Predicting Airline Passengers’ Satisfaction

Material and Methodology

A secondary data-set from the Kaggle data platform was used to predict airline passengers’ satisfaction using machine algorithms Random Forest classifier the Datasets were partitioned into training and testing, 70% of the data was retained as a training set while 30% was considered as a testing set.

Read Data File

library(readr)
Capd <- read_csv("Capd.csv")

In this stage the data were reprocessed

# Capd %>% na.omit() # Removing missing values in the dataset 
# 
# # Converting data types using the following methods below
# Capd$Gender <- as.factor(Capd$Gender)
# Capd$customer_type <-  as.factor(Capd$customer_type)
# Capd$type_of_travel <- as.factor(Capd$type_of_travel)
# Capd$customer_class <- as.factor(Capd$customer_class)
# Capd$flight_distance <- as.integer (Capd$flight_distance)
# Capd$inflight_wifi_service <- as.factor(Capd$inflight_wifi_service)
# Capd$ease_of_online_booking <- as.factor(Capd$ease_of_online_booking)
# Capd$gate_location <- as.factor(Capd$food_and_drink)
# Capd$food_and_drink <- as.factor(Capd$food_and_drink)
# Capd$online_boarding <- as.factor(Capd$online_boarding)
# Capd$seat_comfort <- as.factor(Capd$seat_comfort)
# Capd$inflight_entertainment <- as.factor(Capd$inflight_entertainment)
# Capd$onboard_service <- as.factor(Capd$onboard_service)
# Capd$leg_room_service <- as.factor(Capd$leg_room_service)
# Capd$baggage_handling <- as.factor(Capd$baggage_handling)
# Capd$checkin_service <- as.factor(Capd$checkin_service)
# Capd$inflight_service <- as.factor(Capd$inflight_service)
# Capd$cleanliness <- as.integer (Capd$departure_delay_in_minutes)
# Capd$departure_delay_in_minutes <- as.integer(Capd$departure_delay_in_minutes)
# Capd$arrival_delay_in_minutes <- as.integer(Capd$arrival_delay_in_minutes)
# Capd$satisfaction <- as.factor(Capd$satisfaction)

## Diagram presentation

Table 1: Demo-graphical Characteristics of study participants

Characteristic N = 129,8801
Gender
    Female 65,899 (51%)
    Male 63,981 (49%)
customer_type
    disloyal Customer 23,780 (18%)
    Loyal Customer 106,100 (82%)
customer_class
    Business 62,160 (48%)
    Eco 58,309 (45%)
    Eco Plus 9,411 (7.2%)
1 n (%) 
Capd %>% 
  select(age,flight_distance,cleanliness,departure_delay_in_minutes,arrival_delay_in_minutes) %>% 
na.omit() %>%  report_parameters() 
  - age: n = 129487, Mean = 39.43, SD = 15.12, Median = 40.00, MAD = 17.79, range: [7, 85], Skewness = -3.38e-03, Kurtosis = -0.72, 0% missing
  - flight_distance: n = 129487, Mean = 1190.21, SD = 997.56, Median = 844.00, MAD = 767.99, range: [31, 4983], Skewness = 1.11, Kurtosis = 0.27, 0% missing
  - cleanliness: n = 129487, Mean = 3.29, SD = 1.31, Median = 3.00, MAD = 1.48, range: [0, 5], Skewness = -0.30, Kurtosis = -1.01, 0% missing
  - departure_delay_in_minutes: n = 129487, Mean = 14.64, SD = 37.93, Median = 0.00, MAD = 0.00, range: [0, 1592], Skewness = 6.85, Kurtosis = 101.88, 0% missing
  - arrival_delay_in_minutes: n = 129487, Mean = 15.09, SD = 38.47, Median = 0.00, MAD = 0.00, range: [0, 1584], Skewness = 6.67, Kurtosis = 95.12, 0% missing
tab_xtab(var.row = Capd$Gender, 
         var.col = Capd$customer_type,
         show.row.prc = T)
Gender customer_type Total
disloyal Customer Loyal Customer
Female 12843
19.5 % 		53056
80.5 % 		65899
100 %
Male 10937
17.1 % 		53044
82.9 % 		63981
100 %
Total 23780
18.3 % 		106100
81.7 % 		129880
100 %
χ2=124.313 · df=1 · φ=0.031 · p=0.000 
tab_xtab(var.row = Capd$Gender, 
         var.col = Capd$type_of_travel,
         show.row.prc = T)
Gender type_of_travel Total
Business travel Personal Travel
Female 45794
69.5 % 		20105
30.5 % 		65899
100 %
Male 43899
68.6 % 		20082
31.4 % 		63981
100 %
Total 89693
69.1 % 		40187
30.9 % 		129880
100 %
χ2=11.687 · df=1 · φ=0.010 · p=0.001 
tab_xtab(var.row = Capd$Gender, 
         var.col = Capd$customer_class,
         show.row.prc = T)
Gender customer_class Total
Business Eco Eco Plus
Female 31263
47.4 % 		29670
45 % 		4966
7.5 % 		65899
100 %
Male 30897
48.3 % 		28639
44.8 % 		4445
6.9 % 		63981
100 %
Total 62160
47.9 % 		58309
44.9 % 		9411
7.2 % 		129880
100 %
χ2=20.908 · df=2 · Cramer's V=0.013 · p=0.000

Feature Selection

#FS <- Boruta(satisfaction~., data = Capd, doTrace =2)

Data Partition in to training and testing

set.seed(1234) # A Random Sampling with replacement 

#Data Partition in to trainign and testing 

# Model <- sample(2, nrow(Capd), replace = T, prob = c(0.7, 0.3))
# train <- Capd[Model ==1,]
# test <- Capd[Model ==2,]

Building/Developing the models using Decision Tree

# Random Forest Model
# set.seed(333)
# as.data.frame(Capd) # we converted the data in to datafarme 
# 
# rf23 <-randomForest(satisfaction~., data = train, method = "class", na.action=na.exclude)

Training the Model

# # Prediction & Confusion Matrix - Test
# p <- predict(rf23, train)
# confusionMatrix(p, train$satisfaction)
Training the Model Training the Model

Evaluating the Model

# p2 <- predict(rf23, test)
# confusionMatrix(p2, test$satisfaction)
Evaluating the Model Test Classification 
# ConfusionTableR::binary_visualiseR(train_labels = train$satisfaction,
#                                    truth_labels= train$satisfaction,
#                                    class_label1 = "Not satisfied", 
#                                    class_label2 = "Satisfied",
#                                    quadrant_col1 = "#28ACB4", 
#                                    quadrant_col2 = "#4397D2", 
#                                    custom_title = "Confusion Metric on Airline P", 
#                                    text_col= "black")

Train-set Classification

# ConfusionTableR::binary_visualiseR(train_labels = test$satisfaction,
#                                    truth_labels= test$satisfaction,
#                                    class_label1 = "Not satisfied", 
#                                    class_label2 = "Satisfied",
#                                    quadrant_col1 = "#28ACB4", 
#                                    quadrant_col2 = "#4397D2", 
#                                    custom_title = "Confusion Metric on Airline P", 
#                                    text_col= "black")

Testing set the Model

Result

The demographical profiles of the airline passengers; 65899(51%) were females while 63981(49%) were males. Customer class, 62160 (48%) were business class, 58309 (45%) were Economic class and 9411(7.2%) were Economic plus class. The mean age of the airline passengers was 39.4 years with a standard deviation of 15. The average flight distance was 1190.2 miles The overall accuracy of the model was found to be 95% with a sensitivity of 97% and specificity of 93%

Conclusion

Most of the customers were unsatisfied with the airline service. Therefore based on these findings, the Random-Forest algorism predicated 57%  at 95%  accuracy with a sensitivity of 97% and specificity of 93% that the participants were not satisfied with the daily operation of the airline industry, especially in the areas involved in Air travelers purchasing ticket/booking online, values added services such, In-flight Wi-Fi service check-in in service, Baggage handling, in-flight entertainments, customer service quality, timely departure time, safety, customer service solutions, price, website ease of use.

Recommendation

Considering that 57% of participants reported not being satisfied with airline service rendered this is a significant proportion that may significantly decrease the daily, weekly or monthly income revenues generated. Therefore, this study recommends that the airline industry should endeavor to improve daily operation services, especially in the areas of travelers purchasing tickets/booking online, value-added services such, as In-flight Wi-Fi service, check-in service, Baggage handling, inflight entertainment, customer service quality, timely departure time, safety, customer service solutions, price, website ease of use. This will increase the volume of patronage and, as a result, boost their market share and hence profitability.