.board.Service                                  0.039064190      0.244618669
## Online.Boarding                                   1.000000000      0.008819308
## Seat.Comfort                                      0.028560733      0.189979117
## Leg.Room.Service                                  0.109449655      0.152693216
## Cleanliness                                       0.015124786      0.176658031
## Food.and.Drink                                    0.030513982      0.085197877
## In.flight.Service                                 0.035372567      0.237601243
## In.flight.Wifi.Service                            0.714806849      0.043762366
## In.flight.Entertainment                           0.046563505      0.119554033
## Baggage.Handling                                  0.039148282      0.234503128
## Satisfaction_Binary                               0.168877139      0.237252360
##                                        On.board.Service Online.Boarding
## Departure.and.Arrival.Time.Convenience       0.06729698     0.437619655
## Ease.of.Online.Booking                       0.03906419     1.000000000
## Check.in.Service                             0.24461867     0.008819308
## On.board.Service                             1.00000000     0.039064190
## Online.Boarding                              0.03906419     1.000000000
## Seat.Comfort                                 0.13054488     0.028560733
## Leg.Room.Service                             0.35772132     0.109449655
## Cleanliness                                  0.12208376     0.015124786
## Food.and.Drink                               0.05740401     0.030513982
## In.flight.Service                            0.55156883     0.035372567
## In.flight.Wifi.Service                       0.11992768     0.714806849
## In.flight.Entertainment                      0.41857358     0.046563505
## Baggage.Handling                             0.52029553     0.039148282
## Satisfaction_Binary                          0.32220482     0.168877139
##                                        Seat.Comfort Leg.Room.Service
## Departure.and.Arrival.Time.Convenience  0.008666445       0.01061711
## Ease.of.Online.Booking                  0.028560733       0.10944966
## Check.in.Service                        0.189979117       0.15269322
## On.board.Service                        0.130544875       0.35772132
## Online.Boarding                         0.028560733       0.10944966
## Seat.Comfort                            1.000000000       0.10427240
## Leg.Room.Service                        0.104272400       1.00000000
## Cleanliness                             0.679613003       0.09669472
## Food.and.Drink                          0.575846177       0.03317279
## In.flight.Service                       0.068842149       0.36956948
## In.flight.Wifi.Service                  0.121513245       0.16031696
## In.flight.Entertainment                 0.611836657       0.30039744
## Baggage.Handling                        0.074619552       0.37145468
## Satisfaction_Binary                     0.348829346       0.31242382
##                                        Cleanliness Food.and.Drink
## Departure.and.Arrival.Time.Convenience 0.009862085   0.0006866832
## Ease.of.Online.Booking                 0.015124786   0.0305139818
## Check.in.Service                       0.176658031   0.0851978765
## On.board.Service                       0.122083757   0.0574040099
## Online.Boarding                        0.015124786   0.0305139818
## Seat.Comfort                           0.679613003   0.5758461771
## Leg.Room.Service                       0.096694724   0.0331727940
## Cleanliness                            1.000000000   0.6580539298
## Food.and.Drink                         0.658053930   1.0000000000
## In.flight.Service                      0.090355980   0.0352096628
## In.flight.Wifi.Service                 0.131299526   0.1322138724
## In.flight.Entertainment                0.692510538   0.6234609372
## Baggage.Handling                       0.097071490   0.0353207442
## Satisfaction_Binary                    0.307034671   0.2113402076
##                                        In.flight.Service In.flight.Wifi.Service
## Departure.and.Arrival.Time.Convenience        0.07219480             0.34491518
## Ease.of.Online.Booking                        0.03537257             0.71480685
## Check.in.Service                              0.23760124             0.04376237
## On.board.Service                              0.55156883             0.11992768
## Online.Boarding                               0.03537257             0.71480685
## Seat.Comfort                                  0.06884215             0.12151324
## Leg.Room.Service                              0.36956948             0.16031696
## Cleanliness                                   0.09035598             0.13129953
## Food.and.Drink                                0.03520966             0.13221387
## In.flight.Service                             1.00000000             0.11002855
## In.flight.Wifi.Service                        0.11002855             1.00000000
## In.flight.Entertainment                       0.40609361             0.20780165
## Baggage.Handling                              0.62923720             0.12037590
## Satisfaction_Binary                           0.24491784             0.28346023
##                                        In.flight.Entertainment Baggage.Handling
## Departure.and.Arrival.Time.Convenience            -0.008380014       0.07083304
## Ease.of.Online.Booking                             0.046563505       0.03914828
## Check.in.Service                                   0.119554033       0.23450313
## On.board.Service                                   0.418573575       0.52029553
## Online.Boarding                                    0.046563505       0.03914828
## Seat.Comfort                                       0.611836657       0.07461955
## Leg.Room.Service                                   0.300397442       0.37145468
## Cleanliness                                        0.692510538       0.09707149
## Food.and.Drink                                     0.623460937       0.03532074
## In.flight.Service                                  0.406093608       0.62923720
## In.flight.Wifi.Service                             0.207801648       0.12037590
## In.flight.Entertainment                            1.000000000       0.37912276
## Baggage.Handling                                   0.379122757       1.00000000
## Satisfaction_Binary                                0.398233651       0.24867992
##                                        Satisfaction_Binary
## Departure.and.Arrival.Time.Convenience         -0.05426971
## Ease.of.Online.Booking                          0.16887714
## Check.in.Service                                0.23725236
## On.board.Service                                0.32220482
## Online.Boarding                                 0.16887714
## Seat.Comfort                                    0.34882935
## Leg.Room.Service                                0.31242382
## Cleanliness                                     0.30703467
## Food.and.Drink                                  0.21134021
## In.flight.Service                               0.24491784
## In.flight.Wifi.Service                          0.28346023
## In.flight.Entertainment                         0.39823365
## Baggage.Handling                                0.24867992
## Satisfaction_Binary                             1.00000000

Interpretation: In.flight.Entertainment (0.40), Seat.Comfort (0.35), Leg.Room.Service (0.31), On.board.Service (0.32), Cleanliness (0.31) are the top 5 related factores.

Logistic regression model

model <- glm(Satisfaction_Binary ~ In.flight.Entertainment + Seat.Comfort + 
                 Leg.Room.Service + On.board.Service + Cleanliness, 
                family = "binomial", data = data,)
summary(model)

## 
## Call:
## glm(formula = Satisfaction_Binary ~ In.flight.Entertainment + 
##     Seat.Comfort + Leg.Room.Service + On.board.Service + Cleanliness, 
##     family = "binomial", data = data)
## 
## Coefficients:
##                          Estimate Std. Error z value Pr(>|z|)    
## (Intercept)             -5.240091   0.033127 -158.18   <2e-16 ***
## In.flight.Entertainment  0.185839   0.007925   23.45   <2e-16 ***
## Seat.Comfort             0.392795   0.006979   56.29   <2e-16 ***
## Leg.Room.Service         0.377576   0.005475   68.96   <2e-16 ***
## On.board.Service         0.358746   0.006020   59.59   <2e-16 ***
## Cleanliness              0.124844   0.007776   16.06   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## (Dispersion parameter for binomial family taken to be 1)
## 
##     Null deviance: 177814  on 129879  degrees of freedom
## Residual deviance: 141796  on 129874  degrees of freedom
## AIC: 141808
## 
## Number of Fisher Scoring iterations: 4

Interpretation: The estimated logistic regression equation is E(y) = e-5.24+0.19x1+0.39x2+0.38x3+0.36x4+0.12x5/1+e-5.24+0.19x1+0.39x2+0.38x3+0.36x4+0.12x5. 

Calculate Odds Ratios

odds_ratios <- exp(coefficients(model))
odds_ratios

##             (Intercept) In.flight.Entertainment            Seat.Comfort 
##             0.005299773             1.204228185             1.481114008 
##        Leg.Room.Service        On.board.Service             Cleanliness 
##             1.458744715             1.431532725             1.132971908

Interpretation: 
For each one-unit increase in the in flight entertainment, the odds of being satisfied increase by 20.42%. 
For each one-unit increase in the seat comfort, the odds of being satisfied increase by 48.11%.
For each one-unit increase in the leg room service, the odds of being satisfied increase by 45.87%.
For each one-unit increase in the on board service, the odds of being satisfied increase by 43.15%.
For each one-unit increase in the cleanliness, the odds of being satisfied increase by 13.30%.

Calculate McFadden’s R-squared

pR2(model)

## fitting null model for pseudo-r2

##           llh       llhNull            G2      McFadden          r2ML 
## -7.089797e+04 -8.890703e+04  3.601812e+04  2.025606e-01  2.421869e-01 
##          r2CU 
##  3.247974e-01

Interpretation: McFadden's R-squared value is 0.20, which indicates a moderate to good model fit in the context of logistic regression. 

Conclusion

Based on the analysis, the factors contributing most to passenger satisfaction were identified using correlation analysis and logistic regression. The following conclusions can be drawn:

Top drivers of satisfaction:
In-flight Entertainment: Passengers highly value the availability and quality of entertainment onboard, as shown by the significant odds ratio of 1.20 and its strong correlation with satisfaction (0.40).
Seat Comfort: Comfort during the flight plays a major role, with an odds ratio of 1.48 and a correlation of 0.35.
Leg Room Service: Enough legroom contributes significantly to satisfaction, with an odds ratio of 1.46 and a correlation of 0.31.
Onboard Service: Attentive and professional service from cabin staff is critical, with an odds ratio of 1.43 and a correlation of 0.32.
Cleanliness: A clean environment enhances passenger experience, as indicated by its odds ratio of 1.13 and a correlation of 0.31.

All independent variables (In flight Entertainment, Seat Comfort, Leg Room Service, On Board Service, Cleanliness) have p-values that are less than the significance level (0.05). 
This indicates they are statistically significant predictors of satisfaction.
Positive coefficients for all variables suggest that improvements in these factors contribute positively to customer satisfaction.

Model Performance:
The logistic regression model fits moderately well, as evidenced by McFadden's R-squared value of 0.20. This suggests the model explains 20% of the variance in satisfaction, a reasonable fit in customer satisfaction studies.

Recommendations

To improve customer satisfaction and loyalty, the airline should focus on the following areas:

1. Enhance In-flight Entertainment Systems
Provide a diverse selection of entertainment options (e.g., movies, TV shows, music, and games) according to different passenger preferences.
Ensure the systems are user-friendly and functional throughout the flight.
Regularly update content to include trending and seasonal options.
2. Improve Seat Comfort and Legroom
Upgrade seats with better designs to enhance comfort, especially for long distance flights.
Offer optional upgrades for passengers seeking additional legroom (e.g., premium economy or extra-legroom seats).
Provide adjustable headrests and lower back support for maximum comfort.
3. Focus on Onboard Service Excellence
Train cabin crew to deliver personalized, professional, and attentive service.
Implement feedback systems to evaluate and improve service delivery.
Enhance amenities like welcome drinks, blankets, and small treats to create a more enjoyable experience.