project to determine the relative importance of each parameter with regards to their contribution to passenger satisfaction using both analytical techniques and predictive algorithm.
In the aviation industry, high-grade customer satisfaction is a key factor to run the business, as the airline industry is very competitive and customer loyalty varies with small changes in the services. Therefore, companies need to understand the customers’ need to deliver unparalleled experiences to retain customers. Using the customer’s satisfaction dataset, we here to analysis the reasons for customer experience being satisfied or not. Based on that, improvements will be made to provide better service by the airline company. Also, as part of the analysis, we will be able to understand several factors which improve customer satisfaction level.
The main aim of this project is to determine the relative importance of each parameter with regards to their contribution to passenger satisfaction using both analytical techniques and predictive algorithm.
Specific objectives are:
- To analysis the dataset and understand which variable plays an important role in passengers satisfaction.
- And to predict their satisfaction on the basis of their feedback.
- And to check the accuracy of the prediction.
Our dataset of passengers feedback and rating was downloaded from Kaggle and imported into the program. The dataset contains over 1,29,880 entries. It contains passengers personal information such as ID, gender, purpose of travel and age. It also contains their details of their flight class (Economy, Business, Eco-Plus), type of customer(first time or regular customer) and flight distance. And it also contains passengers ratings and feedback on different categories and features of the flight(Wi-Fi, Leg room, Food, etc.).
From analysing the dataset we have inferred that
- Airlines should highly focus on inflight wi-fi experience.
- Ease of online booking is important for business customers.
- The airlines should provide better on-board service.
- Need to concentrate more on economy class passengers.
- The customers are satisfied with the food and services.
- The customers like the seat comfort.